Material handling system and method therefor - Patents.com

Abstract

A material handling system for processing and placing packages onto pallets for an order store, the material handling system including a storage array, an automated package transport system, an automated palletizer, and a controller operatively connected to the automated palletizer, the controller being programmed with a pallet load generator having at least one pallet and order store affinity characteristic for a predetermined method of pallet load package delivery at the order store, the pallet load generator being configured such that a pallet load is formed by the automated palletizer with packages placed on the pallet load embodying the at least one pallet and order store affinity characteristic.

Description

[Technical field]

[0001] [CROSS-REFERENCE TO RELATED APPLICATIONS] This application claims the benefit of, and is a non-provisional application of, U.S. Provisional Patent Application No. 63 / 288,253, filed December 10, 2021, the entire disclosure of which is incorporated herein by reference.

[0002] [Technical field] The present disclosure relates generally to material handling systems, and more specifically to using material handling systems to handle and place items on pallets. [Background technology]

[0003] A brief description of related developments A warehouse or distribution center for goods generates pallets of goods for various customers, where such customers include, but are not limited to, retail stores. Each of the various customers orders goods, and the orders are fulfilled by the warehouse or distribution center by loading the ordered goods onto one or more pallets. Each of the various customers may have its own preferred method of depalletizing the ordered goods from the warehouse or distribution center to facilitate replenishment of those goods on store shelves. Summary of the Invention

[0004] The foregoing aspects and other features of the present disclosure are explained in the following description taken in conjunction with the accompanying drawings. [Brief description of the drawings]

[0005] [Figure 1] FIG. 1 is an exemplary schematic diagram of a warehouse or distribution center incorporating aspects of the present disclosure. [Diagram 2] FIG. 1 is an exemplary schematic diagram of a palletized package delivery according to an aspect of the present disclosure. [Diagram 3] FIG. 1 is an exemplary schematic diagram of a palletized package delivery according to an aspect of the present disclosure. [Figure 4] FIG. 1 is an exemplary schematic diagram of a palletized package delivery according to an aspect of the present disclosure. [Diagram 5] FIG. 1 is an example schematic diagram of an order for pallet planning according to aspects of the present disclosure. [Figure 6] FIG. 13 is an example diagram of a pallet-aisle binary matrix according to an aspect of the present disclosure. [Figure 7] 1 is an exemplary method according to an aspect of the present disclosure. [Figure 8] FIG. 1 is an exemplary diagram of a planned order according to an aspect of the present disclosure. [Figure 9] FIG. 13 is an example diagram of a pallet-to-aisle selection process according to aspects of the present disclosure. [Figure 10] FIG. 1 is an exemplary diagram of a case unit delivery for a pallet load according to an aspect of the present disclosure. [Figure 11] FIG. 1 is an exemplary diagram of a case unit delivery for a pallet load according to an aspect of the present disclosure. [Figure 12A] FIG. 1 is an exemplary method according to an aspect of the present disclosure. [Figure 12B] FIG. 1 is an exemplary method according to an aspect of the present disclosure. [Figure 13] FIG. 1 is an exemplary method according to an aspect of the present disclosure. [Figure 14] FIG. 1 is an exemplary method according to an aspect of the present disclosure. [Figure 15] FIG. 1 is an exemplary method according to an aspect of the present disclosure. [Figure 16] FIG. 1 is an exemplary method according to an aspect of the present disclosure. [Figure 17] 1 is a graph illustrating the variation in case size within a population of representative cases. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0006] 1 illustrates an exemplary warehouse or distribution center 199 (generally referred to herein as warehouse 199) according to an embodiment of the present disclosure. Although embodiments of the present disclosure are described with reference to the drawings, it should be understood that they may be embodied in many forms. Additionally, any suitable size, shape, or type of elements or materials may be used.

[0007] Aspects of the present disclosure generally apply to warehouse systems in which pallet loads (such as those described herein, collectively referred to as pallet load(s) PALO) are built by automated machines such as robotized palletizers 162, 162' according to a pallet plan generated by a controller. However, aspects of the present disclosure may also be applied to manual pallet construction, in which a pallet load generator (such as those described herein) outputs a specification (according to the present disclosure) of case units CU to be included on the pallet, and a human worker builds the pallet with the pre-specified case units CU based on warehouse rules and previous work experience. Aspects of the present disclosure may also be applied to manual warehouses in which a pallet plan is computer generated according to the present disclosure and output in tangible form (e.g., video monitors, graphical user interfaces, smart devices such as phones and tablets, paper instructions, etc.) in the role of advisor for a human worker to follow to build the pallet as described herein. Here, the goods included in the pallet load PALO are delivered to the human worker by conveyors, mobile robots, or other suitable transport means in a pre-specified order inferred from the pallet plan.

[0008] According to the present disclosure, each pallet load PALO is planned using any suitable calculation method, including but not limited to the methods described in U.S. Patent No. 8,965,559, issued February 24, 2015, and U.S. Patent No. 9,969,572, issued May 15, 2018, the disclosures of which are incorporated herein by reference in their entirety. As used herein, a "planned pallet" or "planned pallet load" is a pallet load having a list of goods (individual items, boxes, totes, trays, etc. as described herein, generally referred to as case units CU) with coordinates (X, Y, Z (see FIG. 1)) assigned relative to the corner of the goods having coordinates close to the origin of the pallet coordinate system (X=0, Y=0, Z=0). The orientation of the goods along the X, Y, Z axes has, for example, length, width, height, or width, length, height values ​​for goods that cannot be tilted sideways. Additional values ​​may be provided for goods that can be placed on the surface on any side of the goods. These additional values ​​include, for example, length, height, width, or height, length, or height, length, width, or height, width, length. A pallet plan is a physically valid plan in which (1) the items do not cross in physical space, (2) each of the items is stably supported by other items or the pallet base, (3) no portion of any item is outside the predefined boundaries of the pallet's outer dimensions Lp, Wp, Hp (or a predefined volume Vp of the pallet load defined by the outer dimensions Lp, Wp, Hp), and (4) the total weight of the items on the pallet does not exceed a predefined maximum weight Wmax of the pallet load PALO.

[0009] Also according to the present disclosure, a "planned order" is a numbered list of planned pallets, such that every ordered case unit CU belongs to some pallet in the list, and there is no case unit CU that does not belong to any pallet load. It is noted that consecutive case unit CUs in an order list do not have to be assigned to the same or consecutive pallet loads. For example, case unit number 1 may be assigned to pallet load number 5, while case unit number 2 is assigned to pallet load number 3.

[0010] It is also noted that case units CU may have integer values ​​of "product group types" to which they belong within a retail store. For example, retail stores generally assign a predefined relationship between these product group types and the physical locations within the store (e.g., aisle, department, section, etc.) where the product group types are located. As used herein, product group types and corresponding physical locations within a retail store are generally referred to as "aisles." It is noted that aisles are aisles within a retail store and are not to be confused with the storage / picking aisles (of a distribution center) of the storage array 130 of the material handling system 190 (of a distribution center). Here, the aisles of a retail store and the picking aisles (of the storage array 130) of a distribution center are completely separate from each other. It is also noted that the aisles of a retail store are referred to by a numerical designation ranging from 1 to n (e.g., aisle 1, aisle 2, ... aisle n), where n is an integer value indicating the predefined highest aisle number for a given store. Although the aisles may be numbered, the locations of the aisles need not be consecutive. According to aspects of the present disclosure, case units CU that belong to a common (e.g., same) aisle (e.g., physical location / aisle and / or product group type) are assigned to a common pallet (unless otherwise noted) for purposes of the palletized package delivery methods described herein.

[0011] In one aspect, aisles that are close in number within a retail store (e.g., aisle 34 and aisle 35, etc.) may be spatially and physically close to one another. In this aspect, the present disclosure may optimize products placed on a given pallet by combining products from physically close aisles (e.g., aisle 34 and aisle 35, etc.) onto a common pallet, rather than combining products from aisles that are physically separate from one another (e.g., aisle 34 and aisle 73, etc.).

[0012] In other aspects, the relationship between aisle numbers and spatial proximity of the aisles may be more complex than adjacent aisle numbers (e.g., aisle 34 and aisle 35) being physically adjacent in space. For example, adjacent or close aisle numbers (e.g., aisle 20 and aisle 21) may not mean that the aisles are physically close to each other in space (e.g., aisle 20 may be located at one end of the retail store while aisle 21 may be located at the opposite end of the retail store). Here, a pairwise relationship between two aisles may be provided with respect to the allocation of case units to pallet loads as described herein. For example, according to aspects of the present disclosure, the pairwise relationship between two aisles is in the form of a coefficient A[i,k] for aisle i and aisle k. This pairwise relationship not only specifies the physical proximity between the two aisles, but also specifies the retailer's preference to store products from these aisles on one pallet or on separate pallets based on the retailer's business logic other than, for example, distance-based unloading optimization. An example of such business logic may be the separation of caustic products (eg, laundry detergent) and food products (eg, baby food), preferably transported on separate pallets.

[0013] Aspects of the present disclosure are applicable to any suitable volume of product in any given aisle. For example, some aisles may have a total case unit volume that is much larger than the volume of a single pallet (see volume V2 of aisle 2 in FIG. 5). Here, aspects of the present disclosure allocate case unit volumes to the entire pallet load until the remaining case unit volume does not fill the entire pallet load. Here, the remaining case unit volume is allocated to pallets in accordance with the package delivery methods described herein. As another example, the case unit volume for other aisles may be a few case units or even a single case unit, in which case these case units are allocated to pallet loads in accordance with the package delivery methods described herein.

[0014] 2-4, as described herein, a material handling system 190 of a warehouse 199 is configured to provide for optimization of an automated process of planning and building mixed product orders 299 (e.g., see FIG. 2) to be delivered, for example, to a retail store (or other suitable customer whose goods are delivered on pallets). The retail store that places the order is referred to herein as an order store 200 (e.g., see FIG. 2-4). Each of the one or more pallet loads PALOs in the mixed product order 299 is built by the material handling system 190 such that each pallet load PALO is a "store-friendly pallet" or "store-friendly pallet load." Here, "store-friendly" means that the pallet load PALO is configured for easy and efficient unloading and delivery to the store shelves. For purposes of explanation only, "store friendly" refers to the store affinity of a pallet load or pallet load store affinity, whereby a pallet load configuration (i.e., pallet load build) includes predetermined characteristics (or factors) of store affinity that bias or influence the determination of each pallet load PALO to match and provide each resulting pallet load PALO with characteristics of a retail store that conform or are aligned with the predetermined characteristics of the retail store as described herein. For example, when a pallet load PALO(s) of a fulfilled mixed product order 299 (see FIGS. 2-4) arrives at the order store 200, the pallet load PALO(s) (e.g., pallet load PALOC of FIG. 2, PALOA, PALOA' of FIG. 3, and PALOC, PALOC' of FIG. 4) is quickly unloaded (e.g., by following "just in time" inventory management practices) and the goods are delivered (e.g., restocked / stocked) to the store shelves 233 with minimal disruption to store operations.To facilitate rapid unloading and delivery of goods to store shelves 233, the material handling system 190 is configured to structure the pallet load(s) PALO such that the structure of goods CUs (also referred to herein as packages, products, case units, mixed cases, cases, shipping cases, and shipping units) on the pallet load(s) PALO are grouped in a manner similar to the way the goods CUs are delivered to the store shelves 233.

[0015] Each warehouse customer (e.g., order store 200) of the warehouse 199 may have its own priority with respect to handling of pallet loads within the order store 200. Aspects of the present disclosure provide for the construction of store-friendly pallets that accommodate the various ways in which pallet loads are handled and products are distributed by warehouse customers.

[0016] 2, one exemplary method for handling palletized loads PALO may be referred to as a "clustered aisle palletized load package delivery method" and includes dismantling / unloading the palletized load(s) PALOC at the loading dock area 222 (or other suitable area) of the order store and placing the goods CU belonging to different sections of the order store 200 onto two or more separate secondary pallets PAL21-PAL23 (three secondary pallets are shown in FIG. 2 for illustrative purposes). These secondary pallets PAL21-PAL23 contain goods CUs assigned to a given shopping aisle and are moved to each given shopping aisle for unloading (see FIG. 2). When each shopping aisle has a secondary pallet PAL21-PAL23, the goods CUs from the secondary pallets PAL21-PAL23 are delivered to the assigned shelves 233.

[0017] With reference to FIG. 3, another example of handling pallet loads PALO may be referred to as an "adjacent aisle pallet load package delivery method" and includes moving the entire pallet load PALOA, PALOA' to the shopping aisle (e.g., without loading or unloading the pallet). When the pallet load PALOA, PALOA' is in the shopping aisle, the goods CU are delivered substantially directly from the pallet load PALOA, PALOA' to the assigned shelf 233 (see FIG. 3). Here, the goods are placed on the pallet load(s) PALOA, PALOA' in a manner that minimizes the travel distance of each pallet load PALOA, PALOA' in the store and substantially avoids the pallet PALOA, PALOA' returning to an aisle previously visited by the corresponding pallet (e.g., the pallet passes through the aisle only once along a predetermined path 301, 302). The goods CU may be placed on the pallet load PALO, PALOA' according to the path 300, 302 of movement of the respective pallet load PALOA, PALOA' through the shopping aisle.

[0018] With reference to FIG. 4, yet another example of pallet PALO may be referred to as a "mixed mode clustering and adjacent aisle pallet load package delivery method" and includes a combination of the above handling methods. With reference to FIG. 4, pallet loads PALOC, PALOC' arrive at the order store 200 by truck (or other suitable transportation means) from the warehouse / distribution center 199. The pallet loads PALOC, PALOC' are moved (without unloading the pallet) to a shopping area generally proximate to the shelf to which the product CU on the pallet load PALOC, PALOC' is assigned. With the pallet loads PALOC, PALOC' generally located near the assigned shelf, the pallet loads PALOC, PALOC' are unloaded onto the respective secondary pallets PALO21, PALO22, PALO23, PALO21', PALO22' assigned to the respective shopping aisle. Here, the pallet loads PALOC, PALOC' are constructed such that each pallet load PALOC, PALOC' includes goods belonging / assigned to store aisles that are adjacent to one another (e.g., pallet load PALOC includes goods located in aisle 1, aisle 2 (adjacent to aisle 1), and aisle 4, which is one aisle away from aisle 2; similarly, pallet load PALOC' includes goods belonging / assigned to adjacent aisles 12 and 13). The goods CU may also be arranged within each pallet load PALOC, PALOC' such that the pallet structure corresponds to the way goods are loaded / unloaded onto each secondary pallet (e.g., sequential loading / unloading, where goods assigned to secondary pallet PALO21 are at the top of the pallet structure of the pallet load PALOC, goods assigned to secondary pallet PALO22 are in the middle of the pallet structure of the pallet load PALOC, goods assigned to secondary pallet PALO23 are at the bottom of the pallet structure of the pallet load PALOC, etc.). In this embodiment, the aisles to which the products CU are assigned do not have to be located along respective specific routes (e.g., see routes 301, 302 in FIG. 3) for unloading the products CU of each pallet load PALO, PALO' onto store shelves.

[0019] The above examples of pallet handling / unloading methods at the order store 200 are merely illustrative. It is again noted that the pallet load PALOC, PALOC', PALOA, PALOA' for each of the pallet handling / unloading methods are generally referred to herein as the pallet load PALO. It is also noted that the pallet load PALO(s) are constructed in any suitable manner by the material handling system 190 such that the goods on the pallet load PALO are arranged according to any suitable at least one order pallet and order store affinity characteristic 166, 166' in the pallet load package delivery method described herein. It is noted that the pallet load determination according to the affinity of the order store (as described herein) is decoupled from the arrangement of the storage array 130 and the throughput of the material handling system 190 of case CUs to the palletizer 162. Here, the output of case CUs from the storage array 130 by the material handling system 190 is selected to follow or otherwise depend (based on) the pallet load determination according to the affinity of the order store. In one or more embodiments, the throughput of case CUs output by the material handling system 190 may be effected in a manner similar to that described in U.S. Patent Application No. 17 / 091,265, filed November 6, 2020, entitled "Pallet Building System with Flexible Sequencing," the entire disclosure of which is incorporated herein by reference. According to embodiments of the present disclosure, the placement of case CUs within the storage array 130 may be freely optimized for optimal throughput, separate from the determination and building of pallet loads PALOs according to order store affinities. An example of throughput optimization can be found in U.S. Patent No. 9,733,638, issued August 15, 2017, entitled "Automated Storage and Retrieval System and Control System Thereof," the entire disclosure of which is incorporated herein by reference.

[0020] With reference to FIG. 1 , the material handling system 190 may be located in a retail distribution center or warehouse 199 to fulfill orders received from a retailer (e.g., an order store 200 (see FIGS. 2-4 )) for replenishment items to be shipped in, for example, cases, packages, and / or parcels. The terms case, package, and parcel are used interchangeably herein and, as previously described, may be any container that may be used for shipping and that may be filled with one or more product units by a manufacturer. Case(s) as used herein refers to a case, package, or parcel unit that is not stored (e.g., not contained) in a tray, on a tote, etc. It is noted that a case unit CU may include a case of items / units (e.g., a case of soup cans, boxes of cereal, etc.) or an individual item / unit that is adapted to be removed from or placed on a pallet. According to the present disclosure, case units (e.g., cartons, barrels, boxes, crates, jugs, shrink-wrapped trays or groups, or any other suitable device for holding goods) may have variable sizes, may be used to hold goods during shipment, and may be configured to be palletizable for shipment. Case units CU may also include totes, boxes, and / or containers of one or more individual goods (generally referred to as break-pack goods) that have been unpacked / released from their original packaging and placed in totes, boxes, and / or containers (collectively referred to as totes) with one or more other individual goods of a mixed or common type at the order filling station. For example, it is noted that when incoming bundles or palletized PALNs (e.g., from a case unit manufacturer or supplier) arrive at material handling system 190 for replenishing goods stored in storage array 130 of material handling system 190, the contents of each palletized PALN may be uniform (e.g., each pallet holds a predetermined number of the same items, i.e., one pallet holds soup and another pallet holds cereal).As can be appreciated, the cases of such palletized PALNs may be substantially similar, or in other words, homogenous cases (e.g., similar dimensions) and may have the same SKU (or, as previously discussed, the pallet may be a "rainbow" pallet having layers formed of homogenous cases).

[0021] As the palletized load PALN exits material handling system 190 with cases or totes fulfilling a store's replenishment orders, the palletized load PALN may contain any suitable number and combination of different case units (e.g., each pallet may hold different types of case units, a pallet may hold a combination of canned soup, cereal, drink cartons, cosmetics, and household cleaners). The cases combined on a single pallet may have different dimensions and / or different SKUs.

[0022] The material handling system 190 generally includes a storage array 130 and an automated package transport system 195. The storage array 130 includes storage spaces 130S for holding case units CU therein. The automated transport system 195 is fluidly connected to the storage array 130 for storing the case units CU within the storage spaces 130S of the storage array 130 and retrieving the case units CU from the storage spaces 130S of the storage array 130.

[0023] The automated palletizer 162, 162' includes an automated package picking device 162D (e.g., a robotic arm, a gantry picker, etc.) that can move case units CU from a package stacking section (e.g., an outfeed transfer station 160) to a pallet (also referred to herein as a pallet base) to form a pallet load PALO from the case units CU, where the pallet load PALO includes one or more composite layers L1-Ln of case units CU. As described herein, the one or more composite layers L1-Ln of case units CU are formed of case units CU arranged in a pallet load PALO that embodies at least one pallet and order store affinity characteristic 166, 166' for a given method of pallet load package delivery at the order store 200 (see FIGS. 2-4). The automated palletizer 162, 162' is communicatively connected to an automated package transport system 195. The automated package transport system 195 provides individual case units CU from the storage array 130 to the automated palletizer 162 to form a palletized load PALO, where the palletized load PALO includes more than one composite layer L1-Ln of case units CU. The individual case units CU from the storage array 130 from which the palletized load PALO is constructed have case dimensions (e.g., any one or more of case length, case width, and case height) where the case dimension(s) have a substantially Gaussian distribution or a substantially probabilistic distribution as represented by a normal probability curve as illustrated in FIG. 17. FIG. 17 is a graph illustrating the variation of case dimensions (e.g., length, height, and width) within a representative population of case CUs, such as those found in the material handling system 190 and used to generate a mixed case palletized load PALO pursuant to a customer replenishment order (as described herein). As can be appreciated, as a result of the order, the mixed case palletized load PALO may include many cases having dimensions from different portions of the dimension spectrum illustrated in FIG. 17.

[0024] The controller 164, 164' is operatively connected to the automated palletizer 164. The controller 164, 164' is programmed with a non-transitory computer program code that defines a pallet load generator 165, 165' with at least one pallet and order store affinity characteristic 166, 166' (as described herein) for a given method of delivery of the pallet load PALO case units CU at the order store 200. As described herein, the pallet load generator 166, 166' is configured such that the pallet load PALO is formed by the automated palletizer 162 with case units CU placed on the pallet load PALO embodying at least one pallet and order store affinity characteristic 166, 166'.

[0025] Now in more detail, and still referring to Figure 1, the material handling system 190 may be configured to be installed in, for example, an existing warehouse structure or may be adapted to a new warehouse structure. As previously mentioned, the material handling system 190 shown in Figure 1 is representative and may include, for example, infeed and outfeed conveyors (e.g., transferring case units to / from respective depalletizers 162' and palletizers 162) terminating at respective infeed and outfeed transfer stations 170, 160, lift module(s) 150A, 150B, a storage array 130 (e.g., including suitable structures such as racks, vehicle loading surfaces, storage shelves, etc.), and a number of autonomous guided vehicles 110 (also referred to herein as "bots").

[0026] It is noted that the material handling system 190 is formed by at least the storage array 130 and the bot 110. In some embodiments, the lift modules 150A, 150B also form part of the material handling system 190, but may form a vertical sequencer in addition to the material handling system, as described in U.S. Patent Application No. 17 / 091,265, filed November 6, 2020, entitled "Pallet Building System with Flexible Sequencing," the entire disclosure of which is incorporated herein by reference. In alternative embodiments, the material handling system 190 may include a robot or bot transfer station 140 that may provide an interface between the bot 110 and the lift module(s) 150A, 150B.

[0027] The storage array 130 includes any suitable structure forming a plurality of (stacked) storage levels 130L1-Ln (see FIG. 1, generally referred to as (one or more) storage levels 130L, where n is an integer indicating the upper number of storage levels present in the material handling system 190) of storage rack modules, where each level 130L includes a respective picking aisle 130A, storage space 130S, and transfer deck 130B for transferring case units between any of the storage spaces 130S of the storage structure 130 and the shelves of the (one or more) lift modules 150A, 150B. The storage spaces 130S are arranged along (or alongside) one or more sides of each picking aisle 130A such that a bot 110 moving along the picking aisle 130A has access to the storage spaces 130S on either side of the picking aisle 130A.

[0028] The picking aisle 130A, in one embodiment, is configured to provide guided movement of the bot 110 (e.g., movement along a vehicle riding surface VRSR that includes a bot guidance feature such as a rail), while in other embodiments, the picking aisle is configured to provide unrestricted movement of the bot 110 (e.g., along a vehicle riding surface VRSR that is open and non-deterministic with respect to the guidance / movement of the bot 110). The transfer deck 130B has an open, non-deterministic bot support movement surface VRS along which the bot 110 moves under guidance and control provided by bot steering (e.g., such steering is provided by one or more of differential drive wheel steering, steerable wheels, etc.). In one or more embodiments, the transfer deck 130B has multiple lanes between which the bot 110 transitions freely to access the picking aisle 130A and / or the lift modules 150A, 150B. The picking aisle 130A and the transfer deck 130B also enable the bots 110 to place case units CU in the picking stock and to retrieve ordered case units CU. In an alternative embodiment, each storage level 130L may include a respective bot transfer station 140 that provides a case unit transfer interface between the bots 110 and the lift module(s) 150A, 150B.

[0029] The bot 110 may be configured to place case unit CUs, such as the retail items described above, into picking stock at one or more levels 130L of the storage array 130, and then selectively remove the ordered case unit CUs and ship the ordered case unit CUs, for example, to an order store 200 (see, for example, Figures 2-4) or other suitable location.

[0030] The infeed transfer station 170 and the outfeed transfer station 160 may operate in conjunction with respective lift module(s) 150A, 150B to transfer case units CU bidirectionally to / from one or more levels 130L of the storage structure 130. Although the lift modules 150A, 150B may be described as dedicated inbound lift modules 150A and outbound lift modules 150B, it is noted that in alternative embodiments, each of the lift modules 150A, 150B may be used for both inbound and outbound transfer of case units from the material handling system 190. Similarly, although the palletizers 162, 162' may be described as dedicated (inbound) depalletizers 162' and (outbound) palletizers 162, in alternative embodiments, each of the palletizers 162, 162' may be used for both inbound and outbound transfer of case units from the material handling system 190.

[0031] As can be appreciated, the material handling system 190 may include multiple in-feed and out-feed lift modules 150A, 150B that are accessible, for example, by the bot 110 of the material handling system 190, such that uncontained case unit(s) (e.g., case unit(s) not held in a tray) or contained case unit(s) (in a tray or tote) may be transferred from the lift modules 150A, 150B to each storage space 130S on the respective level 130L, and from each storage space to any one of the lift modules 150A, 150B on the respective level 130L. The bot 110 may be configured to transfer case units between the storage space 130S (e.g., located in the picking aisle 130A or other suitable storage space / case unit buffer located along the transfer deck 130B) and the lift modules 150A, 150B. Generally, the lift modules 150A, 150B include at least one movable payload support that can move the case unit(s) between the infeed and outfeed transfer stations 160, 170 and the respective level 130L of the storage space 130S where the case unit(s) CU are stored and retrieved. The lift module(s) can have any suitable configuration, such as, for example, a reciprocating lift, or any other suitable configuration. The lift module(s) 150A, 150B can include any suitable controller (such as the controller 120, or other suitable controller coupled to the controller 120, a warehouse management system 2500, and / or a palletizer controller 164, 164') to form a sequencer or sorter in a manner similar to that described in U.S. Patent Application No. 16 / 444,592, filed June 18, 2019, and entitled "Vertical Sequencer for Product Order Fulfillment," the disclosure of which is incorporated herein by reference in its entirety.

[0032] The material handling system 190 may include a control system with, for example, one or more control servers 120 communicatively connected to the in-feed and out-feed conveyors and transfer stations 170, 160, lift modules 150A, 150B, and bots 110 via a suitable communication and control network 180. The communication and control network 180 may have any suitable architecture, which may incorporate various programmable logic controllers (PLCs), such as for commanding the operation of the automation of the in-feed and out-feed conveyors and transfer stations 170, 160, lift modules 150A, 150B, and other suitable systems. The control server 120 may include high level programming implementing a case management system (CMS) that manages case flow through the material handling system 190.

[0033] Network 180 may further include appropriate communications to provide a two-way interface with bot 110. For example, bot 110 may include an on-board processor / controller 1220. Network 180 may include an appropriate two-way communications suite to enable bot controller 1220 to request or receive commands from control server 120 to effect a desired transport of a case unit CU (e.g., placement in or removal from a storage location), and to transmit desired bot 110 information and data to control server 120, including bot 110 ephemeris, status, and other desired data.

[0034] 1, the control server 120 may be further connected to a warehouse management system 2500, for example, to provide inventory control and customer order fulfillment information to the CMS 120 level programs. A suitable example of a material handling system arranged to hold and store case units is described in U.S. Patent No. 9,096,375, issued August 4, 2015, the entire disclosure of which is incorporated herein by reference.

[0035] Constructing a pallet load PALO according to the affinity characteristic of at least one pallet and an order store will be described in more detail with respect to an embodiment of the present disclosure with reference to Figures 1-5. As described above, the affinity characteristic of at least one pallet and an order store 166, 166' is for at least one of the clustered aisle pallet load package delivery method (e.g., see Figure 2), the adjacent aisle pallet load package delivery method (e.g., see Figure 3), and the mixed mode clustered and adjacent aisle pallet load package delivery method (e.g., see Figure 4). The affinity characteristic of at least one pallet and an order store 166, 166' may be stored in any suitable memory, such as the memory of the control server 120 and / or the palletizer 162, 162' as described herein, and may be utilized by the control server 120 and / or the palletizer 162, 162' to generate the pallet load PALO as described herein. The term "aisle" as used hereinafter refers to the aisle of the order store that is specified for the pallet load PALO, unless otherwise specified.

[0036] 1 and 5, an exemplary graph for a sample order for pallet planning is illustrated (see FIG. 5), which includes case units from one or more aisles of an order store 200 (FIGS. 2-4). In the exemplary sample order shown in FIG. 5, the aisles are numbered 1-12. The total volumes V1-V12 of case units CU ordered for each respective aisle 1-12 are represented by the height of the respective bars in the graph (each bar corresponds to a respective aisle 1-12). The volumes V1-V12 are illustrated as subunits for a predicted total volume Vp of case units CU on a full pallet load (e.g., a full pallet load has maximum pallet load dimensions with length Lp, width Wp, and height Hp). The volume Vp is the product of the maximum dimensions Lp (length), Wp (width), and Hp (height) (e.g., of the space allocated to case units CU on a pallet load) multiplied by the predicted volumetric efficiency E of the packaging of the products on the pallet load.

[0037]

number

[0038] Typically, the dimensions (e.g., length, width, height) of the goods / case units CU are known, where the case units CU have an approximately rectangular parallelepiped shape. Here, the known dimensions of the case units are provided for the determination of the total volume Vp of the case units CU (e.g., the combined volume of the case units CU assigned to any one given pallet load). As an example, and depending on the calculation method for planning the individual pallet loads, the average total volume of the goods on a pallet is statistically about 0.8 with a standard deviation of 0.03 of the volume of the outer boundary of a pallet load with dimensions Lp (length) x Wp (width) x Hp (height) (e.g., about 80% of the pallet volume is occupied by goods, the rest is empty space between the goods). The predicted efficiency E depends on the packing algorithm of the calculation method (such as that described herein), which will generally exceed a value of about 0.8 for state-of-the-art packing algorithms (such as those of the calculation method described herein) and for mixed products including boxes of various dimensions.

[0039] 5 , it can be seen that some of the aisles 1-12 (see, e.g., aisle 2) may have a total volume (such as volume V2 of aisle 2) that exceeds the predicted (e.g., maximum) volume Vp of one pallet load PALO. Other aisles 1-12 may have respective volumes (see volume V9 of aisle 9) that are smaller or less compared to the predicted total volume Vp. As described herein, according to aspects of the disclosure, a pallet load generator 165, 165′ (e.g., of the control server 120 and / or palletizer 162, 162′) may generate the following for a pallet load PALO: Maximized with respect to at least one of maximum pallet load volume Vp and maximum pallet load Wmax; having the maximum number of packages from the minimum number of store aisles; Generated with a minimum number of pallet loads for each store order; For each pallet load intended for an order store 200, the case units CU forming the pallet load are generated to represent a minimum number of order store aisles; and For each pallet load intended for an order store 200, a determined pallet load is generated to represent a minimum number of order store aisles; The method is configured to determine a pallet load PALO according to affinity characteristics 166, 166' of at least one pallet and an order store, so that one or more of the above is performed.

[0040] With reference to Figures 1, 2, 5, 6, 7, 8, and 12, the pallet and order store affinity properties 166, 166' for the clustered aisle pallet load package delivery method are described in more detail. The clustered aisle pallet load package delivery method minimizes both (1) the number of pallets created from a given set of products and (2) the average pallet per aisle ratio RPA. The pallet per aisle ratio RPA is calculated by dividing the total number of instances of products from each aisle on each pallet by the total number of aisles. The pallet per aisle ratio RPA can be understood as the number of times a pallet load PALO is present in any aisle or the number of aisles a pallet load PALO is present in for unloading. This number is sought to be minimized (e.g., close to 1).

[0041] The pallet-aisle ratio RPA may be represented by a pallet-aisle binary matrix PA as illustrated in FIG. 6. Here, the pallet-aisle binary matrix PA has a number of rows equal to the number of aisles planned for a given order (eight aisles are illustrated for illustrative purposes) and a number of columns equal to the number of pallets (four pallets are illustrated for illustrative purposes). If a product from aisle (i) is present on pallet (j), the element of the pallet-aisle binary matrix PA[i,j] at row (i) and column (j) is equal to 1, otherwise, the element of the pallet-aisle binary matrix PA[i,j] at row (i) and column (j) is equal to 0. The pallet-aisle ratio RPA is calculated by dividing the sum of all elements of the pallet-aisle binary matrix PA by the number of aisles in which the product is present in the order. It is noted that if all the aisles are present on only one pallet, the pallet-aisle ratio RPA is equal to 1. The more products from some aisles are distributed across some pallets, the higher the pallet-aisle ratio RPA. If product from every aisle is present on every pallet, then the pallets per aisle ratio RPA is equal to the number of pallets. In the example illustrated in Figure 6, the pallets per aisle ratio RPA is equal to 11 / 8 or 1.375. Here, the pallets per aisle ratio RPA is greater than 1 because product from aisle 1 is present on pallet 1 and pallet 3, product from aisle 6 is present on pallet 2 and pallet 4, and product from aisle 8 is present on pallet 1 and pallet 4.

[0042] In the clustered aisle pallet load package delivery method, all single aisle pallets are planned for aisles with a volume of case units CU that exceeds the predicted pallet volume Vp or maximum pallet weight Wmax as described in more detail herein. The remaining pallets to fulfill the store order are planned from the aisle combinations, where such planning utilizes an iterative dual-loop determination such as that illustrated in FIG. 12A, where for each aisle combination iteration IAi (e.g., nested within a wider pallet building iteration Pj), the pallets are planned such that the volume Vc(IAi) is maximized (e.g., minimizes the number of pallets in the order) relative to the predicted pallet volume Vp and the ratio of pallets per aisle RPA is minimized (e.g., approaches 1). Here, the iterative dual-loop determination is iterated through the aisle combinations until the planned pallet is successfully planned (as described in more detail below), and the entire store order is processed (e.g., the order is filled) and pallets are iterated through until there are no case units CU that are not planned (i.e., not assigned to a pallet load) in the store order. Here, the order store affinity characteristics 166, 166' are informed by an iterative dual loop determination, where at least one loop of the iterative dual loop determination associates order store aisles with each other, and at least one other loop of the iterative dual loop determination determines available combinations of order store aisles that determine placement of case units or package CUs in a given pallet load PALO. The iterative dual loop determination is illustrated, for example, in Figures 7 and 12B and described below with respect to building a pallet load according to the order store affinity characteristics for a clustered aisle pallet load package delivery method.

[0043] The warehouse management server 2500 or the control system 120 (or any other suitable controller of the warehouse 199) receives a store order (FIG. 12B, block 1200). When the warehouse management server 2500 receives the store order, the store order is communicated to the control server 120 via the network 180 or any other suitable manner. The control server 120 commands the automated package transport system 195 to remove the ordered goods from the storage array 130 and transport them to the palletizer 162. For example, the bots 110 on one or more predefined storage levels 130L1-130Ln are instructed by the control server 120 to remove the ordered case units CU from predefined storage spaces 130S of the respective storage levels 130L1-130Ln. The bots transport the removed case units CU from the storage spaces 130S to the lift(s) 150B so that the removed case units CU are output to the palletizer via the outfeed transfer station 160 in the predefined order. Here, a given order of output of a case unit CU is determined at least in part by the affinity characteristics 166, 166' of the order store.

[0044] The control server 120 and one or more of the palletizers 162 are configured to determine the affinity characteristics 166, 166' of the pallet and the order store (FIG. 12B, block 1210), for example, based on the order store 200 that originates the order. For example, the palletizer controllers 164, 164' of one or more of the control server 120 and the palletizers 162 are configured with pallet load generators 165, 165'. For example, in one aspect, each respective order store 200 may inform the pallet load generator 165, 165 of the affinity characteristics 166, 166' of the respective pallet and the order store prior to the origination of the order (such as when the order store opens an account with the warehouse 199 or at any other suitable time and when the affinity characteristics 166, 166' of the pallet and the order store are communicated or entered into the warehouse management system). Here, the pallet load generator 166, 166' may include any suitable table associating each order store 200 with a respective pallet-to-order store affinity characteristic 166, 166'. In other aspects, the pallet-to-order store affinity characteristic 166, 166' may be communicated to the pallet load generator 165, 165' contemporaneously with the issuance of the order (e.g., as input at the time of order submission, where the pallet load generator determines the pallet-to-order store affinity characteristic 166, 166' substantially directly from the order without regard to the identity of the order store 200). As noted above, in this example, the pallet-to-order store affinity characteristic 166, 166' is for a clustered aisle pallet load package delivery method.

[0045] The pallet load generator 165, 165' determines any aisles having a total case unit volume Vcomb greater than the predicted volume Vp of the pallet load PALO (FIG. 7, block 700A) (in the example illustrated in FIG. 5, aisle 2 has a volume V2 greater than the predicted volume Vp). Alternatively, the pallet load generator 165, 165' determines any aisles having a total case unit weight Wcomb greater than the predicted weight Wmax (e.g., maximum weight) of the pallet load PALO. Based on the existence of any aisles having a total case unit volume Vcomb greater than the predicted volume Vp or a total case unit weight Wcomb greater than the predicted weight Wmax (collectively referred to herein as "aisles-in-excess"), the pallet load generator 165, 165' plans a pallet load PALO ordered for the excess aisle and formed only with case units belonging to the excess aisle (FIG. 7, block 710). In some embodiments, some case units CU will remain from the over-aisle (FIG. 7, block 720) and these remaining case units are included in a subsequent pallet load. For example, pallet load generator 165, 165' forms pallet load 1 (see FIG. 8) with portion V2A of case unit volume V2 of FIG. 5, while the remaining portion V2B of case unit volume V2 of FIG. 5 is included in pallet load 5, as described below.

[0046] Subsequent pallet loads (or pallet loads in the absence of excess aisles) are scheduled from one store aisle or a combination of multiple store aisles. As described herein, the aisle combinations are computationally created by the pallet load generator 165, 165' to minimize the ratio of pallets per aisle and maximize the case unit volume of each pallet load PALO. Here, each of the available aisle combinations of the order store aisles is determined based on maximizing the pallet load, or in other aspects as described herein, based on a combination of maximizing the pallet load and the contiguity or adjacency of the aisles in the available combinations, where maximizing the pallet load is weighted more heavily than the contiguity or adjacency of the aisles.

[0047] Each of the subsequent pallet loads has a total volume in case units Vcomb less than the predicted product volume Vp of the pallet load PALO and a total weight in case units Wcomb less than the predicted weight Wmax of the pallet load PALO. Each of the aisle combinations may have a different number of aisles ranging from one aisle to the total number of aisles remaining in the order. By utilizing a binary representation of the integer iterator, a list of allowed aisle combinations ALC (see FIG. 1) may be determined (FIG. 7, block 730), where the integer iterator ranges from 1 to 2 Na It has a value k ranging from -1 to -1, where Na is the number of paths remaining in the order. Each increment of this integer iterator corresponds to a potential path combination as follows: if the mth least significant bit of the binary representation of the integer iterator is 1, then path m from the list of remaining paths is present in the combination, and if the least significant bit is 0, then path m is not present in the combination.

[0048] As an example of the use of integer iterators, consider a store order that has 5 aisles (there may be more or less than 5 aisles) and an integer iterator equal to 12, i.e., the 12th iteration (note that 11 of the 31 possible iterations occur before the 12th iteration (where, in this example, the integer iterator is k=2 Na -1=2 5 (The number of paths can range from 1 to 31 as determined by the iterator / iteration of -1=31, there can be subsequent iterations after the 12th iteration if the paths remain in order, etc.). The binary representation of the number 12 (i.e., an integer iterator) is 01100. The numbers of paths, ordered from largest to smallest, can be placed on a grid for the binary representation of the integer iterator as follows (such that the numbers of paths line up with the corresponding numbers in the binary representation of the integer iterator):

[0049] [Table 1]

[0050] As mentioned above, a case unit CU from a passage is present in a passage combination if the bit of the integer iterator corresponding to that passage is 1. In the example provided above, the bits of the integer iterator corresponding to passage 4 and passage 3 are 1, which means that case units CU from passage 4 and passage 3 are included in the 12th iteration combination of the passage, while passages 5, 2, and 1 are excluded from the 12th iteration combination of the passage.

[0051] For each value k of the integer iterator, the total volume Vcomb and weight Wcomb of the case units CU in the corresponding aisle (e.g., each aisle combination for a given value k of the integer iterator) are determined by the pallet load generator 165, 165' and compared to the predicted pallet volume Vp and maximum pallet weight Wmax. If any of the values ​​of Vcomb and Wcomb exceed the values ​​of Vp and Wmax, respectively, the aisle combinations having at least one of the values ​​of Vcomb and Wcomb exceeding the values ​​of Vp and Wmax are discarded. If both the values ​​of Vcomb and Wcomb are less than the values ​​of Vp and Wmax, respectively, the aisle combinations having both the values ​​of Vcomb and Wcomb less than the values ​​of Vp and Wmax are added to a list ALC of allowed aisle combinations. Referring to the example above, in order to be included in the list of allowed aisle combinations ALC, the combined volume V3 and volume V4 of aisle 3 and aisle 4, respectively, must be less than or equal to the predicted pallet volume Vp, and the combined weight W3 and weight W4 of aisle 3 and aisle 4, respectively, must be less than or equal to the maximum pallet weight Wmax.

[0052] The list of allowed aisle combinations ALC may be sorted in any suitable manner, such as in descending order of the total (case unit) volume Vcomb of each of the aisle combinations. By sorting the list of allowed aisle combinations in descending order of the total case volume Vcomb, a minimum number of pallets for a given store order may be constructed. Here, the list of allowed aisle combinations ALC serves as a list of candidate combinations of products selected for planning pallet loads PALO in the output pallet list for a given store order.

[0053] An exemplary sorted list of allowed path combinations of 10 paths ALC may be represented as follows:

[0054] [Table 2]

[0055] Here, the right-most column represents the ratio of the total case unit volume in each aisle in the aisle combination (eg, combined volume Vcomb) to the predicted pallet volume Vp.

[0056] It is noted that when a store order includes a large number of aisles, each aisle may be subdivided into any suitable number of aisle subdivisions, with the size of the aisle subdivision depending on the computational resources of the pallet load generator 165, 165'. The size of the aisle subdivision may also affect the minimum number of pallets generated / output by the warehouse 199 for a given store order. Aisle subdivisions may be grouped with other aisle subdivisions to form store sections, where each aisle subdivision is treated as an aisle and a list of aisle combinations ALC is determined for each store section in the manner described above.

[0057] As described above, the pallet-to-order store affinity properties for the clustered aisle pallet load package delivery method are characterized by an iterative dual-loop DRL determination, where at least one loop of the iterative dual-loop DRL determines available combinations of order store aisles that determine the placement of packages in the pallet load, and another loop associates the order store aisles with each other. In the iterative dual-loop DRL, the pallet load is planned by utilizing a list ALC of aisle combinations.

[0058] In one loop of the iterative dual loop DRL, the pallet load generator 165, 165' determines the available aisle combinations that determine the package placement in the pallet load (FIG. 12B, block 1230). The entry from the list of aisle combinations ALC with the highest Vcomb / Vp ratio (aisle combination 1 in the above example) is selected (FIG. 7, block 735) and the pallet load PALO is planned with the case units CU corresponding to the aisles in the selected aisle combination (FIG. 7, block 740), thus resulting in optimization with respect to the minimum number of pallets. If the pallet plan for the selected aisle combination does not fit all the case units from the aisles in the selected aisle combination in the pallet load PALO (this means that some of the case units in the corresponding aisles remain unpacked for inclusion in other pallets, confirming or validating the optimization of the ratio RPA of pallets per aisle (FIG. 7, block 745)), the pallet plan is discarded (FIG. 7, block 750). The next entry from the list of aisle combinations ALC (e.g., the next aisle combination, which is aisle combination 2 in the above example) having the next highest Vcomb / Vp ratio is selected (FIG. 7, block 735), thus resulting in optimization with respect to the minimum number of pallets. With the case units CU corresponding to the aisles in the next aisle combination, a pallet load PALO is planned (FIG. 7, block 740), where blocks 740, 745, 750, 735 are repeated (for subsequent aisle combinations, e.g., aisle combination 2, aisle combination 3, aisle combination 4, etc.) until the pallet plan for the selected entry from the list of aisle combinations ALC succeeds in packing all case units for the corresponding aisles in the pallet load (e.g., pallet load PALO), again confirming or validating the optimization of the ratio of pallets per aisle RPA. Now, the aisle combinations are sequentially analyzed by the pallet load generator 165, 165' via iterative dual-loop DRL determination with respect to the pallet plan, until a planning solution is found that will include all case units ordered for the aisles in the aisle combination.

[0059] Using aisle combinations 1-4 above as an example of sequential analysis of aisle combinations, the pallet load generator 165, 165' first analyzes aisle combination 1 (aisles 2, 3, 8) to determine whether all ordered case units CU for aisles 2, 3, and 8 fit into one pallet load having a maximum volume Vp and maximum weight Wmax. For illustrative purposes, assume that not all ordered case units for aisles 2, 3, and 8 fit into one pallet load, and as such, the next aisle combination in the aisle combination sequence (e.g., aisle combination 2) is analyzed. Now, the pallet load generator 165, 165' analyzes aisle combination 2 (aisles 1, 4, 6, and 9) to determine whether all ordered case units CU for aisles 1, 4, 6, and 9 fit into one pallet load having a maximum volume Vp and maximum weight Wmax. For illustrative purposes, it is assumed that all ordered case units for aisles 1, 4, 6, and 9 will fit into one pallet load, and as such, the decision loop that sequentially analyzes the aisle combinations is stopped and the remaining aisle combinations (e.g., aisle combinations 3 and 4) are not analyzed. The updated set of aisle combinations (which are separate and different from the previous set of aisle combinations and exclude aisles in which all ordered case units have been assigned to a pallet load) is used to generate any subsequent pallet loads as described below.

[0060] Successful pallet plans (aisle combination 2 in the example above) form a planned pallet load PALO and are added to an output list ( FIG. 7 , block 755) executed by the automated package transport system 195 such that the automated package transport system 195 picks and sorts ( FIG. 12B , block 1220) case units CU in the planned pallet load PALO to build the planned pallet load ( FIG. 12B , block 1250) at the palletizer 162. In some aspects, picking and pallet building of case units for a given store order may occur substantially simultaneously with the planning of subsequent pallet loads in that store order, while in other aspects, picking and pallet building of case units may occur after all pallets have been planned for the store order.

[0061] In another loop of the iterative dual loop DRL, if the planned pallet load PALO is successfully planned, the pallet load generator 165, 165 determines whether there are any case units CU from any aisle in the store order that are not included in the (successful) planned pallet load PALO (FIG. 7, block 760). If there are no case units CU, the pallet planning is stopped (FIG. 7, block 765), the case units CU of the planned pallet load PALO for the store order are retrieved from storage and sorted by the automated package transport system 195 (FIG. 12B, block 1220), and the pallet load PALO is built by the palletizer 162 (FIG. 12B, block 1250). If there are any case units CU remaining, another (e.g., a subsequent) pallet is planned to be included in the store order (FIG. 7, block 770). The pallet load generator 165, 165' now updates the store aisle relationships (FIG. 12B, block 1240; FIG. 7, block 730) such that all combinations including any aisles completely consumed by a previous pallet (e.g., aisles where all ordered case units have already been assigned to a pallet load) are removed and an updated list of aisle combinations ALC is generated (FIG. 7, block 730). The iterative dual loop DRL continues until there are no unplanned case units CU for any aisle in the store order (i.e., all ordered case units are assigned to a pallet load). The pallet load generator 165, 165' is configured to associate each store aisle with each other (FIG. 12B, block 1240; see also FIG. 7, block 730 described herein) to minimize one or both of the total number of pallet loads and the ratio of pallets per aisle RPA in the order. As described herein, the term "aisle" as used herein generally refers to both order store aisles and product groups that are assigned integer values. As such, order store aisles (e.g., physical locations in an order store) are related to one another by at least one of an inter-aisle affinity characteristic and an inter-product group type affinity characteristic.

[0062] 8 is an exemplary store order 800 planned by the pallet load generator 165, 165' utilizing the clustered aisle pallet load package delivery methodology described above. In this exemplary order 800, the volume of case units in each aisle illustrated in FIG. 5 are shown included in each pallet load (e.g., pallet 1 through pallet 6), where each pallet load is sequentially planned (as described above) to have a volume Vcomb that is less than the maximum pallet volume Vp. As can be seen in FIG. 8, the volume V2 corresponding to case units CU ordered for aisle 2 is split (as described above) between pallet load 1 and pallet load 5, such that pallet load 1 is completely consumed by the case units ordered for aisle 2. It is noted that the last planned pallet load (e.g., pallet load 6) may have a smaller combined volume Vcomb than the previously planned pallet load (e.g., pallet load 1-5) because it includes a case unit for an aisle that was not included in the previous aisle combination for previously planned pallet loads 1-5, and the volume or weight of that previously planned pallet load has been optimized, resulting in an optimization of the minimum number of pallets and / or an optimization of the ratio RPA of pallets per aisle.

[0063] According to the clustered aisle palletized load package delivery method, the generated palletized load PALO(s) are built by the palletizer 162 and shipped to the order store 200 ( FIG. 12B , block 1260). The palletized load PALO(s) arrive at the order store 200 from the warehouse 199. With reference to FIG. 2 , the palletized load PALO(s) are generally received at the loading dock area 222 of the order store 200. Each of the palletized load PALO(s) includes products from several physical locations (e.g., aisles, departments, sections, etc.) of the order store 200. For illustrative purposes, these physical locations are referred to herein as aisles. While aisles 1-4 and aisles 11-14 are illustrated in FIG. 2 , it is noted that the order store may have any suitable number of aisles. In the clustered aisle palletized load package delivery method, case units CU stored on the palletized load PALO are unloaded (e.g., manually or by automation, such as an automated depalletizer similar to that described herein with respect to palletizers 162, 162′) from the palletized load PALO onto separate and distinct secondary palletized loads PALO21, PALO22, PALO23 (three are shown for illustrative purposes, it should be understood that the number of secondary palletized loads may be more or less than three), where each of the secondary palletized loads PALO21, PALO22, PALO23 includes case units CU from a separate single aisle. For example, palletized load PALO21 includes only case units CU assigned to aisle 1, palletized load PALO22 includes only case units CU assigned to aisle 3, and palletized load PALO23 includes only case units CU assigned to aisle 12. The secondary pallets PALO21, PALO22, PALO23 are moved (e.g., manually and / or by automated transport equipment) from the loading dock area 222 to their respective assigned aisles within the shopping area 224 of the order store 200, where the case units CU of each secondary pallet load PALO21, PALO22, PALO23 are unloaded and placed on their respective store shelves 233 in their respective assigned aisles.

[0064] In the clustered aisle pallet load package delivery method, the pallet load PALO may hold case units CU that are assigned to aisles that are spatially separated (e.g., far away) from each other in the order store 200. As described above, the unloading of the case units CU that are assigned to each aisle onto each secondary pallet load PALO21, PALO22, PALO23 is such that the pallet load PALOs that hold case units CU that are assigned to aisles that are spatially separated (e.g., far away) from each other have substantially little or no impact on the replenishment / storage of the store shelves 233. Here, in the clustered aisle pallet load package delivery method, case units CU from different aisles may be assigned to a common pallet load PALO (regardless of the proximity of the aisles) to maximize the number of full-sized pallet loads (e.g., pallet loads with the largest pallet load dimensions and / or weights) and minimize the number of pallet load PALOs on a transport device that moves the pallet load PALOs from the warehouse 199 to the order store 200.

[0065] 1, 4, 5, 9, 12, and 13, the pallet and order store affinity properties for the mixed mode clustered and adjacent aisle pallet load package delivery method are described in more detail. The mixed mode clustered and adjacent aisle pallet load package delivery method minimizes both (1) the number of pallets created from a given set of products and (2) the average pallet per aisle ratio RPA while minimizing the distance between the shelf locations of the case units assigned to each pallet. For purposes of illustration, aisle numbers that are numerically close to each other are also spatially close to each other (e.g., aisle 10 and aisle 11 are close to each other, while aisle 60 is far from both aisle 10 and aisle 11). Here, the clustered aisle pallet load package delivery method described above is modified such that pallet loads are scheduled (e.g., based on the contiguity or contiguity of one order store aisle to another order store aisle) at the order store 200 such that products on a common pallet are unloaded into aisles that are contiguous or adjacent to each other.

[0066] In the mixed mode clustering and adjacent aisle pallet load package delivery method, orders are originated by the order store 200 and affinity characteristics of at least one store order are determined in the manner described above with respect to Figure 12B, blocks 1200 and 1210. Blocks 700A, 700B, 710, 720 of Figure 13 are the same as similarly numbered blocks in Figure 7 described above. As such, entire pallets are scheduled from aisles having case unit volumes greater than the pallet load volume Vp and / or weights greater than the maximum pallet load Wmax, where the remaining case units ordered for those aisles are included in the aisle combination analysis in the manner described above (Figure 13, blocks 700A, 700B, 710, and 720). Aisle combinations for the mixed-mode clustered and adjacent aisle palletized package delivery methods are also determined in the manner described above with respect to Figure 7, block 730 (see also Figure 12B, block 1220), but the determined aisle combinations are sorted by a score S that takes into account the volume of the ordered case units for a given aisle, the weight of the ordered case units for a given aisle, and the proximity of the aisles included in the planned pallet (Figure 13, block 1330). For example, the score S may be determined by the following formula:

[0067]

number

[0068] where minAisle and maxAisle are the minimum and maximum number of aisles included in a given aisle combination, and d0 is a parameter greater than 0 that reflects the relative importance of aisle spread / distance to the volume of case units in a pallet load (e.g., store convenience). As can be seen in Equation 2, for small values ​​of d0, the aisle spread / distance is more important than the volume of case units in a pallet load, and for large values ​​of d0, the volume of case units in a pallet load is more important than the spread / distance between the aisles assigned to the pallet load. The determined aisle combinations (see FIG. 7, block 730) are weighted or scored with a score S and classified based on the score S (FIG. 13, block 1330). An iterative dual loop DRL is performed to plan pallets in the manner described above with respect to FIG. 7, blocks 735, 740, 745, 750, 755, 760, 765, 770 (see also FIG. 12B, block 1230) to result in optimization for the minimum number of pallets and to validate / confirm the optimization of the pallets per aisle ratio RPA, but for each subsequent pallet, the updated aisle combinations are again scored with a score S and sorted based on the score S. The ordered case units are picked and planned pallet loads PALO are built and shipped to the order store in the manner described above with respect to FIG. 12B, blocks 1240, 1250, and 1260.

[0069] FIG. 9 is an illustrative example of planned pallet loads (e.g., pallet 1 through pallet 7) determined by the mixed-mode clustering and adjacent aisle pallet load package delivery method. In this illustrative example, planned pallet loads are determined from an order having the aisles and respective case unit volumes illustrated in FIG. 2. As can be seen in FIG. 9, the first pallet load is planned from a portion V2A of the case unit volume from aisle 2 alone, and all other planned pallet loads in the store order have a volume Vcomb that is less than the predicted volume Vp of the pallet load (as described above). In accordance with the mixed-mode clustering and adjacent aisle pallet load package delivery method, planned pallet load 1 includes only the volume V1 of case units assigned to aisle 1. Planned pallet load 2 includes the volume V2B and volume V3 of case units assigned to aisle 2 and aisle 3. Planned pallet load 4 includes case unit volume V4 and volume V7 assigned to aisle 4 and aisle 7, and although planned pallet load 4 causes a break in the aisle sequence, it is noted that since aisle 4 is only three aisles away from aisle 7, this break is not a major break, which meets the objectives of the mixed mode clustered and adjacent aisle pallet load package delivery method. Planned pallet load 5 includes case unit volume V5 and volume V6 assigned to aisle 5 and aisle 6. Planned pallet load 6 includes case unit volumes V8-V11 assigned to aisles 8-11. Planned pallet load 7 includes case unit volume V12 assigned to aisle 12.

[0070] 14, in some embodiments of the mixed-mode clustering and adjacent aisle pallet load package delivery method, a maximum (or average) distance MDmax, generally expressed in terms of the difference between aisle numbers, between the aisles for the ordered case units CU assigned to any given pallet may be identified by the order store 200. This embodiment of the mixed-mode clustering and adjacent aisle pallet load package delivery method is the same as the embodiment described above, except that aisle combinations that include aisles with distances between aisles that exceed the maximum distance MDmax are filtered out / discarded before sorting the list of aisle combinations (see FIG. 14, block 1430).

[0071] In other aspects of the mixed-mode clustering and adjacent aisle pallet load package delivery method, as described herein with respect to FIG. 15, a pairwise relationship between aisle p and aisle q may be identified by the order store 200. The relationship between aisle p and aisle q may be expressed as an aisle affinity matrix A[p,q], where p and q belong to the set of all aisles present in the order. The aisle affinity matrix A[p,q] is diagonally symmetric such that A[p,q] is equal to A[q,p]. The values ​​of the aisle affinity matrix A[p,q] should be approximately equal to or close to 1 for "store friendly" aisles such that case units CU for the "store friendly" aisles should be on the same (e.g., single) pallet load. The values ​​of the aisle affinity matrix A[p,q] should be approximately equal to or close to 0 for "unfriendly" aisles, and the case units CU in these aisles should be kept separate with different pallet loads (e.g., separation of caustic products (e.g., detergent detergent) from food products (e.g., baby food), as discussed above). The diagonal elements of the aisle affinity matrix A[p,q] should be equal to 1 for each p, e.g., A[p,p]=1, implying that every aisle is friendly to itself.

[0072] Taking advantage of the pairwise relationships between aisles, the mixed-mode clustering and adjacent aisle palletized package delivery method remains as described above, but for all {p,q} belonging to a given aisle combination, the score S is modified as shown in the following formula:

[0073]

number

[0074] In Equation 3, the variable p is a multiplier, greater than or equal to 0, indicating the relative importance of pallet volume (e.g., minimizing the total number of pallets) and inter-aisle convenience in a given aisle combination. For smaller values ​​of p, aisle convenience is less important than minimizing the total number of pallets, while for larger values ​​of p, aisle convenience is more important than minimizing the total number of pallets. In the method described above (see FIG. 13), the determined aisle combinations are scored and sorted in descending order according to score, and starting with the first aisle combination in the sorted list of aisle combinations, a pallet load is planned for each successive aisle combination until a successful pallet load is planned, again optimizing the minimum number of pallets and validating / confirming the optimization of the pallets per aisle ratio RPA.

[0075] With reference to Figures 1, 3, 5, 10, 11, 12, and 15, the pallet and order store affinity characteristics for the adjacent aisle pallet load package delivery method are described in more detail. The adjacent aisle pallet load package delivery method of pallet planning can be utilized for warehouse customers to transport ordered pallets to store aisles to unload case units directly from the ordered pallets to store shelves. Here, as can be seen in Figure 3, each ordered pallet load PALOA, POLOA' is transported from one aisle to another along respective transport paths 300, 302 to unload the case units. The transport paths 300, 302 traverse the aisles in a sequence of consecutive aisles (e.g., pallet load PALOA travels through consecutive aisles 1-3, and pallet load POLOA' travels through consecutive aisles 11-13).

[0076] In the adjacent aisle pallet load package delivery method, the selection of consecutive or adjacent aisles is prioritized when planning a pallet load, while the total number of pallets planned for any given order is minimized and excessive splitting of aisles between pallets is substantially avoided. If an aisle is split between two pallets, no more than one aisle is split between the two pallets. An illustration of a pallet load planned with the "pure" adjacent aisle pallet load package delivery method is shown in FIG. 10. As with the other pallet load package delivery methods, an aisle with a volume larger than the given volume Vp of the pallet load (or a weight larger than the maximum weight Wmax of the pallet load) is selected and assigned to full pallets / entire pallets until the volume or weight remaining in the respective aisle is less than the volume Vp or weight Wmax (Aisle 2 has a volume V2 larger than the volume Vp of the pallet load, see FIG. 5). As can be seen in FIG. 10, pallet load 1 is completely consumed by a portion V2A of the volume V2 of aisle 2. As described herein, with a full pallet load scheduled from an excess aisle, all remaining volumes and weights of the aisles in the order (e.g., the volumes and weights of case units ordered for each respective aisle) are less than the volume Vp and weight Wmax of the pallet load. As such, each aisle is projected to fit a single pallet load, and in many instances will have a case unit quantity that is combined with other case units from other aisles in a single pallet load, resulting in a minimization of the number of pallets and the ratio of pallets per aisle RPA.

[0077] In the adjacent aisle pallet load package distribution method, the total number of pallet loads in an order and the ratio of pallets per aisle RPA are minimized, but to a lesser extent compared to allocating case units CU to pallets in a consecutive / adjacent aisle sequence (e.g., each available combination of order store aisles is determined based on the contiguity or adjacency of order store aisles in the available combinations rather than based on maximizing pallet loads (either by volume or weight). When planning pallet loads according to the adjacent aisle pallet load package allocation method, some aisles may split between pallets, but only when splits are avoided will additional pallets be generated, thereby increasing the total number of pallets planned for any given order.

[0078] If aisle splitting between pallet loads is not allowed, the total number of pallets may increase. For example, FIG. 11 illustrates a store order planned with an adjacent aisle pallet load package distribution method (such as that illustrated in FIG. 2) without splitting case units from the aisles between pallet loads (with the exception of any excess aisles, such as aisle 2, where, according to the adjacent aisle pallet load package distribution method, a portion of the case units for each excess aisle are consumed across the pallet load and the remainder of the case units are distributed among the remaining pallet loads). In FIG. 11, the resulting order plan includes seven pallet loads, the same number of pallet loads as the mixed mode clustered and adjacent aisle pallet load package delivery method, but one more pallet load than the clustered aisle pallet load package delivery method (the example delivery method is based on the case unit order for the aisles shown in FIG. 5). Also, as can be seen in FIG. 11, when not splitting aisles between pallet loads, most pallets have case unit volumes that are less than the maximum volume Vp of their respective pallet loads, whereas both the mixed mode clustered and adjacent aisle pallet load package allocation method and the clustered aisle pallet load package allocation method (with the exception of the last scheduled pallet load) have case unit volumes that are closer to the allowed volume Vp for the pallet load.

[0079] In order to increase the average pallet volume and reduce / minimize the number of planned pallets, splitting of case units CU from some aisles is implemented in the pallet planning, while prioritizing contiguous / adjacent aisle planning (e.g., store convenience). Here, the adjacent aisle pallet load package delivery method can be "modified" to utilize thresholds Vp0 and Vp1:

[0080]

number

[0081] The values ​​of Vp0 and Vp1 optimize the combination of pallet volumes (and minimize the number of pallets) and optimize the number of split aisles. The values ​​of Vp0 and Vp1 should be reasonably close to Vp, for example:

[0082]

number

[0083]

number

[0084] The values ​​of Vp0 and Vp1 are generally held constant (e.g., not changed during the iterative loop of pallet planning described herein), but may be adjusted for particular order profiles. For example, for very large case units, a decrease in Vp0 and Vp1 may be required since it is more likely that some case units will not fit on a given pallet load, while for small cases, an increase in Vp0 and Vp1 may be required since it is more likely that the case units will fit on a given pallet load.

[0085] In the adjacent aisle pallet load package delivery method, an order is placed by the order store 200 and at least one store order affinity characteristic is determined in the manner described above with respect to FIG. 12B, blocks 1200 and 1210. As described above, aisles having a volume greater than the predetermined volume Vp of a pallet load (or a weight greater than the maximum weight Wmax of a pallet load) are selected and assigned to full pallets / entire pallets. The number of pallets Np0 for the order is determined by the pallet load generator 165, 165' based on the remaining case unit volume Vrem and weight Wrem and the predicted product volume Vp and maximum weight Wmax in one pallet load according to the following formula (FIG. 15, block 1500):

[0086]

number

[0087] The pallet load generator 165, 165' relates the aisles to each other (FIG. 12B, block 1220, which is a sequential aisle relationship in this example) and determines the aisle combination that determines the case unit placement in the pallet load (FIG. 12B, block 1230). For example, the pallet load generator 165, 165' determines the aisle combination for the "next" pallet load (FIG. 15, block 1505, where the "next" pallet load is the currently planned pallet load). Here, the aisles are selected sequentially (e.g., i, i+1, i+2...) and for each added aisle (FIG. 15, block 1510), the cumulative case unit volume Vcomb and cumulative pallet weight Wcomb are updated (FIG. 15, block 1515). Now, the cumulative volume Vcomb is equal to or less than Vp0 and the cumulative weight Wcomb is equal to or less than Wmax (FIG. 15, block 1520), and the next aisle in the sequence of aisles is added to the aisle combination (FIG. 15, block 1510), resulting in validation / confirmation of the optimization of the pallets per aisle ratio RPA. Aisles are added sequentially to the aisle combination until either the cumulative volume Vcomb exceeds the value Vp0 or the cumulative weight Wcomb exceeds the maximum pallet load Wmax.

[0088] If the cumulative volume Vcomb exceeds a value Vp0 or the cumulative weight Wcomb exceeds a maximum pallet load Wmax, the remaining product volume Vrem and remaining product weight Wrem are updated (FIG. 15, block 1530). An updated estimate for the number of pallets Np1 for the order is determined by the pallet load generator 165, 165' using the updated Vrem and Wrem values ​​(i.e., the remaining volume and weight after the last aisle is selected in FIG. 15, block 1510 of the first nested loop RL1, which includes blocks 1510, 1515, 1520 of FIG. 15 and is nested within the overall / wider loop illustrated in blocks 1500-1580 and 1590 of FIG. 15) in a manner similar to that described above, but as follows:

[0089]

number

[0090] If the total number Np0 of pallets determined before the passage selection for the next pallet load is the same as the number Np1 of updated pallets (i.e., Np0 = Np1 + 1, where the number 1 represents the current pallet) (Figure 15, block 1536), the selection of the passage for the next pallet load is stopped, and the pallet load is planned from the passage combinations (Figure 15, block 1565), resulting in optimization regarding the minimum number of pallets.

[0091] If the number Np1 of updated pallets increases (i.e., Np0 < Np1 + 1), additional passages in the passage sequence are added to the passage combinations in the second nested loop RL2, which includes blocks 1540, 1545, 1550, 1555, 1560 of Figure 15 and is nested within the overall / broader loop illustrated in blocks 1500 - 1580 and 1590 of Figure 15 (Figure 15, block 1540). When the next sequential passage is added to the passage combination (Figure 15, block 1540), the cumulative case unit volume Vcomb and the cumulative pallet weight Wcomb are updated (Figure 15, block 1545). The remaining volume Vrem and the remaining weight Wrem of the case units in the order are also updated (Figure 15, block 1550). Using the above-described method (refer to Equation 8), but with the updated values of Vrem and Wrem determined in block 1550 of Figure 15, an updated estimated value for the (updated) number Np1 of pallets for the order is determined by the pallet load generators 165, 165' (Figure 15, block 1555). Here, if any one of the following conditions (Equations 9 - 11) is not satisfied, the recursive loop RL2 is repeated to add additional passages to the passage combination.

[0092]

Number

[0093]

Number

[0094]

number

[0095] If any one of the above conditions (equations 9-11) is met, the aisle selection for the next palletized load is stopped and the palletized load is planned from the aisle combination (FIG. 15, block 1565), again resulting in optimization with respect to the minimum number of pallets.

[0096] Once a pallet load is planned (FIG. 15, block 1565), unplanned products from an aisle combination (e.g., split aisle, such as aisle 6 split into case unit volumes V6A, V6B and aisle 12 split into case unit volumes V12A, V12B) are added to the remaining products in the order by the pallet load generator 165, 165′ (FIG. 15, block 1570). The pallet load generator 165, 165′ adds the planned pallet load (from FIG. 15, block 1565) to an output list of pallet loads (FIG. 15, block 1575), resulting in the construction of a pallet load in the output list. The pallet load generator 165, 165′ determines whether any case units CU remain in the order (FIG. 15, block 1580), again validating / confirming the optimization of the pallets per aisle ratio RPA. If the case unit is no longer present, pallet load planning for the order is stopped (FIG. 15, block 1585) and pallet loads PALOA, PALOA' are constructed in the manner described above with respect to FIG. 12B, blocks 1240, 1250, and 1260, and shipped to the order store 200. If case units CU remain in the order, the pallet count for the order is updated (FIG. 15, block 1590) and another pallet is planned for the order in the manner described above, resulting in minimizing the number of pallets.

[0097] In the adjacent aisle pallet load package delivery method described above, by increasing the volume of the selected case units above the first threshold volume Vp0, it may be possible that at least one aisle will not be fully packed into the currently planned pallet load, thereby increasing the likelihood that at least one aisle will overflow into the next subsequent pallet load being planned. Overflow of case units from one pallet load into the next subsequent pallet load may increase the value of the pallet per aisle ratio RPA and decrease the aisle adjacency (e.g., overall store convenience of the ordered pallet load). As described above, the values ​​of Vp0 and Vp1 may be adjusted to reflect the importance of minimizing the total number of pallets relative to the pallet per aisle ratio RPA. Higher values ​​of both Vp0 and Vp1 (e.g., closer to Vp) may decrease the number of predicted pallets, while lower values ​​of both Vp0 and Vp1 may decrease the likelihood of splitting aisles between pallets (but may increase the number of predicted pallets).

[0098] FIG. 11 illustrates the pallet loads of an order planned with the adjacent aisle pallet load package delivery method described above. As mentioned above, the volumes illustrated in FIG. 11 are those same volumes corresponding to the aisles illustrated in FIG. 5. According to the adjacent aisle pallet load package delivery method, a portion V2A of the volume V2 of aisle 2 consumes the entire pallet load / pallet load (e.g., pallet load 1), while the remaining volume V2B of aisle 2 is considered for pallet planning according to FIGS. 12 and 15 (as described above). It is noted that the volume V6 of aisle 6 is divided between pallet load 4 and pallet load 5, while the volume V12 of aisle 12 is divided between pallet load 6 and pallet load 7. The remaining volumes VI, V3, V4, V5, and V7-V11 for aisles 1, 3, 4, 5, and 7-11 as well as the remaining volume of aisle 2 are assigned to only one respective pallet load, and each pallet load has an uninterrupted sequence of aisles assigned to it. In this example, the total number of pallet loads is seven (as in FIG. 10, which pallet loads are planned with "pure" aisle adjacency, e.g., without employing thresholds Vp0, Vp1 and dual nested loops RL1, RL2), but in FIG. 11, the last pallet load (pallet load 7) has a smaller volume compared to the last pallet load of FIG. 10 and can be placed on top of another pallet load in the order, reducing the amount of floor space required to transport the ordered pallet loads. It is noted that, generally, the "modified" adjacent aisle pallet load package allocation method (which allows for the division of aisle case unit volume between pallet loads) results in a smaller number of planned pallet loads than the "pure" adjacent aisle pallet load package allocation method (which does not allow for the division of aisle case unit volume between pallet loads).

[0099] 1-4 and 16, a method for constructing a palletized load PALO according to any one or more of the clustered aisle palletized load package delivery method, the mixed mode clustered and adjacent aisle palletized load package delivery method, and the adjacent aisle palletized load package delivery method is described. Here, packages are placed on a pallet (see FIG. 1) to form a palletized load PALO (FIG. 16, block 1600). Individual case units CU are provided from the storage array 130 to an automated palletizer as described herein to form a palletized load PALO, where the palletized load PALO includes more than one composite layer L1-Ln of case units CU. The palletized load PALO is formed with case units CU placed on the palletized load PALO embodying at least one pallet and order store affinity characteristic 166, 166' for a given method of palletized load package delivery at the order store 200 (FIG. 16, block 1610). As described herein, the at least one pallet to order store affinity characteristic 166, 166' is for at least one of a clustered aisle pallet load package delivery method, a mixed mode clustered and adjacent aisle pallet load package delivery method, and an adjacent aisle pallet load package delivery method at the order store.

[0100] In accordance with one or more aspects of the present disclosure, a material handling system for processing and placing packages onto pallets for an order store includes a storage array with storage spaces for holding packages therein; an automated package transport system communicatively connected to the storage array for storing packages within the storage spaces of the storage array and retrieving packages from the storage spaces of the storage array; an automated palletizer for placing packages onto pallets to form pallet loads, the automated palletizer communicatively connected to the automated package transport system, the automated package transport system configured to provide individual packages from the storage array to the automated palletizer to form pallet loads, the pallet loads including more than one composite layer of packages; and a controller operatively connected to the automated palletizer, the controller programmed with a pallet load generator having at least one pallet and order store affinity characteristic for a predetermined method of pallet load package delivery at the order store, the pallet load generator configured such that a pallet load is formed by the automated palletizer with packages placed into the pallet load that embodies the at least one pallet and order store affinity characteristic.

[0101] According to one or more aspects of the present disclosure, the affinity characteristic of the at least one pallet to the order store is for at least one of a clustered aisle pallet load package delivery method, a mixed mode clustered and adjacent aisle pallet load package delivery method, and an adjacent aisle pallet load package delivery method at the order store.

[0102] According to one or more aspects of the present disclosure, an affinity characteristic of at least one pallet and order store is characterized by an iterative dual-loop determination, where at least one loop of the iterative dual-loop determination associates the order store aisles with each other.

[0103] According to one or more aspects of the present disclosure, within the determination of at least one loop, the order store aisles are associated with each other by at least one of an inter-aisle affinity characteristic and an inter-product group type affinity characteristic.

[0104] According to one or more aspects of the present disclosure, an affinity characteristic between aisles is the distance separating one order store aisle from another order store aisle, or the continuity or adjacency of one order store aisle with another order store aisle.

[0105] According to one or more aspects of the present disclosure, affinity characteristics of at least one pallet and order store are characterized by an iterative dual-loop determination, where at least one loop of the iterative dual-loop determination determines available combinations of order-store aisles that determine the placement of packages in the pallet load.

[0106] According to one or more aspects of the disclosure, each available combination of order store aisles is determined based on maximizing pallet load or a combination of maximizing pallet load and aisle contiguity or adjacency in the available combination, with maximizing pallet load being weighted more heavily than aisle contiguity or adjacency.

[0107] According to one or more aspects of the present disclosure, each available combination of order store aisles is determined based on contiguity or adjacency of the order store aisles in the available combinations, rather than based on maximizing pallet load.

[0108] According to one or more aspects of the present disclosure, a pallet load generator determines a pallet load according to at least one pallet and order store affinity characteristic such that the pallet load is maximized with respect to at least one of a maximum pallet load volume and a maximum pallet load weight.

[0109] According to one or more aspects of the present disclosure, a pallet load generator determines a pallet load according to at least one pallet and order store affinity characteristic such that the pallet load has a maximum number of packages from a minimum number of order store aisles.

[0110] According to one or more aspects of the present disclosure, a pallet load generator determines pallet loads according to at least one pallet and order store affinity characteristic to generate a minimum number of pallet loads for each order store.

[0111] According to one or more aspects of the disclosure, a pallet load generator determines, for each pallet load intended for an order store, a pallet load according to at least one pallet and order store affinity characteristic such that the packages forming the pallet load represent a minimum number of order store aisles.

[0112] According to one or more aspects of the disclosure, the pallet load generator determines, for each pallet load intended for an order store, a pallet load according to at least one pallet and order store affinity characteristic such that the determined pallet load represents a minimum number of order store aisles.

[0113] According to one or more aspects of the present disclosure, the pallet load generator is configured to sequentially determine each pallet load via an iterative dual-loop determination that characterizes affinity characteristics of at least one pallet and order store.

[0114] According to one or more aspects of the present disclosure, affinity characteristics of at least one pallet and order store are characterized by a dual nested loop determination, where at least one loop of the dual nested loop determination determines available combinations of order store aisles that associate order store aisles with each other or determine the placement of packages in the pallet load.

[0115] According to one or more aspects of the present disclosure, an automated palletizer includes an automated package picking device capable of moving packages from a package stack section to a pallet to form a pallet load from the packages, the pallet load including one or more composite layers of packages; and a controller operatively connected to the automated palletizer, the controller being programmed with a pallet load generator having at least one pallet and order store affinity characteristic for a predetermined method of pallet load package delivery at an order store, the pallet load generator being configured such that a pallet load is formed by the automated palletizer with packages placed on the pallet load that embodies the at least one pallet and order store affinity characteristic.

[0116] According to one or more aspects of the present disclosure, the affinity characteristic of the at least one pallet to the order store is for at least one of a clustered aisle pallet load package delivery method, a mixed mode clustered and adjacent aisle pallet load package delivery method, and an adjacent aisle pallet load package delivery method at the order store.

[0117] According to one or more aspects of the present disclosure, an affinity characteristic of at least one pallet and order store is characterized by an iterative dual-loop determination, where at least one loop of the iterative dual-loop determination associates the order store aisles with each other.

[0118] According to one or more aspects of the present disclosure, within the determination of at least one loop, the order store aisles are associated with each other by at least one of an inter-aisle affinity characteristic and an inter-product group type affinity characteristic.

[0119] According to one or more aspects of the present disclosure, an affinity characteristic between aisles is the distance separating one order store aisle from another order store aisle, or the continuity or adjacency of one order store aisle with another order store aisle.

[0120] According to one or more aspects of the present disclosure, affinity characteristics of at least one pallet and order store are characterized by an iterative dual-loop determination, where at least one loop of the iterative dual-loop determination determines available combinations of order-store aisles that determine the placement of packages in the pallet load.

[0121] According to one or more aspects of the disclosure, each available combination of order store aisles is determined based on maximizing pallet load or a combination of maximizing pallet load and aisle contiguity or adjacency in the available combination, with maximizing pallet load being weighted more heavily than aisle contiguity or adjacency.

[0122] According to one or more aspects of the present disclosure, each available combination of order store aisles is determined based on contiguity or adjacency of the order store aisles in the available combinations, rather than based on maximizing pallet load.

[0123] According to one or more aspects of the present disclosure, a pallet load generator determines a pallet load according to at least one pallet and order store affinity characteristic such that the pallet load is maximized with respect to at least one of a maximum pallet load volume and a maximum pallet load weight.

[0124] According to one or more aspects of the present disclosure, a pallet load generator determines a pallet load according to at least one pallet and order store affinity characteristic such that the pallet load has a maximum number of packages from a minimum number of order store aisles.

[0125] According to one or more aspects of the present disclosure, a pallet load generator determines pallet loads according to at least one pallet and order store affinity characteristic to generate a minimum number of pallet loads for each order store.

[0126] According to one or more aspects of the disclosure, a pallet load generator determines, for each pallet load intended for an order store, a pallet load according to at least one pallet and order store affinity characteristic such that the packages forming the pallet load represent a minimum number of order store aisles.

[0127] According to one or more aspects of the disclosure, the pallet load generator determines, for each pallet load intended for an order store, a pallet load according to at least one pallet and order store affinity characteristic such that the determined pallet load represents a minimum number of order store aisles.

[0128] According to one or more aspects of the present disclosure, the pallet load generator is configured to sequentially determine each pallet load via an iterative dual-loop determination that characterizes affinity characteristics of at least one pallet and order store.

[0129] According to one or more aspects of the present disclosure, affinity characteristics of at least one pallet and order store are characterized by a dual nested loop determination, where at least one loop of the dual nested loop determination determines available combinations of order store aisles that associate order store aisles with each other or determine the placement of packages in the pallet load.

[0130] According to one or more aspects of the present disclosure, a method for building a pallet load includes arranging packages on a pallet to form a pallet load, where individual packages are provided from a storage array to form the pallet load, where the pallet load includes more than one composite layer of packages, where the pallet load is formed with the packages arranged on the pallet load embodying at least one pallet and order store affinity characteristic for a given method of pallet load package delivery at the order store.

[0131] According to one or more aspects of the present disclosure, the affinity characteristic of the at least one pallet to the order store is for at least one of a clustered aisle pallet load package delivery method, a mixed mode clustered and adjacent aisle pallet load package delivery method, and an adjacent aisle pallet load package delivery method at the order store.

[0132] According to one or more aspects of the present disclosure, an affinity characteristic of at least one pallet and order store is characterized by an iterative dual-loop determination, where at least one loop of the iterative dual-loop determination associates the order store aisles with each other.

[0133] According to one or more aspects of the present disclosure, within the determination of at least one loop, the order store aisles are associated with each other by at least one of an inter-aisle affinity characteristic and an inter-product group type affinity characteristic.

[0134] According to one or more aspects of the present disclosure, an affinity characteristic between aisles is the distance separating one order store aisle from another order store aisle, or the continuity or adjacency of one order store aisle with another order store aisle.

[0135] According to one or more aspects of the present disclosure, affinity characteristics of at least one pallet and order store are characterized by an iterative dual-loop determination, where at least one loop of the iterative dual-loop determination determines available combinations of order-store aisles that determine the placement of packages in the pallet load.

[0136] According to one or more aspects of the disclosure, each available combination of order store aisles is determined based on maximizing pallet load or a combination of maximizing pallet load and aisle contiguity or adjacency in the available combination, with maximizing pallet load being weighted more heavily than aisle contiguity or adjacency.

[0137] According to one or more aspects of the present disclosure, each available combination of order store aisles is determined based on contiguity or adjacency of the order store aisles in the available combinations, rather than based on maximizing pallet load.

[0138] According to one or more aspects of the present disclosure, the pallet load is determined according to at least one pallet and order store affinity characteristic such that at least one of a maximum pallet load volume and a maximum pallet load weight is maximized.

[0139] According to one or more aspects of the present disclosure, a pallet load is determined according to at least one pallet and order store affinity characteristic to have a maximum number of packages from a minimum number of order store aisles.

[0140] According to one or more aspects of the present disclosure, pallet loads are determined according to at least one pallet-to-order store affinity characteristic to generate a minimum number of pallet loads for each order store.

[0141] According to one or more aspects of the present disclosure, pallet loads are determined according to affinity characteristics of at least one pallet and the order store such that for each pallet load intended for an order store, the packages forming the pallet load represent a minimum number of order store aisles.

[0142] According to one or more aspects of the present disclosure, a pallet load is determined according to at least one pallet and order store affinity characteristic, such that for each pallet load intended for an order store, the determined pallet load represents a minimum number of order store aisles.

[0143] According to one or more aspects of the present disclosure, each pallet load is sequentially determined via an iterative dual-loop determination that characterizes affinity characteristics of at least one pallet with an order store.

[0144] According to one or more aspects of the present disclosure, affinity characteristics of at least one pallet and order store are characterized by a dual nested loop determination, where at least one loop of the dual nested loop determination determines available combinations of order store aisles that associate order store aisles with each other or determine the placement of packages in the pallet load.

[0145] According to one or more aspects of the present disclosure, a pallet load includes one or more composite layers of packaging stacked on a pallet base, the one or more composite layers of packaging formed with packages disposed on the pallet load that embody at least one pallet and order store affinity characteristic for a given method of pallet load package delivery at the order store.

[0146] According to one or more aspects of the present disclosure, the affinity characteristic of the at least one pallet to the order store is for at least one of a clustered aisle pallet load package delivery method, a mixed mode clustered and adjacent aisle pallet load package delivery method, and an adjacent aisle pallet load package delivery method at the order store.

[0147] According to one or more aspects of the present disclosure, an affinity characteristic of at least one pallet and order store is characterized by an iterative dual-loop determination, where at least one loop of the iterative dual-loop determination associates the order store aisles with each other.

[0148] According to one or more aspects of the present disclosure, within the determination of at least one loop, the order store aisles are associated with each other by at least one of an inter-aisle affinity characteristic and an inter-product group type affinity characteristic.

[0149] According to one or more aspects of the present disclosure, an affinity characteristic between aisles is the distance separating one order store aisle from another order store aisle, or the continuity or adjacency of one order store aisle with another order store aisle.

[0150] According to one or more aspects of the present disclosure, affinity characteristics of at least one pallet and order store are characterized by an iterative dual-loop determination, where at least one loop of the iterative dual-loop determination determines available combinations of order-store aisles that determine the placement of packages in the pallet load.

[0151] According to one or more aspects of the disclosure, each available combination of order store aisles is determined based on maximizing pallet load or a combination of maximizing pallet load and aisle contiguity or adjacency in the available combination, with maximizing pallet load being weighted more heavily than aisle contiguity or adjacency.

[0152] According to one or more aspects of the present disclosure, each available combination of order store aisles is determined based on contiguity or adjacency of the order store aisles in the available combinations, rather than based on maximizing pallet load.

[0153] According to one or more aspects of the present disclosure, the pallet load is determined according to at least one pallet and order store affinity characteristic such that at least one of a maximum pallet load volume and a maximum pallet load weight is maximized.

[0154] According to one or more aspects of the present disclosure, a pallet load is determined according to at least one pallet and order store affinity characteristic to have a maximum number of packages from a minimum number of order store aisles.

[0155] According to one or more aspects of the present disclosure, pallet loads are determined according to at least one pallet-to-order store affinity characteristic to generate a minimum number of pallet loads for each order store.

[0156] According to one or more aspects of the present disclosure, pallet loads are determined according to affinity characteristics of at least one pallet and the order store such that for each pallet load intended for an order store, the packages forming the pallet load represent a minimum number of order store aisles.

[0157] According to one or more aspects of the present disclosure, a pallet load is determined according to at least one pallet and order store affinity characteristic, such that for each pallet load intended for an order store, the determined pallet load represents a minimum number of order store aisles.

[0158] According to one or more aspects of the present disclosure, each pallet load is sequentially determined via an iterative dual-loop determination that characterizes affinity characteristics of at least one pallet with an order store.

[0159] According to one or more aspects of the present disclosure, affinity characteristics of at least one pallet and order store are characterized by a dual nested loop determination, where at least one loop of the dual nested loop determination determines available combinations of order store aisles that associate order store aisles with each other or determine the placement of packages in the pallet load.

[0160] It should be understood that the foregoing description is merely illustrative of aspects of the present disclosure. Various alternatives and modifications may be contemplated by those skilled in the art without departing from the aspects of the present disclosure. Accordingly, the aspects of the present disclosure are intended to embrace all such alternatives, modifications, and variations that are within the scope of any claims appended hereto. Moreover, the mere fact that different features are recited in mutually different dependent or independent claims does not indicate that a combination of these features cannot be used to advantage and that such combination remains within the scope of the aspects of the present disclosure.

Claims

1. 1. A material handling system for processing and placing packages onto pallets for an order store, the material handling system comprising: a storage array having a storage space for holding the packages therein; an automated package transport system communicatively connected to the storage array for storing packages in the storage spaces of the storage array and retrieving packages from the storage spaces of the storage array; an automated palletizer for placing packages onto pallets to form a pallet load, the automated palletizer communicatively connected to the automated package transport system, the automated package transport system configured to provide individual packages from the storage array to the automated palletizer to form the pallet load, the pallet load including more than one composite layer of packages; a controller operatively connected to the automated palletizer, the controller being programmed with a pallet load generator having at least one pallet and order store affinity characteristic for a predetermined method of pallet load package delivery at the order store, the pallet load generator being configured such that the pallet load is formed by the automated palletizer with packages placed on the pallet load that embody the at least one pallet and order store affinity characteristic; A material handling system comprising:

2. 2. The material handling system of claim 1, wherein the affinity characteristic of the at least one pallet to order store is for at least one of a clustered aisle pallet load package delivery method, a mixed-mode clustered and adjacent aisle pallet load package delivery method, and an adjacent aisle pallet load package delivery method at the order store.

3. The material handling system of claim 1 , wherein the affinity characteristics of the at least one pallet and order store are characterized by a recursive dual-loop determination, and at least one loop of the recursive dual-loop determination associates order store aisles with each other.

4. 2. The material handling system of claim 1, wherein the affinity characteristics of the at least one pallet and order store are characterized by an iterative dual-loop determination, and at least one loop of the iterative dual-loop determination determines available combinations of order store aisles that determine package placement in the pallet load.

5. 2. The material handling system of claim 1, wherein the pallet load generator determines the pallet load according to affinity characteristics of the at least one pallet and order store such that the pallet load is maximized with respect to at least one of a maximum pallet load volume and a maximum pallet load weight.

6. 2. The material handling system of claim 1, wherein the pallet load generator determines the pallet load according to affinity characteristics of the at least one pallet and an order store such that the pallet load has a maximum number of packages from a minimum number of order store aisles.

7. The material handling system of claim 1 , wherein the pallet load generator determines the pallet loads according to an affinity characteristic of the at least one pallet and an order store so as to generate a minimum number of pallet loads for each order store.

8. 2. The material handling system of claim 1, wherein the pallet load generator determines, for each pallet load intended for the order store, the pallet load according to an affinity characteristic of the at least one pallet and the order store such that the packages forming the pallet load represent a minimum number of order store aisles.

9. 2. The material handling system of claim 1, wherein the pallet load generator determines, for each pallet load intended for the order store, the pallet load according to an affinity characteristic of the at least one pallet and the order store such that the determined pallet load represents a minimum number of order store aisles.

10. The material handling system of claim 1 , wherein the pallet load generator is configured to sequentially determine each pallet load via an iterative dual-loop determination that characterizes affinity characteristics of the at least one pallet and an order store.

11. 2. The material handling system of claim 1, wherein the affinity characteristics of the at least one pallet and order store are characterized by a dual nested loop determination, and at least one loop of the dual nested loop determination determines available combinations of order store aisles that associate order store aisles with each other or determine package placement in the pallet load.

12. An automated palletizer, comprising: an automated package picking device capable of moving packages from a package deposition section to a pallet to form a pallet load from said packages, said pallet load including more than one composite layer of packages; a controller operatively connected to the automated palletizer, the controller being programmed with a pallet load generator having at least one pallet and order store affinity characteristic for a predetermined method of pallet load package delivery at an order store, the pallet load generator being configured such that the pallet load is formed by the automated palletizer with packages placed on the pallet load embodying the at least one pallet and order store affinity characteristic; An automated palletizer comprising:

13. 13. The automated palletizer of claim 12, wherein the affinity characteristic of the at least one pallet to order store is for at least one of a clustered aisle pallet load package delivery method, a mixed mode clustered and adjacent aisle pallet load package delivery method, and an adjacent aisle pallet load package delivery method at the order store.

14. The automated palletizer of claim 12 , wherein the affinity characteristic of the at least one pallet and order store is characterized by a recursive dual-loop determination, and at least one loop of the recursive dual-loop determination associates order store aisles with each other.

15. 13. The automated palletizer of claim 12, wherein the affinity characteristics of the at least one pallet and order store are characterized by an iterative dual-loop determination, and at least one loop of the iterative dual-loop determination determines available combinations of order store aisles that determine package placement in the pallet load.

16. 13. The automated palletizer of claim 12, wherein the pallet load generator determines the pallet load according to affinity characteristics of the at least one pallet and order store such that the pallet load is maximized in terms of at least one of a maximum pallet load volume and a maximum pallet load weight.

17. 13. The automated palletizer of claim 12, wherein the pallet load generator determines the pallet load according to affinity characteristics of the at least one pallet and an order store such that the pallet load has a maximum number of packages from a minimum number of order store aisles.

18. The automated palletizer of claim 12 , wherein the pallet load generator determines the pallet loads according to affinity characteristics of the at least one pallet and an order store so as to generate a minimum number of pallet loads for each order store.

19. 13. The automated palletizer of claim 12, wherein the pallet load generator determines, for each pallet load intended for an order store, the pallet load according to an affinity characteristic of the at least one pallet and the order store such that the packages forming the pallet load represent a minimum number of order store aisles.

20. 13. The automated palletizer of claim 12, wherein the pallet load generator determines, for each pallet load intended for an order store, the pallet load according to an affinity characteristic of the at least one pallet and the order store such that the determined pallet load represents a minimum number of order store aisles.

21. The automated palletizer of claim 12 , wherein the pallet load generator is configured to sequentially determine each pallet load via an iterative dual-loop determination that characterizes affinity characteristics of the at least one pallet and an order store.

22. 13. The automated palletizer of claim 12, wherein the affinity characteristics of the at least one pallet and order store are characterized by a dual nested loop determination, and at least one loop of the dual nested loop determination determines available combinations of order store aisles that associate order store aisles with each other or determine package placement in the pallet load.

23. 1. A method for constructing a pallet load, said method comprising: arranging packages on a pallet to form a pallet load, wherein individual packages are provided from a storage array to form said pallet load, said pallet load including more than one composite layer of packages; The method, wherein the pallet load is formed of packages placed on the pallet load that embody at least one pallet and order store affinity characteristic for a predetermined method of pallet load package delivery at the order store.

24. 24. The method of claim 23, wherein the affinity characteristic of the at least one pallet to order store is for at least one of a clustered aisle pallet load package delivery method, a mixed-mode clustered and adjacent aisle pallet load package delivery method, and an adjacent aisle pallet load package delivery method at the order store.

25. 24. The method of claim 23, wherein the affinity characteristics of the at least one pallet and order store are characterized by an iterative dual-loop determination, and at least one loop of the iterative dual-loop determination associates order store aisles with each other.

26. 24. The method of claim 23, wherein the affinity characteristics of the at least one pallet and order store are characterized by an iterative dual-loop determination, and at least one loop of the iterative dual-loop determination determines available combinations of order store aisles that determine package placement in the pallet load.

27. 24. The method of claim 23, wherein the pallet load is determined according to affinity characteristics of the at least one pallet and order store such that at least one of a maximum pallet load volume and a maximum pallet load weight is maximized.

28. 24. The method of claim 23, wherein the pallet load is determined according to affinity characteristics of the at least one pallet and order store to have a maximum number of packages from a minimum number of order store aisles.

29. 24. The method of claim 23, wherein the pallet loads are determined according to an affinity characteristic of the at least one pallet and order store to generate a minimum number of pallet loads for each order store.

30. 24. The method of claim 23, wherein the pallet loads are determined according to an affinity characteristic of the at least one pallet and the order store such that, for each pallet load intended for an order store, the packages forming the pallet load represent a minimum number of order store aisles.

31. 24. The method of claim 23, wherein the pallet load is determined according to an affinity characteristic of the at least one pallet and the order store such that, for each pallet load intended for an order store, the determined pallet load represents a minimum number of order store aisles.

32. 24. The method of claim 23, wherein each pallet load is sequentially determined via an iterative dual-loop determination that characterizes affinity characteristics of the at least one pallet and order store.

33. 24. The method of claim 23, wherein the affinity characteristics of the at least one pallet and order store are characterized by a dual nested loop determination, and at least one loop of the dual nested loop determination determines available combinations of order store aisles that associate order store aisles with each other or determine package placement in the pallet load.

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