Product arrangement planning system and product arrangement planning method

Abstract

A product arrangement planning system (1) according to the present invention is characterized by being provided with a product arrangement plan generation unit (22) that refers to shipping schedule information (33) and picking method information (32), the shipping schedule information (33) being information in which scheduled shipping amounts per unit period of a plurality of products handled in a logistics warehouse are stored, and the picking method information (32) being information in which at least one of processing efficiency and an upper limit of personnel who can be engaged is stored for each of a plurality of picking methods carried out in the logistics warehouse, and that assigns picking methods in the picking method information to the products on the basis of variations in the scheduled shipping amounts of the products among the individual unit periods in the shipping schedule information, thereby generating a product arrangement plan for a product arrangement period longer than the unit period.

Description

Product placement planning system and product placement planning method

[0001] The present invention relates to a product placement planning system and a product placement planning method. In particular, the present invention relates to product placement planning when multiple picking methods with at least different processing efficiencies are mixed together.

[0002] With advancements in automation technology and the diversification of products, picking operations that combine at least multiple methods with different processing efficiencies have become mainstream. When the shipment volume of each product in the warehouse fluctuates, it is important to maximize the processing efficiency of each picking method in order to carry out picking operations efficiently. Generally, in logistics warehouses, the picking method is fixed for each picking area within the warehouse. Therefore, in order to improve the efficiency of picking operations, the placement of products in the picking area is important.

[0003] As a technology for improving the efficiency of picking operations, there is an item placement optimization system described in Patent Document 1. This system calculates a recommended capacity value for each of the multiple frontage spaces, each having multiple shelves and on which multiple products are placed, based on the predicted shipment volume of the products placed in that frontage space as a result of future demand forecasts for a specified period. Furthermore, based on the current capacity value and the recommended capacity value for each of the multiple frontage spaces, the system determines one or more replacement pairs, each of which is a pair of frontage spaces where product replacement will occur.

[0004] Japanese Patent Publication No. 2021-120891

[0005] In logistics warehouse picking operations, the amount of goods shipped using each picking method, as well as the overall productivity of the logistics warehouse, changes depending on which goods are placed in which picking area. For example, by arranging goods so that more picking operations are performed using the picking method with the highest processing efficiency (details below), it becomes possible to ship a large amount of goods with high efficiency, thereby improving the overall productivity of the logistics warehouse.

[0006] Here, the shipment volume for each product may fluctuate over short periods, such as daily or weekly. In this case, the total shipment volume for each picking method will fluctuate daily. Therefore, if the planned shipment volume exceeds or falls below the capacity limit (details below) of each picking method, overtime work may occur, or the overall productivity of the logistics warehouse may decrease.

[0007] While it is possible to bring the planned shipment volume closer to the capacity limit of each picking method by changing the product arrangement for each unit period, this would require the work of swapping products each unit period, potentially increasing the overall workload of the logistics warehouse and reducing productivity.

[0008] Patent Document 1's item placement optimization system only assumes a picking method in which workers pick items from multiple shelves in multiple opening spaces. For example, when picking areas where multiple picking methods are used are mixed, a separate measure was needed to improve overall productivity. Therefore, the present invention aims to improve the productivity of the entire logistics warehouse in accordance with fluctuations in the shipment volume of each item per unit period.

[0009] The product placement planning system of the present invention is characterized by comprising a product placement plan generation unit that generates a product placement plan for a product placement period longer than the unit period by referring to shipment schedule information which stores the planned shipment quantities for each unit period of multiple products handled in a logistics warehouse, and pick method information which stores at least one of the processing efficiency and the maximum number of personnel that can be engaged for each of multiple pick methods implemented in the logistics warehouse, and assigning a pick method from the pick method information to the product based on the fluctuation of the planned shipment quantities for each unit period of the product in the shipment schedule information. Other means will be described in the embodiments for carrying out the invention.

[0010] According to the present invention, the productivity of the entire logistics warehouse can be improved in response to fluctuations in the shipment volume of each product for each unit period.

[0011] This is a diagram showing the configuration of the product placement planning system. This is a diagram showing an example of the configuration of a logistics warehouse. This is a diagram explaining the relationship between fluctuations in shipment volume and capacity limits. This is a diagram explaining the relationship between fluctuations in shipment volume and capacity limits. This is a diagram explaining the distribution of product shipment volumes and product groups. This is a diagram explaining the distribution of product shipment volumes and product groups. This is a diagram showing an example of product storage information. This is a diagram showing an example of picking method information. This is a diagram showing an example of shipment schedule information. This is a diagram showing an example of a product placement plan. This is a diagram showing an example of an output screen. This is a flowchart of the processing procedure.

[0012] Hereinafter, embodiments of the present invention ("this embodiment") will be described in detail with reference to the drawings. This embodiment is an example in which three different picking methods are mixed within the same logistics warehouse. However, the present invention is not to be interpreted as being limited to the contents of this embodiment. The specific configuration can be changed without departing from the idea or spirit of the present invention. In this embodiment, "goods" means articles that can be stored in a logistics warehouse, regardless of whether they are the subject of a transaction or not. Note that "pick" means to select or collect, but here it refers to the work of taking goods out of a storage place such as a warehouse or logistics center (picking).

[0013] (Configuration of the product placement planning system) Figure 1 shows the configuration of the product placement planning system 1. The product placement planning system 1 is a general-purpose computer and includes a central control unit 11, input devices 12 such as a camera, microphone, mouse, and keyboard, output devices 13 such as a speaker and display, main memory 14, auxiliary memory 15, and communication device 16.

[0014] The input processing unit 21, the product placement plan generation unit 22, and the output processing unit 23 in the main memory 14 are programs. The product placement plan generation unit 22 includes an optimization calculation model 22b (details described later). In the following description, when the subject is described as "the ○○ unit," it means that the central control unit 11 reads the program from the auxiliary storage device 15 into the main memory 14 and realizes the function of the program. The auxiliary storage device 15 stores product storage information 31, picking method information 32, shipping schedule information 33, and product placement plan 34 (details described later). The auxiliary storage device 15 may be configured independently as a separate enclosure. That is, the product placement planning system 1 performs information processing using product storage information 31, etc., but this information may be stored in a cloud server or the like located outside the product placement planning system 1.

[0015] (Logistics Warehouse Configuration) Figure 2 shows an example of the configuration of a logistics warehouse 40. The logistics warehouse 40 has multiple picking methods that differ in at least one of the following: processing efficiency and the maximum number of personnel that can be employed (details below). Corresponding to each picking method, there is a picking area 41, which is a workspace where the picking work is actually carried out. The goods picked in each picking area 41 are transported to the next process. At this time, the destination after picking is set for each picking method. The goods transport work may be done manually 42, or it may be done automatically using transport robots 43 and conveyors 44.

[0016] In the logistics warehouse 40, each product is typically stored in a product box such as a cardboard box or container, and these product boxes are stored in a picking area 41 corresponding to each picking method. Typical picking methods in the logistics warehouse 40 are as follows:

[0017] (Picking Methods) The first picking method is an "automated warehouse" in which product boxes are automatically stored in the warehouse using an automated machine device 45 that performs storage and retrieval, and product boxes are retrieved and products are picked as needed. The second picking method is "shelf transport" in which product shelves 46 in which product boxes are stored are transported to the picking area, and pickers take out the necessary products from the product shelves 46. The third picking method is a "cart" in which workers move between product shelves in which product boxes are stored using a cart 47 and take out the necessary products from the product shelves.

[0018] In these picking methods, the picking operation of removing products from product boxes may be performed manually or automatically using a picking robot or the like. The three types of picking methods described above are examples of various picking methods, and actual picking methods are not limited to these. The logistics warehouse 40 performs the product picking operation in the following procedure.

[0019] (Procedure 1) The logistics warehouse 40 receives order quantity information for each product, or predicts future shipment quantities based on past performance, to create shipment schedule information 33 (details below), and decides which picking method to use for each product based on the shipment schedule information 33. (Procedure 2) For each picking method, the logistics warehouse 40 takes out the required quantity of products from the product boxes and stores them in picking boxes such as containers. (Procedure 3) The logistics warehouse 40 transports the picking boxes containing the picked products to the next process.

[0020] (Capacity Limit and Processing Efficiency) The capacity limit for each picking method represents the upper limit of the shipment volume per unit period, calculated from the processing efficiency and the maximum number of personnel that can be employed for that picking method. Processing efficiency (unit: rows / person-hour) is the result of dividing the shipment volume (unit: rows) by the work hours (unit: person-hour). The higher the processing efficiency and the larger the maximum number of personnel that can be employed, the higher the capacity limit. A predetermined period longer than the unit period is called the "product placement period". In this embodiment, the unit period is one day (each day of the week). In this embodiment, the product placement period is one week, which is longer than the unit period (details below). In reality, the product placement period is at least one week and is often an integer multiple of one month. During the product placement period, the placement of products does not change. That is, each product continues to be picked using the same picking method. The length of the product placement period may or may not be fixed.

[0021] In this embodiment, assigning a picking method to a product is synonymous with determining the placement of the product, and is also synonymous with assigning a picking area to the product. This is because the picking method for a product is uniquely determined depending on where the product is placed.

[0022] (Function of the Product Placement Plan Generation Unit) The Product Placement Plan Generation Unit 22 generates a Product Placement Plan 34 in which a picking method is assigned to each product according to fluctuations in the planned shipment volume of each product. Examples of methods for generating the Product Placement Plan 34 are the following Placement Method 1 and Placement Method 2.

[0023] (Placement Method 1) The product placement plan generation unit 22 sorts the products scheduled for shipment in descending order of planned shipment volume during the product placement period, and in descending order of fluctuation in planned shipment volume, and assigns picking methods to the sorted products in descending order of processing efficiency. At this time, the product placement plan generation unit 22 generates a product placement plan 34 such that the planned shipment volume of the products placed in the picking area 41 of each picking method does not exceed the capacity limit.

[0024] (Placement Method 2) The product placement plan generation unit 22 classifies the products scheduled for shipment into one or more product groups. At this time, the product placement plan generation unit 22 determines the product groups such that the total fluctuation in the planned shipment volume of the products belonging to each product group is small during the product placement period. Next, the product placement plan generation unit 22 rearranges each product group in descending order of planned shipment volume during the product placement period and in descending order of fluctuation in planned shipment volume, and assigns picking methods to the rearranged product groups in descending order of processing efficiency. At this time, the product placement plan generation unit 22 determines the product placement plan 34 such that the planned shipment volume of the product groups placed in the picking area 41 of each picking method does not exceed the capacity limit.

[0025] Generally, the number of product types is far greater than the number of picking methods. Therefore, one picking method can be assigned to many different types of products. Arrangement method 2 is based on this premise. Arrangement method 2 is a characteristic arrangement method of this embodiment.

[0026] The product placement plan generation unit 22 generates a product placement plan 34 using placement method 1 and / or placement method 2. The product placement plan generation unit 22 may use the product placement plan 34 generated by this placement method as an initial solution and generate other initial solutions by swapping products that have been assigned an arbitrary picking method (details below).

[0027] The product placement plan 34 generated by the product placement plan generation unit 22 may consist of one or more items. Furthermore, it is not necessary for all of the future planned shipment volumes for each product to be known. In this case, the product placement plan 34 can be generated by classifying the products into product groups, as in placement method 2, based on information such as fluctuations in planned shipment volumes and shipment trends.

[0028] (Relationship between shipment volume fluctuations and capacity limits) Figures 3 and 4 illustrate the relationship between shipment volume fluctuations and capacity limits. Hereafter, unless otherwise specified, the planned shipment volume for the future product placement period will simply be referred to as "shipment volume." The shipment volume of each product fluctuates according to the unit period, such as the day of the week (Monday, Tuesday, ..., Sunday). For example, the shipment volume of office supplies is high on Mondays. The shipment volume of energy drinks is high on Wednesdays, when work fatigue begins. The shipment volume of leisure goods is high on Fridays. These are extreme examples that are easy to understand, but it has been empirically observed that for daily necessities (consumables), for example, the peak in shipment volume appears periodically according to the volume of the sales unit. Furthermore, it has been empirically observed that for office supplies used by financial institutions, etc., the peak in shipment volume appears on settlement days (5th, 10th, ..., last day of the month).

[0029] First, let's look at Figure 3, which illustrates a conventional example. The product placement plan generation unit 22 determines the placement of each product using the average shipment volume during the product placement period. As a result, products with similar timing of fluctuations (increases and decreases) may be assigned the same picking method. The product placement plan generation unit 22 assigns a picking method with high processing efficiency to products with a large average shipment volume.

[0030] As a result, at specific times, the shipment volume 102 per unit period exceeds or falls significantly below the capacity limit 106. If the shipment volume 102 per unit period exceeds the capacity limit 106, overtime work occurs or shipments are delayed. If the shipment volume 102 per unit period falls significantly below the capacity limit 106, the processing efficiency of the picking method is not fully utilized, and the overall productivity of the logistics warehouse decreases.

[0031] Next, let's look at Figure 4, which illustrates an example of this embodiment. The product placement plan generation unit 22 combines products whose shipment volume 102 fluctuates (increases or decreases) at different timings to form product groups, and assigns the same picking method to each product group so as not to exceed the capacity limit 106 of each picking method. Here again, the product placement plan generation unit 22 assigns picking methods with high processing efficiency to product groups with a large average shipment volume.

[0032] As a result, compared to Figure 3, the fluctuation in shipment volume 102 during product placement periods longer than the unit period becomes smoother, and the shipment volume no longer exceeds the capacity limit 106. This helps to suppress the decline in overall productivity of the logistics warehouse, such as overtime and shipping delays.

[0033] The three types of lines within the ellipse in Figure 4 indicate that products with different peak and bottom shipping volumes generate product groups. More specifically, the type of line represents the magnitude of the shipping volume (dotted line > single dotted line > dashed line). The thickness of the line represents the magnitude of the fluctuation in shipping volume (thick > medium > thin). The product placement plan generation unit 22 assigns each product to either an automated warehouse, shelf transport, or cart. As mentioned above, in this case, the product placement plan generation unit 22 assigns a picking method with higher processing efficiency to a product the larger the shipping volume, and assigns a picking method with higher processing efficiency to a product the smaller the fluctuation in shipping volume.

[0034] The capacity limit 106 is determined by processing efficiency and the maximum number of personnel that can be employed. Therefore, the higher the processing efficiency or the larger the maximum number of personnel that can be employed, the higher the capacity limit (the amount of goods shipped per unit period for each picking method). On the other hand, the higher the processing efficiency of each picking method, the more personnel are needed to achieve the same amount of goods shipped. From the perspective of overall productivity and labor saving in the logistics warehouse, it is desirable to achieve a amount of goods shipped close to the capacity limit using a picking method with high processing efficiency throughout the product placement period.

[0035] Therefore, the product placement planning system 1 needs to generate a product placement plan 34 that enables a larger shipment volume from the pick area 41, which has a high processing efficiency, and reduce the number of personnel working in the pick area 41, which is well below the capacity limit 106.

[0036] The difference between Figure 3 and Figure 4 lies in the presence or absence of the idea of ​​appropriately combining the variance of each product's shipment volume over the product placement period to reduce the overall variance of the product group. This will be further explained in Figures 5 and 6.

[0037] (Dispersion of Product Shipment Volume and Product Groups) Figures 5 and 6 are diagrams for explaining the dispersion of product shipment volume and product groups. First, focus on Figure 5 which illustrates a conventional example. The product layout plan generation unit 22 manages the shipment volume for each day of the week (columns 102a to 102g) and the weekly average of the shipment volume (column 102h) for each product (column 101). The "#" indicates different numerical values in an abbreviated manner. The product layout plan generation unit 22 associates available picking methods (column 103) with product groups composed of multiple products without particularly considering the variation in the shipment volume for each day of the week for each product (e.g., the dispersion over 7 days). At this time, the product layout plan generation unit 22 assigns a picking method with a higher processing efficiency (column 104) to a product group as the group average (column 102j), which is the average of the shipment volume of the product group, is larger.

[0038] In the example of Figure 5, products A, B, and C form product group ABC. Similarly, products D, E, and F form product group DEF, and products G, H, and J form product group GHJ. Among these product groups, the product group with the largest group average is product group ABC, and the smallest is product group CFJ. Among the three types of picking methods, the one with the highest processing efficiency is the automated warehouse, and the lowest is the cart. Therefore, the product layout plan generation unit 22 assigns, for example, the automated warehouse to product group ABC.

[0039] The product layout plan generation unit 22 manages the processing efficiency (column 104), the working hours (column 105), and the capacity limit (column 106) in association with the picking method (column 103). The productivity (column 107) is defined for the entire logistics warehouse and is the result of dividing the shipment volume for one day or one week by the working hours for one day or one week. In the conventional example, as a result of the shipment volume significantly exceeding or falling far below the capacity limit at a certain timing in one week, the productivity decreases.

[0040] Next, pay attention to FIG. 6 which illustrates an example of the present embodiment. The difference between FIG. 6 and FIG. 5 is that it has variance (column 102k) and group variance (column 102m). Now, assume that the shipment volume of product A is larger on Monday than on other days of the week, the shipment volume of product D is larger on Tuesday and Wednesday than on other days of the week, and the shipment volume of product G is larger on Friday, Saturday, and Sunday than on other days of the week. Then, the peaks and bottoms of the shipment volumes of product A, product D, and product G cancel each other out, and the fluctuation of the shipment volume as the product group ADG stabilizes throughout the week. That is, even if the variance of the shipment volume of each of product A, product D, and product G is relatively large, the variance of the shipment volume of the product group ADG is relatively small. The "large" in column 102k of FIG. 6 should be compared with the "small" in column 102m.

[0041] Among these product groups, the one with the largest group average is the product group ADG, and the one with the smallest is the product group CFJ. Among the three types of picking methods, the one with the highest processing efficiency is the automated warehouse, and the one with the lowest is the cart. Therefore, the product placement plan generation unit 22, for example, assigns the automated warehouse to the product group ADG. This assignment method is the same as that in FIG. 5. However, throughout the week, as the shipment volume stably remains below the capacity limit, productivity increases.

[0042] (Product storage information) FIG. 7 is a diagram showing an example of the product storage information 31. The product storage information 31 stores the required storage quantity (column 112) in association with the product ID (column 111). There is one record (horizontal arrangement) for each type of product stored in the logistics warehouse. Note that the information included in the record is not limited to the information described above. The record may not include either the product ID or the required storage quantity, or may include other information. This is the same for FIGS. 8 to 10 described later.

[0043] The product ID (column 111) is an identifier that uniquely identifies the type of product stored in the logistics warehouse. Even for products of the same type, if it is necessary to classify the products based on the arrival date and time, expiration date, etc., a separate product ID is provided.

[0044] The required storage quantity (column 112) is the physical volume of each product that needs to be stored in the logistics warehouse. The required storage quantity may be expressed as a quantity such as the number of units or cases of the product, or as a volume such as storage volume. "CS" is an abbreviation for "box" or "cases".

[0045] (Picking Method Information) Figure 8 shows an example of picking method information 32. Picking method information 32 stores the picking area ID (field 122), storage capacity limit (field 123), processing efficiency (field 124), and the maximum number of personnel that can be employed (field 125), associated with the picking method ID (field 121). One record exists for each picking method in the logistics warehouse.

[0046] The picking method ID (column 121) is an identifier that uniquely identifies the picking method. Even with the same picking method, if the picking area 41 is divided into multiple sections and the maximum capacity per unit period differs for each divided picking area, a separate picking method ID is provided. The picking area ID (column 122) is an identifier that uniquely identifies the picking area 41. The storage capacity limit (column 123) is the capacity of goods that can be stored in each picking area 41. In terms of storage capacity, it may be expressed in terms of quantity such as the number of units of goods or the number of cases, or in terms of capacity such as storage volume.

[0047] Processing efficiency (column 124) is, as mentioned above, the amount shipped per person per hour for each picking method. "Rows / MH" is an abbreviation for "Number of shipping orders / person-hour". The maximum number of personnel that can work (column 125) is the maximum number of personnel that can work simultaneously in each picking area 41. Looking at each record of the picking method information 32, at least one of the processing efficiency and the maximum number of personnel that can work differs from other records.

[0048] If there is information that shows the upper limit of capacity per unit period for each picking method, the picking method information 32 may add that item to each record, while excluding processing efficiency and the upper limit of personnel that can be engaged from each record.

[0049] (Shipping Schedule Information) Figure 9 shows an example of shipping schedule information 33. The shipping schedule information 33 stores the shipping quantity (column 132) in association with the product ID (column 131). There is one record for each type of product that is scheduled to be shipped during the product placement period. The product ID (column 131) is the same as the product ID in Figure 7. The shipping quantity (column 132) is the future scheduled shipping quantity of the product for each unit period (day of the week).

[0050] The shipping schedule information 33 does not necessarily need to accurately store the planned future shipping volume for each product. It may store the results of predicting the shipping volume for each unit period based on past performance, or the range of shipping fluctuations expressed as the upper and lower limits of the shipping volume during the product placement period.

[0051] (Product Placement Plan) Figure 10 shows an example of a product placement plan 34. The product placement plan 34 stores the pick method ID (field 142), pick area ID (field 143), and storage quantity (field 144) associated with the product ID (field 141). There is one record for each type of product. However, if the same product is placed across multiple pick areas, multiple pick area IDs may correspond to one product ID. The product ID (field 141) is the same as the product ID in Figure 7. The pick method ID (field 142) is the same as the pick method ID in Figure 8. The pick area ID (field 143) is the same as the pick area ID in Figure 8. The storage quantity (field 144) is the quantity of products to be stored in the pick area.

[0052] The product placement plan generation unit 22 generates a product placement plan 34 that improves the overall productivity of the warehouse, taking into account fluctuations in the shipment volume of each product. For example, the product placement plan generation unit 22 assigns a highly efficient picking method to product groups with a large average shipment volume during the product placement period. Also, the product placement plan generation unit 22 assigns a highly efficient picking method to product groups with small fluctuations (variances) in shipment volume during the product placement period. As a result, the highly efficient picking method consistently utilizes its capacity to the maximum extent throughout the product placement period, thereby improving the overall productivity of the warehouse.

[0053] (Output Screen) Figure 11 shows an example of the output screen 51. The product placement plan generation unit 22 generates the output screen 51 based on the product placement plan 34. The output processing unit 23 displays the output screen 51 on the output device 13. The output screen 51 has multiple pages corresponding to tags 52 that describe the picking method. Each page displays storage location information, product ID, product name, and storage quantity in an interconnected manner. The storage location information is detailed information about each storage area (such as the location of the picking area 41).

[0054] (Processing Procedure) Figure 12 is a flowchart of the processing procedure. As a prerequisite for starting the processing procedure, the input processing unit 21 is assumed to have acquired product storage information 31, picking method information 32, and shipping schedule information 33 from other devices as needed and stored them in the auxiliary storage device 15. As a result, the product storage information 31, picking method information 32, and shipping schedule information 33 are assumed to be stored in the auxiliary storage device 15 in the state described in Figures 7, 8, and 9.

[0055] In step S201, the product placement plan generation unit 22 of the product placement planning system 1 calculates the average shipment volume of each product. Specifically, the product placement plan generation unit 22 calculates the average shipment volume of each product (102h in column 6 of Figure 6) during the product placement period (1 week) based on the scheduled shipment information 33.

[0056] In step S202, the product placement plan generation unit 22 calculates the variance of the shipment volume of each product. Specifically, the product placement plan generation unit 22 calculates the variance of the shipment volume of each product during the product placement period (Figure 6, column 102k) based on the shipment schedule information 33. Note that "variance" is an example of an indicator that shows the fluctuation in shipment volume during the product placement period. The indicator that shows the fluctuation in shipment volume may be an indicator other than variance (for example, difference = weekly maximum value - weekly minimum value).

[0057] In step S203, the product placement plan generation unit 22 generates N product groups whose variance is less than or equal to a threshold. Specifically, firstly, the product placement plan generation unit 22 generates N product groups by combining multiple types of products. N may be, for example, the number of picking methods (in Figure 6, N = 3). Secondly, the product placement plan generation unit 22 calculates the variance (Figure 6 column 102m) of the total daily shipment volume of each product belonging to the product group generated in the "first" step S203, and compares the calculated variance with a predetermined threshold.

[0058] The product arrangement plan generation unit 22 repeats the "first" and "second" processes of step S203 until it can identify N product groups such that the result of the "second" comparison in step S203 is "variance ≤ threshold".

[0059] For the sake of explanation, here we assume that, as a result of the iterative processing, the product arrangement generation unit 22 identifies product groups ADG, BEH, and CFJ, and that the following conditions hold true: "min( , , )" is a function that retrieves the smallest number among the numbers separated by commas. • Variance of product group ADG < Variance of product group BEH < Variance of product group CFJ min(Variance of product A, Variance of product D, Variance of product G) > Variance of product group ADG min(Variance of product B, Variance of product E, Variance of product H) > Variance of product group BEH min(Variance of product C, Variance of product F, Variance of product J) > Variance of product group CFJ

[0060] In step S204, the product placement plan generation unit 22 assigns picking methods to N product groups so that the picking method with higher processing efficiency is assigned in order of the largest average shipment volume and the smallest variance of shipment volume. Specifically, firstly, the product placement plan generation unit 22 generates an initial solution (details below). Here, the product placement plan generation unit 22 accepts from the user in advance the degree to which one should be prioritized over the other (priority rule) if the order of "largest average shipment volume" is different from the order of "smallest variance of shipment volume". An example of a priority rule is as follows.

[0061] (Priority Rule 1) - The product layout plan generation unit 22 further sorts a plurality of product groups whose average shipment volume corresponds to the top 1 / 3 in ascending order of the variance of the shipment volume, and assigns a picking method with higher processing efficiency in order from the top of the sorted groups. At this time, the product layout plan generation unit 22 sorts the picking methods in descending order of processing efficiency and assigns the picking methods in that order. - The product layout plan generation unit 22 further sorts a plurality of product groups whose average shipment volume corresponds to the middle 1 / 3 in ascending order of the variance of the shipment volume, and assigns the remaining picking methods among the picking methods with higher processing efficiency in order from the top of the sorted groups. - The product layout plan generation unit 22 further sorts a plurality of product groups whose average shipment volume corresponds to the bottom 1 / 3 in ascending order of the variance of the shipment volume, and assigns the remaining picking methods among the picking methods with higher processing efficiency in order from the top of the sorted groups.

[0062] (Priority Rule 2) - The product layout plan generation unit 22 sets the order in which a plurality of product groups are arranged in descending order of the average shipment volume as p (p = 1, 2, 3,...) and associates the value of p with each product group. - The product layout plan generation unit 22 sets the order in which a plurality of product groups are arranged in ascending order of the variance of the shipment volume as q (q = 1, 2, 3,...) and associates the value of q with each product group.. - The product layout plan generation unit 22 associates each product group with the value of r (r = w １ ×p + w ２ ×q). Here, w １ and w ２ are weights, and it is assumed that "w １ + w ２ = 1", "0 ≤ w １ ≤ 1" and "0 ≤ w ２ ≤ 1" hold. The user arbitrarily sets the values of w １ and w ２ . - The product layout plan generation unit 22 assigns a picking method with higher processing efficiency to the product group with a smaller associated value of r.

[0063] For the sake of explanation, let's assume that the product placement plan generation unit 22, based on priority rules, assigned automated warehouses to product group ADG, shelf transport to product group BEH, and carts to product group CFJ. The resulting association, such as "ADG: automated warehouse, BEH: shelf transport, CFJ: cart," is called the "initial solution."

[0064] Secondly, the product arrangement generation unit 22 generates M initial solutions by swapping some of the products belonging to a product group across product groups. For example, for “ADG: automated warehouse, BEH: shelf transport, CFJ: cart”, “ADH: automated warehouse, BEJ: shelf transport, CFG: cart” is another initial solution. For the sake of explanation, let's assume that M = 4, and that initial solutions W1, W2, W3, and W4 exist. Of these, initial solution W1 is the initial solution before the product swap.

[0065] Then, the product layout plan generation unit 22 generates M product layout plans 34 based on the M initial solutions and stores them in the auxiliary storage device 15. As is clear from the above, the initial solutions, the product layout plans 34, and the output screen 51 essentially show the same content. Incidentally, at this stage, the product layout plan generation unit 22 can also store the table information shown in Figure 6 in the auxiliary storage device 15 and display it on the output device 13.

[0066] In step S205, the output processing unit 23 of the product placement planning system 1 displays the output screen 51. Specifically, the output processing unit 23 displays the output screen 51 (Figure 11) on the output device 13. At this stage, the product placement plan generation unit 22 generates the output screen 51 based on the product placement plan 34.

[0067] In step S206, the product placement plan generation unit 22 receives the results of continuing picking with the initial solution for M weeks. Specifically, the product placement plan generation unit 22 applies the initial solution W1 to the logistics warehouse for the first week, the initial solution W2 for the next week, the initial solution W3 for the following week, and the initial solution W4 for the last week. During these four weeks, the product placement plan generation unit 22 receives actual values ​​such as the amount of goods shipped from the devices that manage each pick area 41.

[0068] In step S207, the product placement plan generation unit 22 calculates the productivity of M items. Specifically, the product placement plan generation unit 22 calculates the productivity of the entire logistics warehouse on a weekly basis based on the actual values ​​received in step S206.

[0069] In step S208, the product placement plan generation unit 22 finds the optimal solution that maximizes productivity. Specifically, the product placement plan generation unit 22 defines the initial solution applied in the week with the highest productivity as the "optimal solution".

[0070] In step S209, the product placement plan generation unit 22 initiates picking using the optimal solution from week M+1 onwards. Specifically, the product placement plan generation unit 22 applies the optimal solution to the logistics warehouse from week M+1 onwards. After that, the processing procedure is terminated.

[0071] (Modification: Optimization calculation model) The above is an example in which the product placement plan generation unit 22 itself performs the optimization calculation (generation of product placement plan 34, etc.). However, the product placement plan generation unit 22 may have an optimization calculation model 22b (Figure 1) as a module within itself. Furthermore, the product placement plan generation unit 22 may have the optimization calculation model 22b handle at least a part of the processing that it would normally perform in the processing procedure (Figure 12).

[0072] For example, the product placement plan generation unit 22 may have the optimization calculation model 22b handle the processing up to generating the initial solution (steps S201 to S205) (Case 1). The product placement plan generation unit 22 may have the optimization calculation model 22b handle the processing from generating the initial solution to finding the optimal solution (steps S206 to S209) (Case 2). Furthermore, the product placement plan generation unit 22 may have the optimization calculation model 22b handle all processing (steps S201 to S209) (Case 3). In Cases 1, 2, and 3, the optimization calculation model 22b is, for example, a neural network capable of machine learning and deep learning.

[0073] In Case 1, the optimization calculation model 22b takes product storage information 31, picking method information 32, and shipping schedule information 33 as input and outputs product placement plan 34 (initial solution). In Case 2, the optimization calculation model 22b takes product placement plan 34 (initial solution) as input and outputs the optimal solution. In Case 3, the optimization calculation model 22b takes product storage information 31, picking method information 32, and shipping schedule information 33 as input and outputs the optimal solution.

[0074] In either case, the product placement plan generation unit 22 uses previously used input and output data as supervised learning data to machine-learn and deep-learn the optimization calculation model 22b at any arbitrary timing, such as when the product composition changes. By utilizing the optimization calculation model 22b in this way, for example, if a new logistics warehouse is constructed to store the same products that were stored in the past, it is possible to utilize past experience.

[0075] (Summary of Optimization Calculation Model) As is clear from the above, the product placement plan generation unit 22 includes an optimization calculation model that outputs the optimal combination of a group of products and a picking method. The product placement plan generation unit 22 generates a product placement plan by assigning the picking method to the product group in order of the largest planned shipment volume during the product placement period and in order of the smallest fluctuation in the planned shipment volume, and inputs the product placement plan as the initial solution into the optimization calculation model.

[0076] Furthermore, the product placement plan generation unit generates product placement plans by assigning the picking methods to the product groups in order of decreasing capacity limit for each picking method.

[0077] (Effects of the Embodiment) The product placement planning system 1 generates a product placement plan 34 that takes into account the capacity limits of each of the multiple picking methods based on fluctuations in the shipment volume of each product. This has the effect of improving the productivity of the entire warehouse. Furthermore, the product placement planning system 1 generates the product placement plan 34 so that the workload approaches the capacity limit for the picking method with the highest processing efficiency. This makes it possible to process a larger volume of goods with fewer personnel, and the total number of workers in the warehouse is reduced. In addition, because the number of workers is reduced, the warehouse as a whole can handle a larger volume of goods, and an improvement in throughput can be expected.

[0078] While several embodiments of the present invention have been described, these embodiments are presented as examples only and do not limit the scope of the invention. These novel embodiments can be implemented in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims of the invention and its equivalents.

[0079] 1 Product Placement Planning System 11 Central Control Unit 12 Input Device 13 Output Device 14 Main Memory 15 Auxiliary Memory 16 Communication Device 21 Input Processing Unit 22 Product Placement Plan Generation Unit 22b Optimization Calculation Model 23 Output Processing Unit 31 Product Storage Information 32 Picking Method Information 33 Shipping Schedule Information 34 Product Placement Plan 40 Logistics Warehouse 41 Picking Area 45 Automated Machinery 46 Product Shelves 47 Cart

Claims

1. A product placement planning system characterized by comprising: a product placement plan generation unit that generates a product placement plan for a product placement period longer than the unit period by referring to: shipment schedule information which stores the planned shipment quantities for each unit period of multiple products handled in a logistics warehouse; and pick method information which stores at least one of the processing efficiency and the maximum number of personnel that can be engaged for each of multiple pick methods implemented in the logistics warehouse; and assigning a pick method from the pick method information to a product based on fluctuations in the planned shipment quantities for each unit period of the product in the shipment schedule information.

2. The product placement planning system according to claim 1, further comprising a storage device that stores the shipment schedule information and the picking method information.

3. The product placement plan system according to claim 1, characterized in that the product placement plan generation unit generates the product placement plan so as not to exceed the capacity limit of the picking method, which is determined based on the processing efficiency and the maximum number of personnel that can be engaged.

4. The product placement plan system according to claim 1, characterized in that the product placement plan generation unit generates the product group such that the fluctuation in the planned shipment volume of the product group is smaller than the fluctuation in the planned shipment volume of any of the products constituting the product group, and generates the product placement plan by assigning the picking method to the product group.

5. The product placement plan generation unit comprises an optimization calculation model that outputs the optimal combination of a product group, which is a group of products, and the picking method, and generates a product placement plan by assigning the picking method to the product group in order of the largest planned shipment volume for the product placement period and in order of the smallest fluctuation in the planned shipment volume, and inputs the product placement plan as an initial solution to the optimization calculation model, the product placement plan system according to claim 1.

6. The product placement plan system according to claim 5, characterized in that the product placement plan generation unit generates the product placement plan by assigning the picking methods to the product groups in order of their processing efficiency.

7. A product placement planning method characterized in that the product placement plan generation unit of a product placement planning system refers to shipment schedule information which stores the planned shipment quantities for each unit period of multiple products handled in a logistics warehouse, and pick method information which stores at least one of the processing efficiency and the maximum number of personnel that can be engaged for each of multiple pick methods implemented in the logistics warehouse, and generates a product placement plan for a product placement period longer than the unit period by assigning a pick method from the pick method information to the product based on the fluctuation of the planned shipment quantities for each unit period of the product in the shipment schedule information.

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