Logistics operation optimization device and logistics operation optimization method

Abstract

To optimize logistics work of a shipping process in a warehouse, to improve throughput in all the steps of the shipping process, and to shorten working time in the shipping process.SOLUTION: A logistics work optimization device, relative to each individual work step, calculates a degree of deviation of an order of an article of an overall optimal plan indicated by an overall optimal plan list and an order of an article in an individual work step, estimated work time from the overall optimum plan and error of the estimated work time, and the number of combinations of a default order from the overall optimal plan shown by the overall optimal plan list in the past, evaluates it by an evaluation formula of a weighted linear sum composed of the degree of deviation, the estimated work time and error of the estimated work time from the overall optimal plan, and the number of combinations of a default order, and prepares an overall optimum plan list of a best overall optimum plan from an overall optimum proposal shown by an overall optimum proposal list.SELECTED DRAWING: Figure 12

Description

[Technical field]

[0001] The present invention relates to a logistics operation optimization device and a logistics operation optimization method, and more particularly to a logistics operation optimization device and a logistics operation optimization method suitable for optimizing logistics operations in the shipping process in a warehouse, improving throughput in all steps of the shipping process, and shortening the operation time in the shipping process. [Background technology]

[0002] Today, the explosive growth in logistics volume has created a demand for more efficient logistics operations. Operations within logistics warehouses are becoming increasingly systematized, and material handling equipment (so-called "material handling equipment") is often being introduced to reduce the labor required for loading and unloading operations and automate the process. Furthermore, various technologies are being developed to shorten the time from order to shipment, which directly leads to customer satisfaction.

[0003] In sequential workflows such as the shipping process of a logistics warehouse, items to be handled flow in a certain order from the upstream of the workflow. Assuming a scenario in which the order of items is determined at the beginning of the workflow and continues to flow in the same order until the end of the workflow, the order of items that will make the workflow more efficient is output. For such warehouse logistics operations, technology has been proposed to improve the efficiency using information processing devices.

[0004] Patent Document 1 describes a product pickup system that determines the optimal loading order for packing products for each shipping destination based on product storage shelf position information, packing priority based on delivery priority, volume information for each product, and packing box volume information and inventory information, and then determines the picking order based on that order, thereby making packing work more efficient.

[0005] Patent Document 2 describes a product picking facility that arranges work facilities in a warehouse for each work area so that material handling equipment can move and work efficiently.

[0006] Patent Document 3 describes an goods shipment management system that formulates the work time for each process in goods shipment, optimizes the size of the cargo tying, automatically optimizes the shipping process, and minimizes the shipping lead time. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] JP 2005-324945 A [Patent Document 2] JP 2001-253515 A [Patent Document 3] JP 2002-154615 A Summary of the Invention [Problem to be solved by the invention]

[0008] In Patent Document 1, the location information of the product storage shelf, the packing priority according to the delivery priority, and each merchandise Based on the volume information of the products, the volume information of the packing boxes, and the inventory information, the optimal loading order for packing the products for each shipping destination is determined, and the picking order is determined based on that order, thereby improving the efficiency of the packing work. However, if the product picking order is determined by considering only the efficiency of the packing work using the technology described in Patent Document 1, the work efficiency of processes other than packing may decrease, and the work time of the entire shipping process may not be reduced.

[0009] In Patent Document 2, the work facilities in the warehouse are arranged for each work area so that the material handling equipment can move and work efficiently. However, in warehouses currently in operation, it may be costly and time-consuming to apply this technology. The technology described in Patent Document 2 reduces the work time of the shipping process by using currently operated systems and equipment such as material handling equipment. Shortening However, if the aim is to achieve this, it may be difficult to apply.

[0010] In general, warehouse logisticsIn order to perform multi-objective optimization in business operations, it is necessary to understand the constraints and objective functions of each process. However, in many cases, systems and material handling equipment from different vendors are installed in a single logistics warehouse. In this case, one vendor's material handling equipment cannot share information with other vendors' material handling equipment, and the constraints and objective functions of the business operations are unknown, making it impossible to perform multi-objective optimization.

[0011] Furthermore, even if a work plan is created to shorten the shipping process time as in Patent Documents 1 and 3 and presented to the site, it may not be possible to execute the plan as planned for some reason, in which case the actual work hours may end up being longer than the work hours expected when the plan was created.

[0012] An object of the present invention is to provide a logistics operation optimization device and a logistics operation optimization method suitable for optimizing logistics operations in the shipping process in a warehouse, improving throughput in all steps of the shipping process, and shortening the work time in the shipping process. [Means for solving the problem]

[0013] The logistics operation optimization device of the present invention is preferably configured as a logistics operation optimization device that distributes items sequentially and optimizes the order of items for performing operations related to the items in each operation process, and includes a deviation calculation unit that calculates the degree of deviation between the order of items shown in the overall optimum plan list and the order of items in each operation process based on an overall optimum plan list indicating an overall optimum plan in which the optimal order of items is arranged throughout the entire logistics operation and an optimal order list for each operation process in which the optimal order of items is arranged in each operation process; The system has a work time estimation unit that calculates an estimated work time from the overall optimal plan, an overall optimal plan evaluation unit that calculates an evaluation value of the overall optimal plan indicated by the overall optimal plan list using an evaluation formula based on the degree of deviation between the order of items indicated in the overall optimal plan list calculated by the deviation calculation unit and the order of items in each work process and the estimated work time calculated by the work time estimation unit, and an overall optimal plan determination unit that creates an overall optimal plan list that indicates an overall optimal plan that arranges the optimal order of items throughout the entire logistics work, based on the evaluation value of the overall optimal plan calculated by the overall optimal plan evaluation unit. Effect of the Invention

[0014] According to the present invention, it is possible to provide a logistics operation optimization device and a logistics operation optimization method that are suitable for optimizing logistics operations in the shipping process in a warehouse, improving throughput in all steps of the shipping process, and shortening the work time in the shipping process. [Brief description of the drawings]

[0015] [Figure 1] FIG. 1 is a diagram illustrating a process in a logistics warehouse operation. [Diagram 2] 1 is a functional configuration diagram of a logistics operation optimization device according to a first embodiment. [Diagram 3] FIG. 1 is a hardware / software configuration diagram of a logistics operation optimization device according to a first embodiment. [Figure 4] FIG. 13 is a diagram showing an example of a global optimum plan list. [Diagram 5] FIG. 13 is a diagram showing an example of a deviation degree table for each process. [Figure 6] FIG. 13 is a diagram illustrating an example of a default information list table. [Figure 7] FIG. 13 illustrates an example of a default matching table. [Figure 8] FIG. 11 is a diagram illustrating an example of an estimated work time table. [Figure 9] FIG. 13 is a diagram illustrating an example of an overall optimum plan evaluation value table. [Figure 10] FIG. 13 is a diagram showing an example of a field work history table. [Figure 11] FIG. 11 is a diagram showing an example of a weight data table. [Figure 12] FIG. 2 is a diagram showing the process and data flow of the logistics operation optimization device in the first embodiment. [Figure 13] FIG. 13 is a diagram illustrating a comparison between the overall optimal plan and the order of items indicated by the on-site work history in each process. [Figure 14] FIG. 11 is a functional configuration diagram of a logistics operation optimization device according to a second embodiment. [Figure 15] FIG. 11 is a diagram showing the process and data flow of a logistics operation optimization device in the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] Hereinafter, each embodiment of the present invention will be described with reference to FIGS.

[0017] [Embodiment 1] A first embodiment of the present invention will be described below with reference to FIGS. 1 to 13. FIG.

[0018] This embodiment relates to a technology for optimizing work in the shipping process of logistics operations by creating a plan (such an item order plan will be referred to as an "overall optimization plan" hereinafter) that indicates the item handling order that will improve overall throughput, taking into account the work content of each process, assuming a situation in which the item handling order decided at the beginning of the shipping process, which consists of multiple processes from picking to shipping, is maintained in the same order until the end, in response to a shipping instruction (order) from a logistics warehouse.

[0019] First, the process of logistics warehouse operations will be explained using Figure 1. As shown in Figure 1, logistics warehouse operations can be broadly divided into the storage process, storage, and shipping process. For example, the storage process includes the processes of warehousing, receiving inspection, and storage work, while storage includes the storage process, and the shipping process includes the processes of picking, distribution processing, sorting, inspection, packaging, and shipping.

[0020] In logistics warehouse operations, many different types of products are transported and each process is carried out sequentially, and ultimately, there is a demand to shorten the total time from receiving the products to final shipping.

[0021] However, there is a problem in that even if efforts are made to reduce the time spent on individual processes, it is not necessarily possible to reduce the time spent on all processes from receiving the product to final shipping.

[0022] For example, in the distribution processing (e.g., pricing) process, it is more efficient to have packages of the same price in succession, while in the sorting process, it is more efficient to have packages with the same delivery destination in succession.

[0023] Therefore, if the order of products in distribution is set to the optimal price order, there is a possibility that the sorting process will result in an inappropriate order, i.e., there will be a delay in the collection of packages for each delivery destination, which may delay the start of the next process, inspection, and therefore the overall shipping time.

[0024] On the other hand, if the order of products in the distribution chain is set to the optimal sorting order, it may become an inappropriate order for pricing work. but This can slow down the flow of goods from pricing, lengthen the time required for inspection work (creating gaps), and cause delays in overall shipping times.

[0025] In this embodiment, the workability and efficiency of the product order of distribution at each process is improved. differentTaking this into consideration, the present invention relates to a technique for finding a balance in the order of products in distribution, which reduces bottlenecks in the flow of goods, prevents the work time of each process from becoming extremely long, and shortens the time required to complete the entire work. In this case, the actual site To It is also necessary to take into account the actual working conditions at the facility.

[0026] Next, the configuration of the logistics operation optimization device according to the first embodiment will be described with reference to FIG. 2 and FIG.

[0027] As shown in FIG. 2 , the logistics operation optimization device 100 comprises an overall optimal plan creation unit 101, a deviation calculation unit 102, a default sequence search unit 103, a work time estimation unit 104, an overall optimal plan evaluation unit 105, a calculation continuation judgment unit 106, an overall optimal plan determination unit 107, an overall optimal plan execution unit 108, an overall optimal plan display unit 109, a default sequence data creation unit 110, an evaluation index weight learning unit 111, and a memory unit 120.

[0028] The global optimum plan creation unit 101 is a functional unit that creates an optimal plan for the order of items in the logistics operations of the warehouse (hereinafter, also referred to as the "global optimum plan"). The deviation calculation unit 102 is a functional unit that calculates the degree of deviation between the global optimum plan and the optimal order of each process. Details of the deviation will be described later. The default order search unit 103 is a functional unit that searches whether or not the global optimum plan includes an item order pattern in which work is not performed as planned, based on work information at the site. The work time estimation unit 104 is a functional unit that estimates the work time in each process. The global optimum plan evaluation unit 105 is a functional unit that evaluates the created global optimum plan based on an evaluation index. Details of the evaluation index will be described later. The calculation continuation judgment unit 106 is a functional unit that judges whether or not to continue the calculation of the loop that obtains the global optimum plan from the global optimum plan. The global optimum plan determination unit 107 is a functional unit that determines the global optimum plan from the global optimum plan based on the calculated information. The global optimum plan execution unit 108 is a functional unit that executes the global optimum plan of the warehouse. logisticsThis is a functional unit that executes an overall optimal plan determined in a task. For example, it is a function that transmits commands and data to a logistics robot. The overall optimal plan display unit 109 is a functional unit that displays the calculated overall optimal plan. The default order data creation unit 110 is a functional unit that creates data on the order of items that have been defaulted on site in accordance with the overall optimal plan. The evaluation index weight learning unit 111 is a functional unit that learns the weights of evaluation indexes based on a certain learning model. The details of the weights of evaluation indexes will be described later. The memory unit 120 is a functional unit that stores work data, tables, and lists used in the logistics operation optimization device 100 of this embodiment.

[0029] The logistics operation optimization device 100 of this embodiment stores an optimal order list 201 for process i (i=1, 2, 3, ...), an overall optimal plan list 202, a deviation degree table for each process 203, a default matching table 204, an estimated work time table 205, an overall optimal plan evaluation value table 206, an overall optimal plan list 207, an on-site work history table 208, a default information list table 209, and a weighting data table 210. The details of each list and table will be described later.

[0030] Next, the hardware and software configuration of the logistics operation optimization device 100 will be described with reference to FIG. The hardware configuration of the logistics operation optimization device 100 is realized by, for example, a general information processing device such as a personal computer shown in FIG.

[0031] The logistics operation optimization device 100 includes a CPU (Central Processing Unit) 302, a main memory device 304, a network I / F (Interface) 306, a display I / F 308, an input / output I / F 310, and an auxiliary memory I / F 312, all of which are connected via a bus.

[0032] The CPU 302 controls each unit of the logistics operation optimization device 100, and loads necessary programs into the main memory device 304 and executes them.

[0033] The main storage device 304 is typically configured as a volatile memory such as a RAM, and stores the programs executed by the CPU 302 and data referenced by the CPU 302.

[0034] The network I / F 306 is an interface for connecting to the network 5 .

[0035] The display I / F 308 is an interface for connecting a display device 320 such as an LCD (Liquid Crystal Display).

[0036] The input / output I / F 310 is an interface for connecting input / output devices. In the example of Fig. 3, a keyboard 330 and a mouse 332 which is a pointing device are connected.

[0037] The auxiliary storage I / F 312 is an interface for connecting an auxiliary storage device such as a hard disk drive (HDD) 350 or a solid state drive (SSD).

[0038] The HDD 350 has a large storage capacity and stores programs for executing the present embodiment. The logistics operation optimization device 100 includes a total optimum plan creation program 361, a deviation calculation program 362, a default order program, and a general optimum plan creation program 363. search A program 363, a work time calculation program 364, an overall optimal plan evaluation program 365, a calculation continuation judgment program 366, an overall optimal plan determination program 367, an overall optimal plan execution program 368, an overall optimal plan display program 369, a default sequence calculation program 370, and an evaluation index weight learning program 371 are installed.

[0039] Overall optimum plan creation program 361, deviation calculation program 362, default order searchProgram 363, work time calculation program 364, overall optimal plan evaluation program 365, calculation continuation judgment program 366, overall optimal plan determination program 367, overall optimal plan execution program 368, overall optimal plan display program 369, default sequence calculation program 370, and evaluation index weight learning program 371 are programs that realize the functions of the overall optimal plan creation unit 101, the deviation calculation unit 102, the default sequence search unit 103, the work time estimation unit 104, the overall optimal plan evaluation unit 105, the calculation continuation judgment unit 106, the overall optimal plan determination unit 107, the overall optimal plan execution unit 108, the overall optimal plan display unit 109, the default sequence data creation unit 110, and the evaluation index weight learning unit 111, respectively.

[0040] In addition, although not shown, HDD 350 stores an optimal order list 201 for process i (i=1, 2, 3, ...), an overall optimal plan list 202, a deviation degree table for each process 203, a non-performance matching table 204, an estimated work time table 205, an overall optimal plan evaluation value table 206, an overall optimal plan list 207, an on-site work history table 208, a non-performance information list table 209, and a weighting data table 210.

[0041] Next, the data structure used by the logistics operation optimization device of the first embodiment will be described with reference to FIGS.

[0042] The global optimum plan list 202 is a list of global optimum plans presented by the logistics operation optimization device 100 as optimal plans for the order of items in the shipping process, and has a structure in which item IDs (itemA, itemB, ...) of items to be shipped in the shipping process are arranged sequentially as shown in Fig. 4. Although not shown, each global optimum plan shown in the global optimum plan list 202 is assigned an global optimum plan ID that uniquely identifies it.

[0043] The process i optimum order list 201 is a list of the order of items that is estimated to optimize the work for each process calculated for each process i, and has the same structure as the overall optimum plan list 202.

[0044] The deviation degree for each process table 203 is a table for holding the degree of deviation from the global optimum plan for each process i optimal order list, and as shown in FIG. 5, has fields for a global optimum plan 203a, a process 203b, and deviation degree 203c.

[0045] The global optimum plan 203a stores an ID indicating the global optimum plan. The process 203b stores an ID of each process. The deviation 203c stores the degree of deviation between the global optimum plan indicated by the global optimum plan 203a and the process i optimal order list indicated by the process 203b. The method of calculating the deviation will be described in detail later.

[0046] The non-compliance information list table 209 is a table that holds information about cases in which the order of items in the on-site work history differs to a certain extent compared to the overall optimal plan that has been presented up to now, and as shown in Figure 6, it consists of fields for case ID 209a, overall plan optimal order 209b, and on-site work order 209c.

[0047] The case ID 209a stores an ID that uniquely identifies a case. The overall plan optimal order 209b stores the order of items that were not performed in the overall optimal plan in accordance with the on-site work history. The on-site work order 209c stores the order of items that are not performed in the overall optimal plan in accordance with the on-site work history and that are indicated by the actual on-site work history.

[0048] For example, in the case with case ID 209a “0010”, when the item order in the global optimum plan is itemB→itemC, there is an 80 / 100 chance that the work will be carried out in the item order of itemC→itemB in the on-site work history.

[0049] The non-fulfillment matching table 204 is a table that holds the scores at which mismatches occurred that are recorded in the non-fulfillment information list table 209 in comparison with the global optimal plan, and as shown in Figure 7, it consists of fields for the global optimal plan 204a and the non-fulfillment matching points 204b.

[0050] The global optimum plan 204a stores an ID indicating the global optimum plan. The non-execution matching point 204b stores a score at which a mismatch occurred in the global optimum plan indicated by the global optimum plan 204a, which is recorded in the non-execution information list table 209. The larger this score, the greater the number of mismatches in the field work.

[0051] The estimated work time table 205 is a table that holds information about the estimated work time in the global optimum plan, and as shown in FIG. 8, it has fields of global optimum plan 205a, estimated work time 205b, and error 205c. The global optimum plan 205a stores an ID indicating the global optimum plan. The estimated work time 205b stores the information about the estimated work time in the global optimum plan by the work time estimating unit 104. show The estimated work time for each global optimal plan is stored. To be stored are the upper and lower limits of a confidence interval (e.g., 90%) for statistically estimating the estimated work time indicated by the work time estimation unit 104. For example, in the example of Fig. 8, when the global optimum plan 205a is "t_opt_001", the estimated work time is "10.8h", and the upper limit of the 90% confidence interval is "+0.5" and the lower limit is "-0.4".

[0052] The global optimum plan evaluation value table 206 is a table that holds information about values ​​related to the evaluation indexes, and as shown in FIG. 9, it includes a global optimum plan 206a, an f field 206b, a W1 field 206c, an A field 206d, and a W 21 Field 206e, B1 Field 206f, W 22 Field 206g, B2 Field 206h, W 23 The global optimum plan 206a stores an ID indicating the global optimum plan for which the evaluation index is to be calculated. 21 Field 206e, B1 Field 206f, W 22Field 206g, B2 Field 206h, W 23 The B3 field 206j, W3 field 206k, C field 206l, W4 field 206m, and D field 206n store the values ​​of the corresponding evaluation indices. Details of the evaluation indices will be explained later.

[0053] The field work history table 208 is a table that holds history data on the work site in the warehouse, and as shown in FIG. 10, has fields for worker ID 208a, process 208b, handled item 208c, start time 208d, end time 208e, and work location 208f. The worker ID 208a stores an ID that uniquely identifies the worker. The process 208b stores an ID that uniquely identifies the process of this work. The handled item 208c stores the ID of the item handled in the work of this process. The start time 208d stores the time when the work started in the format yyyymmddmm. The end time 208e stores the time when the work ended in the format yyyymmddmm. 。 The work location 208f stores a name indicating the location where the work was performed or an ID that uniquely identifies the location where the work was performed.

[0054] The weight data table 210 is a table that holds weight data for each step in the evaluation index. As shown in FIG. 11, the weight data table 210 includes a W1 field 210a, a W 21 Field 210b, W 22 Field 210c, W 23 The W1 field 210a, W2 field 210b, W3 field 210c, and W4 field 210d. 21 Field 210b, W 22 Field 210c, W 23 Field 210d, W3 field 210e, W4 field 210f are the weights in the evaluation index, W1, W 21 , W 22 , W 23 , W3, and W4 are stored. Details of the weights in the evaluation index will be explained later.

[0055] Next, the processing of the logistics operation optimization device in the first embodiment will be described with reference to FIG. 12 and FIG.

[0056] The following description will be based on the flow of FIG. 12 and will refer to the figures previously described as appropriate. In this embodiment, an example will be described in which there are three shipping processes, designated as process 1, process 2, and process 3, but the number of processes in the logistics operations in a warehouse may be arbitrary.

[0057] First, the overall optimum plan creation unit 101 of the logistics operation optimization device 100 performs the process i (i=1, 2, 3 ,… ) Based on the optimum order list 201, one or more global optimum plan lists 202 shown in FIG. 4 are created.

[0058] As an example of a method for creating this global optimum plan list 202, there is a method in which the process i optimum order list 201 created by each individual process system is used as initial parent data, and thereafter, a global optimum plan is created using a genetic algorithm (GA) while inheriting the characteristics of the global optimum plan list with a high evaluation in the evaluation index (described later).In addition, a method of creating the global optimum plan completely randomly may be used, although the convergence to the global optimum plan is slow.

[0059] Here, the method of creating the process i optimum order list 201 is, for example, if the process is attaching price tags to items, the optimum order is the order in which items with the same price are consecutively placed, and as another example, if the process is packing items according to their shipping destination, the optimum order is the order in which items with the same shipping destination are consecutively placed.

[0060] Next, the deviation calculation unit 102 calculates the deviation of the item order between the optimal order of one process and one global optimal plan based on the global optimal plan list 202 and the process i optimal order list 201, and stores it in the process-specific deviation table 203 shown in Figure 5.

[0061] The degree of discrepancy D between the item order in the process i optimal order list 201 and the item order in the global optimal plan list 202 in this embodiment is expressed by the following (Equation 1).

[0062] The Spearman rank correlation coefficient takes values ​​from −1 to +1, but the deviation in this embodiment is calculated by processing the value range of the Spearman rank correlation coefficient so that it takes values ​​from 0 to +1, and then taking the reciprocal of that value.

[0063]

number

[0064] Here, S is the Spearman rank correlation coefficient, N is the number of items involved in the warehouse operation (equal to the number of entries in the lists indicated by the process i optimal order list 201 and the overall optimal plan list 202), and D j is the difference in rank between the process i optimal order list 201 for each item and the global optimal plan list 202, and the numerator of (Equation 1) is the sum of squares of the difference in rank across all items.

[0065] Next, the default sequence search unit 103 searches whether the overall optimal plan contains any item sequence patterns that are not performed as planned at the site, based on the information on the item sequence patterns that are not performed as planned shown in the default information list table 209 shown in Figure 6 and the overall optimal plan list 202, and calculates a score for the degree to which the item sequence patterns match with the item sequence patterns that are not performed as planned, calculates a default matching score for each overall optimal plan, and stores the score in the default matching table 204 shown in Figure 7.

[0066] Next, the operation time estimation unit 104 calculates an operation estimated time for the item sequence indicated by the overall optimum plan list 202, and stores it in the estimated operation time table 205 shown in FIG. 8 together with a possible error. The operation time estimation unit 104 may, for example, statistically calculate the operation time of logistics operations in a warehouse using a deep learning method, or may calculate the operation time by accumulating individual operation times. In addition, Japanese Patent Application No. 2020-177111 is presented as a reference document for a technology for calculating an estimated operation time.

[0067] Next, the global optimum plan evaluation unit 105 evaluates the global optimum plans shown in the global optimum plan list 202 using an evaluation index based on the process deviation table 203 shown in Fig. 5, the default matching table shown in Fig. 7, the estimated work time table 205 shown in Fig. 8, and the weight data table 210 shown in Fig. 11, and based on the evaluation formula of the following (Formula 2).The smaller the global optimum plan's f shown in (Formula 2), the better the plan (the shorter the total work time) is evaluated to be.

[0068]

number

[0069] Here, the following definitions are made: A: Estimated work time in the estimated work time table in Figure 8 205b Value of Bi: the value of deviation 203c in the process deviation table 203 in FIG. 5, where the subscript i exists for each individual process (in this embodiment, From 1 3). C: The value of the default matching point 204b in the default matching table 204 of FIG. D: The upper and lower limit error ranges indicated by the error 205c in the estimated work time table 205 in FIG. 8 11. Furthermore, wj (in this embodiment, j=1, 21, 22, 23, 3, 4) is a weighting coefficient for each non-negative term, and becomes a value in the weighting data table 210 of FIG.

[0070] Then, the global optimum plan evaluation unit 105 stores the parameters corresponding to the value of the evaluation formula (Formula 2) in the corresponding fields of the global optimum plan evaluation value table 206 in FIG.

[0071] Next, the calculation continuation judgment unit 106 judges whether or not a threshold value set by the user for the time until creation of the overall optimal plan, the number of loops, etc. has been reached. If the threshold value has not been reached, the process returns to the overall optimal plan creation unit 101, and if the threshold value has been reached, the process proceeds to the overall optimal plan determination unit 107.

[0072] Next, the global optimum plan determination unit 107 determines the f field based on the global optimum plan evaluation value table 206 in FIG. 206b The global optimum plan indicated by the record with the smallest f value stored in is determined as the global optimum plan for the order of the items, and is stored in the global optimum plan list 207.

[0073] Next, the total optimum plan execution unit 108 issues an instruction to, for example, a robot or the like regarding the order of handling items in order to execute the total optimum plan shown in the total optimum plan list 207 at the site. Handheld terminal The handling order of the goods shown in the total optimum plan list 207 is displayed on the terminal display of the terminal or the like.

[0074] Next, based on the overall optimal plan shown in the overall optimal plan list 207 and the field work history table 208, the default sequence data creation unit 110 calculates the rate at which an item sequence pattern in the overall optimal plan is likely to be replaced by a different item sequence pattern.

[0075] The overall optimal plan and the item sequence indicated by the on-site work history in each process are shown in comparison in Fig. 13. For example, in the example shown in Fig. 13, the overall optimal plan indicates that the item sequence changes as follows: "itemA → itemB → itemC → itemG → ...", while the item sequence indicated by the on-site work history for process 1 is "itemA → itemC → itemB → itemG → ...".

[0076] Item sequence for global optimal planning So, Although itemB and itemC are lined up consecutively, they are swapped as itemC and itemB in the on-site work history of process 1. If this pattern of swapping occurs at a predetermined rate, for example, 80% or more for the occurrence of this item sequence, then the value of overall plan optimal sequence 209b is recorded as "itemB→itemC" and the value of on-site work sequence 209c is recorded as "itemC→itemB," as in the case where case ID 209a in non-performance information list table 209 in FIG. 6 is "0010."

[0077] The evaluation index weight learning unit 111 calculates a weight w that reduces the evaluation value of f in (Equation 2) when evaluating the executed global optimal plan based on the global optimal plan shown in the global optimal plan list 207, the deviation obtained from the item order for each process obtained from the field work history table 208, and the non-performance matching point. j (j=21, 22, 23, 3) is learned and stored in the weight data table 210 shown in FIG.

[0078] In addition, the weight w when evaluating the executed global optimal plan is calculated based on the global optimal plan shown in the global optimal plan list 207, the estimated work time in the estimated work time table 205, and the upper and lower limit error ranges. j (j=1,4) and the weight w that reduces the evaluation value of f in (Eq. 2) j is learned and stored in the weight data table 210 shown in FIG.

[0079] However, during learning, the weights w j A constraint is set so that the sum of (j=1, 21, 22, 23, 3, 4) is a constant value E.

[0080]

number

[0081] As described above, according to the first embodiment of the present invention, the throughput is improved in all processes in a logistics warehouse, an overall optimal plan can be created even if the system for each process is a black box, and an item sequence plan that is easy to execute on site can be created. Therefore, it is expected that logistics work in the warehouse will be carried out in the predicted optimal work time in accordance with the overall optimal plan.

[0082] [Embodiment 2] A second embodiment of the present invention will be described below with reference to FIGS.

[0083] In the first embodiment, a logistics operation optimization device has been described that optimizes the order of items in the work at a logistics warehouse, thereby optimizing the logistics operation and shortening the time from shipping instructions (order receipt) to shipping. In the first embodiment, the number of times that a discrepancy (non-performance) occurs between the overall optimum plan and each process is counted and incorporated into the evaluation formula (term C in (Formula 2)). This is because in the shipping process at a warehouse, the order of handling items may be changed at the discretion of the worker.

[0084] However, in a factory production line, it is usually impossible for an operator to arbitrarily change the order in which parts or processed products are sent.

[0085] Based on this premise, the present embodiment will explain a logistics operation optimization device 100 for a factory production line, focusing on the differences from the first embodiment.

[0086] First, the configuration of a logistics operation optimization device according to the second embodiment will be described with reference to FIG. The functional configuration of the logistics operation optimization device 100 of this embodiment is almost the same as the functional configuration of the logistics operation optimization device 100 of embodiment 1 shown in Figure 2, but as functional units, the default order search unit 103 and the default order data creation unit 110 are no longer present, and as data, the default matching table 204 and the default information list table 209 are no longer present.

[0087] Next, the process and data flow of the logistics operation optimization device in the second embodiment will be described with reference to FIG. The processing of the logistics operation optimization device in the second embodiment is almost the same as the processing and data flow of the logistics operation optimization device in the first embodiment shown in FIG. 12, but the default order search unit 103 and default order data creation unit 110 are eliminated, and the default matching table 204 and default information list table 209 are eliminated as data.

[0088] Next, the evaluation of the global optimum plan by the global optimum plan evaluation unit 105 will be described. The global optimum plan evaluation unit 105 evaluates the global optimum plans shown in the global optimum plan list 202 using an evaluation index based on the process deviation table 203 shown in Fig. 5, the default matching table shown in Fig. 7, the estimated work time table 205 shown in Fig. 8, and the weight data table 210 shown in Fig. 11, and based on the evaluation formula of the following (Formula 4). The smaller the f shown in (Formula 4), the better the plan is evaluated to be (the shorter the total work time). (Formula 4) is an evaluation formula that does not include the term C, compared to (Formula 1) in the first embodiment.

[0089]

number

[0090] A,B i ,D,w j The definition of is the same as that of (Formula 1) in the first embodiment.

[0091] According to this embodiment, it is possible to provide a logistics operation optimization device that is based on basically the same idea as the method for optimizing the order of items used by the logistics operation optimization device of embodiment 1, and that is particularly specialized for the handling of items on factory production lines.

[0092] Therefore, according to this embodiment, it is expected that the throughput of the entire factory production line will be improved, and even if the work content of each process cannot be coordinated, an overall optimal plan will be created, and the working time on the factory production line will be reduced in accordance with the overall optimal plan. [Explanation of symbols]

[0093] 100...Logistics operation optimization device, overall optimal plan creation unit 101..., 102...Deviation degree calculation unit, 103...Default sequence search unit, 104...Work time estimation unit, 105...Overall optimal plan evaluation unit, 106...Calculation continuation judgment unit, 107...Overall optimal plan determination unit, 108...Overall optimal plan execution unit, 109...Overall optimal plan display unit, 110...Default sequence data creation unit, 111...Evaluation index weight learning unit, 120...Memory unit, 201... process i optimal order list, 202... overall optimal plan list, 203... deviation degree table for each process, 204... default matching table, 205... estimated work time table, 206... overall optimal plan evaluation value table, 207... overall optimal plan list, 208... site work history table, 209... default information list table, 210... weight data table

Claims

1. A logistics operation optimization device that optimizes an order of items for performing operations related to the items in each operation process by sequentially distributing the items, a deviation calculation unit that calculates, for each individual work process, a deviation between the order of items shown in the overall optimum plan list and the order of items in the individual work process, based on an overall optimum plan list showing an overall optimum plan in which the optimum order of items is arranged throughout the entire logistics work and an optimal order list for each process in which the optimum order of items is arranged; an operation time estimation unit for calculating an estimated operation time from the overall optimal plan indicated in the overall optimal plan list; an overall optimal plan evaluation unit that calculates an evaluation value of the overall optimal plan indicated by the overall optimal plan list using an evaluation formula based on a degree of deviation between the order of items indicated by the overall optimal plan list calculated by the deviation degree calculation unit and the order of items in each work process, and on the estimated work time calculated by the work time estimation unit; and an overall optimal plan determination unit that determines an overall optimal plan including an evaluation value that satisfies a predetermined condition from among evaluation values ​​of the multiple overall optimal plans obtained by the overall optimal plan evaluation unit, and creates an overall optimal plan list that indicates an overall optimal plan in which an optimal order of items throughout the entire logistics operation is arranged for the determined overall optimal plan.

2. Further, a default order search unit is provided which compares the order of items indicated in the past overall optimum plan list with the order of items indicated in the work history, searches for order combinations that are a default for the overall optimum plan indicated in the past overall optimum plan list based on a default information list table storing combinations of order of items that differ at a certain rate, and determines the number of order combinations that are a default; 2. The logistics operation optimization device according to claim 1, wherein the global optimal plan evaluation unit determines an evaluation value of the global optimal plan using an evaluation formula based on the number of combinations of orders that result in default determined by the default order search unit.

3. The task time estimation unit calculates an error in the estimated task time, 2. The logistics operation optimization device according to claim 1, wherein the global optimum plan evaluation unit calculates an evaluation value of the global optimum plan using an evaluation formula based on an error in the estimated operation time calculated by the operation time estimation unit.

4. 2. A logistics operation optimization device according to claim 1, wherein an evaluation formula by which the global optimum plan evaluation unit calculates an evaluation value is a weighted linear sum of the degree of discrepancy between the order of items in the global optimum plan list and the order of items in each individual work process, and an estimated work time.

5. 3. A logistics operation optimization device according to claim 2, wherein an evaluation formula by which the global optimum plan evaluation unit calculates an evaluation value is a weighted linear sum of the degree of deviation between the order of items in the global optimum plan list and the order of items in each work process, the estimated work time, and the number of combinations of orders that will be defaulted.

6. 4. A logistics operation optimization device according to claim 3, wherein an evaluation formula by which the global optimum plan evaluation unit calculates an evaluation value is a weighted linear sum that includes a term relating to the degree of discrepancy between the order of items in the global optimum plan list and the order of items in each individual work process, an estimated work time, and an error in the estimated work time.

7. The logistics operation optimization device according to claim 2, further comprising a default order reference unit that creates a default information list table based on an overall optimal plan list determined by the overall optimal plan determination unit and an on-site work history table that stores on-site work history.

8. 5. A logistics operation optimization device according to claim 4, further comprising an evaluation index weight learning unit that learns a weight of the deviation degree term based on an overall optimal plan list determined by the overall optimal plan determination unit and an on-site work history table that stores on-site work history.

9. A logistics operation optimization method using a logistics operation optimization device that optimizes an order of items for performing operations related to the items in each operation process by sequentially distributing the items, comprising: a step in which the logistics operation optimization device calculates, for each individual operation process, a degree of deviation between the order of items in the overall optimal plan shown in the overall optimal plan list and the order of items in the individual operation process, based on an overall optimal plan list in which optimal orders of items throughout the entire logistics operation are arranged as overall optimal plans and a per-operation optimal order list in which optimal orders of items in the individual operation processes are arranged; the logistics operation optimization device compares the order of items in the overall optimal plan indicated by the past overall optimal plan list with the order of items indicated by the operation history, and searches for order combinations that are non-compliant with the overall optimal plan indicated by the past overall optimal plan list based on a non-compliance information list table that stores combinations of order of items that differ by a certain percentage, thereby determining the number of non-compliance order combinations; a step of the logistics operation optimization device calculating an estimated operation time from the overall optimal plan indicated in the overall optimal plan list; a step of calculating an evaluation value of the global optimal plan using an evaluation formula based on a degree of deviation between the order of items in the global optimal plan shown in the global optimal plan list calculated by the deviation degree calculation unit and the order of items in each work process, the number of combinations of orders resulting in default determined by the default order search unit, and the estimated work time determined by the work time estimation unit and an error in the estimated work time; determining an overall optimal plan including an evaluation value that satisfies a predetermined condition from among evaluation values ​​of the multiple overall optimal plans obtained by the overall optimal plan evaluation unit, and creating an overall optimal plan list in which an optimal order of items throughout the entire logistics operation is arranged for the determined overall optimal plan, a weighted linear sum of the degree of discrepancy between the order of items in the global optimal plan list and the order of items in each individual work process, the number of order combinations that will result in default, and the estimated work time and the error in the estimated work time, wherein the evaluation formula used by the global optimal plan evaluation unit to calculate the evaluation value of the global optimal plan is a weighted linear sum of the degree of discrepancy between the order of items in the global optimal plan list and the order of items in each individual work process, the number of order combinations that will result in default, and the estimated work time and the error in the estimated work time.

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