Work plan planning program, work plan planning method, and information processing device

Abstract

To provide: a work plan planning program capable of planning a work plan by suppressing a calculation load; a work plan planning method; and an information processing device.SOLUTION: The work plan planning program causes a computer to execute: first specific processing which specifies the combinations of orders, regarding each of a plurality of candidate paths which goes round a plurality of work positions contained in a plurality of orders, in such a manner that the total of movement distances in which each of a plurality of operators goes round the work positions specified by the combination of orders allocated to each of the plurality of operators, in the case where the plurality of orders is allocated to the plurality of operators satisfies a first predetermined condition; and second specific processing which selects a candidate path in which the total of the movement distances of each of the plurality of operators satisfies a second predetermined condition, out of the plurality of candidate paths acquired by executing the first specific processing, and which, from among the combinations of the orders allocated to each of the plurality of operators, specifies the going-round order where the total of the movement distances of each of the plurality of operators after switching the going-round orders satisfies a third predetermined condition.SELECTED DRAWING: Figure 12

Description

[Technical Field]

[0001] The present invention relates to a work planning program, a work planning method, and an information processing device. [Background technology]

[0002] Techniques for efficiently performing tasks such as picking have been disclosed (see, for example, Patent Documents 1 to 4). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2021-516803 [Patent Document 2] Japanese Patent Publication No. 2020-040836 [Patent Document 3] U.S. Patent No. 10,783,462 [Patent Document 4] U.S. Patent Publication No. 2018 / 0218311 Summary of the Invention [Problem to be solved by the invention]

[0004] There is a need to reduce the total travel distance in tasks that involve movement, such as picking. However, if no restrictions are placed on the direction of movement, the number of combinations becomes enormous, which can lead to a heavy computational load.

[0005] In one aspect, the present invention aims to provide a work planning program, a work planning method, and an information processing device that can create a work plan while reducing the calculation load. [Means for solving the problem]

[0006] In one aspect, the work planning program causes a computer to execute a first identification process to identify a combination of orders for each of a plurality of candidate routes that sequentially visit a plurality of work locations included in a plurality of orders such that when the plurality of orders are assigned to a plurality of workers, the sum of the travel distances traveled by each of the plurality of workers to visit the work locations identified by the combination of orders assigned to each of the plurality of workers satisfies a first predetermined condition; and a second identification process to select, from the plurality of candidate routes obtained by executing the first identification process, a candidate route whose sum of the travel distances traveled by each of the plurality of workers satisfies a second predetermined condition, and to rearrange the order of the combinations of orders assigned to each of the plurality of workers to identify a tour order whose sum of the travel distances traveled by each of the plurality of workers satisfies a third predetermined condition.

[0007] In another aspect, the work planning program causes a computer to execute the following steps: a first identification process for identifying a combination of orders for each of a plurality of candidate routes that sequentially visit a plurality of work locations included in a plurality of orders such that a sum of travel distances traveled by each of the plurality of workers to visit the work locations identified by the combination of orders to be assigned to each of the plurality of workers when the plurality of orders are assigned to the plurality of workers satisfies a first predetermined condition; a third identification process for selecting, from the plurality of candidate routes obtained by executing the first identification process, a candidate route for which a sum of travel distances traveled by each of the plurality of workers satisfies a second predetermined condition, identifying one combination of orders included in the selected candidate route, and repeatedly identifying and adding combinations for the remaining orders among the plurality of candidate routes; and a fourth identification process for rearranging the order order for each combination of orders obtained by executing the third identification process to identify a circumnavigation order for which a sum of travel distances traveled by each of the plurality of workers satisfies the second predetermined condition. [Effects of the Invention]

[0008] Work plans can be formulated while reducing the calculation load. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 10 is a diagram illustrating an example of order batching in which a combination of three orders is made into one batch. [Figure 2] (a) is a diagram illustrating an example of an order #1, (b) is a diagram illustrating an example of an order #2, and (c) is a diagram illustrating an example of an order #1 + an order #2. [Figure 3] 1A is a block diagram illustrating an example of the overall configuration of an information processing device, and FIG. 1B is a block diagram illustrating an example of the hardware configuration of the information processing device. [Figure 4] 10 is a flowchart showing a process executed by an information processing device. [Figure 5] FIG. 10 is a diagram illustrating an example layout. [Figure 6] FIG. 10 is a diagram illustrating an example layout. [Figure 7] 10(a) and 10(b) are diagrams illustrating layouts with orders. [Figure 8] FIG. 1 is a diagram illustrating a candidate route 1. [Figure 9] FIG. 10 is a diagram illustrating a candidate route 2. [Figure 10] FIG. 10 is a diagram illustrating a candidate route 3. [Figure 11] FIG. 10 is a diagram illustrating a candidate route 4. [Figure 12] (a) is a diagram showing the results of order batching optimization, and (b) is a diagram showing an example of the results of solving the TSP and the total travel distance. [Figure 13] FIG. 10 is a diagram illustrating the results of order batching optimization performed on candidate route 3. [Figure 14] FIG. 14 is a diagram illustrating the results of solving the TSP for the picking path in FIG. 13. [Figure 15] 10A is a diagram illustrating the determined combination of the first batch, and FIG. 10B is a diagram illustrating the result of order batching optimization for the remaining orders. [Figure 16]The order batching results for each confirmed batch are shown below. [Figure 17] FIG. 10 is a diagram illustrating an optimal solution output in step S9. DETAILED DESCRIPTION OF THE INVENTION

[0010] Before describing the embodiments, an overview of picking work will be provided. First, a delivery company receives multiple orders from customers. Each order includes picking instructions for one or more items. The delivery company has multiple picking workers, each of whom is assigned one or more orders (combinations of orders). Each worker patrols the shelves in the warehouse and picks up items placed on a specific shelf according to the instructions in the assigned order. Under these conditions, a solution is needed to minimize the total travel distance (total travel distance) of multiple workers.

[0011] Picking work in warehouses is often done by workers walking around the warehouse and loading the items they pick up onto carts or containers. One way to control labor costs and accommodate the growing size of warehouses due to increased logistics volume due to social conditions is to minimize the total travel distance. The key to effectively minimizing the total travel distance is to efficiently combine orders and calculate them.

[0012] In a one-way warehouse, once an order is determined, the picking route that will result in the shortest total travel distance is also determined. Therefore, it is possible to optimize the combination of orders so that the total travel distance is shortest (order batching optimization). However, in a free-layout warehouse with no restrictions on the direction of travel, it is difficult to determine the shortest picking route, so order batching optimization cannot be applied.

[0013] One idea is to select a picking route for each trip from all orders and calculate it each time by solving the Traveling Salesman Problem (TSP). However, in order to shorten the total travel distance, it is necessary to solve the TSP for all order combinations, and it is also necessary to extract the combination that results in the shortest total travel distance. Therefore, there is a risk that the amount of calculation will be enormous.

[0014] Here, we will explain the outline of the order batching optimization method. The order batching optimization method is based on sequential optimization, and solves small-scale problems using a brute force approach. The specific rules are as follows: Rule 1 Confirm the orders to be assigned to the combination (batch) one by one. Rule 2 Combinations to which no orders have been assigned are assigned the top of the candidate order sequence. Rule 3 For combinations to which orders have been assigned, the order with the shortest travel distance is searched for in a brute force manner, starting from the top of the candidate order sequence.

[0015] Figure 1 is a diagram illustrating order batching, in which a combination of three orders forms one batch. The top row of Figure 1 illustrates a candidate order sequence. The candidate order sequence is formed with orders in the order of #1, #2, and #3. First, order #1 is placed in the first position of batch #1 according to rule 2. Next, order #6 is placed in the second position of batch #1 according to rule 3. Next, order #8 is placed in the third position of batch #1 according to rule 3. Next, order #2 is placed in the first position of batch #2 according to rule 2. Next, order #4 is placed in the second position of batch #2 according to rule 3. Next, order #7 is placed in the third position of batch #2 according to rule 3.

[0016] A prerequisite for order batching is that the pickup order for the orders to be incorporated into a batch must be easily determined. If the pickup order cannot be easily determined, a separate optimization must be performed to determine the pickup order that minimizes the total travel distance.

[0017] Here, we will explain the issues with the order batching optimization method. In order batching, the total travel distance is the objective variable. This objective variable, the total travel distance, varies depending on the combination of orders. Figure 2(a) is a diagram illustrating order #1. Pickup will be performed from the hatched shelf. The travel distance for order #1 is assumed to be d1. Figure 2(b) is a diagram illustrating order #2. Pickup will be performed from the hatched shelf. The travel distance for order #2 is assumed to be d2.

[0018] FIG. 2(c) is a diagram illustrating a batch (order #1 + order #2). When order #1 and order #2 are combined, the shortest travel distance d 1+2 does not equal the moving distance d1+the moving distance d2.

[0019] Assuming a warehouse with one-way traffic, the travel distance can be calculated uniquely based on the batch candidate order. However, in a warehouse where free travel is assumed, the travel distance cannot be calculated uniquely. Therefore, it becomes necessary to optimize the picking order that results in the shortest travel route for each batch candidate, which can result in an enormous computational load for order batching.

[0020] As described above, order batching optimization cannot be simply applied to a free-layout warehouse where movement direction is not restricted. A massive amount of calculations may be required to solve the TSP for all orders and extract the optimal combination. Therefore, the following example describes an example in which a work plan can be created while reducing the calculation load. [Example]

[0021] Fig. 3(a) is a block diagram illustrating an example of the overall configuration of the information processing device 100. As illustrated in Fig. 3(a), the information processing device 100 includes a layout storage unit 10, an order storage unit 20, a candidate route generation unit 30, an optimization unit 40, an output unit 50, and the like.

[0022] Fig. 3(b) is a block diagram illustrating an example of a hardware configuration of the information processing device 100. As illustrated in Fig. 3(b), the information processing device 100 includes a CPU 101, a RAM 102, a storage device 103, an input device 104, a display device 105, and the like.

[0023] The CPU (Central Processing Unit) 101 is a central processing unit. The CPU 101 includes one or more cores. The RAM (Random Access Memory) 102 is a volatile memory that temporarily stores programs executed by the CPU 101, data processed by the CPU 101, etc. The storage device 103 is a non-volatile storage device. Examples of the storage device 103 include a read-only memory (ROM), a solid-state drive (SSD) such as a flash memory, and a hard disk driven by a hard disk drive. The storage device 103 stores a work planning program. The input device 104 is an input device such as a keyboard or a mouse. The display device 105 is a display device such as an LCD (Liquid Crystal Display). When the CPU 101 executes the work planning program, the layout storage unit 10, the order storage unit 20, the candidate path generation unit 30, the optimization unit 40, and the output unit 50 are realized. The layout storage unit 10, the order storage unit 20, the candidate path generation unit 30, the optimization unit 40, and the output unit 50 may be implemented using hardware such as dedicated circuits.

[0024] 4 is a flowchart showing the processing executed by the information processing device 100. Each processing executed by the information processing device 100 will be described below with reference to FIG.

[0025] First, the candidate route generating unit 30 acquires the layout stored in the layout storage unit 10 and the order stored in the order storage unit 20 (step S1).

[0026] The layout storage unit 10 stores the layout of the warehouse. The layout can be expressed in two-dimensional coordinates. For example, the layout can be expressed by coordinates (x, y) on mutually orthogonal x- and y-axes. Each shelf is specified by an x-coordinate and a y-coordinate. Coordinates where a shelf is set are impassable, while coordinates where no shelf is set are passable. In the example of FIG. 5, the following coordinates are set for the shelves: (1, 3), (2, 3), (3, 3), (4, 3), (5, 3), (1, 4), (2, 4), (3, 4), (4, 4), and (5, 4). The layout also sets a starting point. In the example of FIG. 5, the starting point is set to (3, 0) as an example.

[0027] The layout also defines the positions for picking up items from each shelf. For example, in the example of Figure 6, the pickup position for picking up items from the hatched shelf (3,3) is defined as (3,2) surrounded by a bold frame, and the pickup position for picking up items from the hatched shelves (5,4) and (5,6) is defined as (5,5) surrounded by a bold frame. In this way, for each pickup position, there may be one shelf from which items can be picked up, or two shelves that are back-to-back.

[0028] The order storage unit 20 stores orders. As an example, the order storage unit 20 stores orders 1 to 18. Each order is associated with a shelf from which to pick up an item. For example, in the example of FIG. 7(a), order 1 is associated with the shelf at (1,10) and the shelf at (11,9). To complete order 1, it is necessary to pick up an item from the shelf at (1,10) at the pickup position at (1,11) enclosed in a thick frame, and to pick up an item from the shelf at (11,9) at the pickup position at (12,9) enclosed in a thick frame.

[0029] Figure 7(b) shows a layout in which all orders, Order 1 to Order 18, are entered. The corresponding order numbers are entered on the shelves. Also, pickup locations #1 to #30 corresponding to the shelves designated for pickup are entered. For example, to pick up an item in Order 1, it is necessary to go through pickup locations #9 and #19.

[0030] Referring again to Figure 4, after executing step S1, the candidate route generation unit 30 generates a plurality of candidate routes that pass through all of the pickup positions #1 to #30 (step S2). There are no particular limitations on the method for generating these plurality of candidate routes, but it is desirable that each candidate route has a variety, such as a different route order. For example, the distance between multiple pickup positions where pickup can be made for two back-to-back item shelves is varied, and a route is created that passes through all pickup positions. Since detours must be taken into consideration in the back-to-back relationship, it is possible to add variety to the candidate routes by varying the distance between multiple pickup positions where pickup can be made for two back-to-back item shelves.

[0031] FIG. 8 is a diagram illustrating candidate route 1. Candidate route 1 passes through pickup positions #2, #5, #11, #14, and so on in a counterclockwise direction. FIG. 9 is a diagram illustrating candidate route 2. Candidate route 2 passes through pickup positions #1, #2, #11, #14, and so on in a counterclockwise direction. FIG. 10 is a diagram illustrating candidate route 3. Candidate route 3 passes through pickup positions #1, #2, #15, #16, and so on in a counterclockwise direction. FIG. 11 is a diagram illustrating candidate route 4. Candidate route 4 passes through pickup positions #2, #11, #5, #15, and so on in a counterclockwise direction.

[0032] Referring again to FIG. 4, after executing step S2, the optimization unit 40 performs order batching optimization for each candidate route (step S3). FIG. 12(a) is a diagram showing the results of the order batching optimization. Furthermore, the optimization unit 40 calculates the total travel distance for each batch order using a predetermined algorithm such as TSP solution based on the order batching optimization results of FIG. 12(a). FIG. 12(b) is a diagram showing an example of the results of TSP solution and the total travel distance. In the example of FIG. 12(b), the total travel distance for candidate route 3 is the shortest.

[0033] For example, Figure 13 illustrates the results of order batching optimization for candidate route 3. In Figure 13, the solid lines connecting each pickup location are sorted in the order of order 4, order 7, and order 13 on candidate route 3. In Figure 13, the dotted lines indicate the picking route that passes through the aisles within the layout. The total length of this picking route is 78 m. Orders are picked in order along the candidate routes sorted by the picking location corresponding to the order. The total travel distance is the sum of the distances between pickup locations calculated using Manhattan distance.

[0034] FIG. 14 is a diagram illustrating the results of solving the TSP for the picking route in FIG. 13. As illustrated in FIG. 14, the picking order is solved using TSP so that the total travel distance is the shortest. For example, in the example in FIG. 13, there was a round trip between pick-up position #6 and pick-up position #7, but this time only the outbound trip is shown. In the example in FIG. 14, the total pick-up route is 72 m.

[0035] Referring again to Figure 4, after executing step S3, the optimization unit 40 stores the results of the order batching optimization in step S3 in the RAM 102 or the storage device 103. Furthermore, based on the results of the order batching optimization in step S3, the optimization unit 40 determines the combination of the first batch for the candidate route with the shortest total travel distance (step S4). In the example of Figure 12, the total travel distance of candidate route 3 is the shortest, so the 1st batch of candidate route 3 (the combination of order 15, order 9, and order 18) is determined to be the first batch.

[0036] Next, the optimization unit 40 performs order batching optimization for the remaining orders not confirmed in step S4 for each candidate route (step S5). In Fig. 12, order batching optimization is performed for orders other than orders 15, 9, and 18 for each candidate route. Fig. 15(a) shows an example of the confirmed combinations of the first batch. Fig. 15(b) shows an example of the results of order batching optimization for the remaining orders.

[0037] Referring again to FIG. 4, the optimization unit 40 next determines whether there are any unconfirmed orders (step S6). If the determination in step S6 is "Yes," the process is executed again from step S4. As a result, the processes in steps S4 and S5 are executed until there are no more orders for which order batching optimization is to be performed. FIG. 16 illustrates an example of the order batching results for each confirmed batch. In FIG. 16, order batching is performed up to the sixth batch until there are no more orders.

[0038] If the answer in step S6 is "No," the optimization unit 40 reads out the results of step S3 stored in the RAM 102 or the storage device 103, and performs TSP solving for the read results of step 3 and the results obtained by repeating steps S4 to S6 in the order within each batch (step S7).

[0039] Next, the optimization unit 40 sums up the movement distances of each batch to calculate the total movement distance (step S8).

[0040] Next, the optimization unit 40 outputs the combination of batches that has the shortest total movement distance from the results obtained by executing step S8 as an optimal solution (step S9). After that, execution of the flowchart ends. Figure 17 is a diagram illustrating an example of the optimal solution output in step S9.

[0041] According to this embodiment, the combination of batches can be identified by steps S1 to S3 and S7, or by both steps S1 to S7.

[0042] According to the execution method of steps S1 to S3 and S7, each batch (combination of orders) is identified by performing order batching optimization for each of multiple candidate routes, and then the order of orders within each batch is rearranged to identify the tour order. This method makes it possible to apply order batching optimization to free layouts where the movement direction is not restricted. This makes it possible to create a work plan while reducing the calculation load.

[0043] Furthermore, according to the execution method of steps S1 to S7, order batching optimization is performed for each of multiple candidate routes to identify each batch (combination of orders), and the first batch of the candidate route with the shortest total travel distance among each candidate route is determined as the first batch. Order batching optimization is then performed for the remaining orders for each candidate route, and the first batch of the candidate route with the shortest total travel distance among each candidate route is determined as the next batch. This method makes it possible to apply order batching optimization to free layouts where the direction of travel is not restricted. This allows work plans to be created with reduced computational load.

[0044] Furthermore, by executing step S9, the result with the shorter travel distance is adopted from the results obtained by executing steps S1 to S3 and S7 and the results obtained by executing steps S1 to S7, so that a work plan with a shorter total travel distance can be formulated.

[0045] Here, we will explain the calculation scale (the scale of the target order combination). In the case of calculations performed one by one, a combination of 3 orders selected from 18 orders is a major factor influencing the calculation scale. 18 C3 = 816 pairs. For this combination, there are 2 combinations of 0 and 1. 816 power = 4.37 x 10 245 On the other hand, in this embodiment, there are 30 pick-up positions, and 30!=2.65×10 32 Therefore, the method of this embodiment makes it possible to significantly reduce the scale of calculation.

[0046] As described above, this embodiment effectively reduces the total movement distance in a free-layout warehouse where movement direction is not restricted. It also significantly reduces the number of combinations used in calculations compared to calculations performed on an as-you-go basis. Furthermore, the amount of information increases with the increase in the number of shelves in a warehouse and the number of pick-up positions between shelves, making this embodiment even more effective.

[0047] In this embodiment, picking up an item is described as an example of a task, but the task is not limited to picking up an item. For example, the task may include returning an item to a shelf.

[0048] In the above example, step S3 in FIG. 4 is an example of a first identification process. Steps S7 to S9 in FIG. 4 are an example of a second identification process. Repetition of steps S4 to S6 in FIG. 4 is an example of a third identification process. Steps S7 to S9 in FIG. 4 are an example of a fourth identification process. The optimization unit 40 is an example of an execution unit.

[0049] Although the embodiments of the present invention have been described in detail above, the present invention is not limited to such specific embodiments, and various modifications and variations are possible within the scope of the gist of the present invention as described in the claims. (Appendix 1) On the computer, a first identification process for identifying a combination of orders for each of a plurality of candidate routes that sequentially visit a plurality of work locations included in a plurality of orders, such that when the plurality of orders are assigned to a plurality of workers, a total of travel distances traveled by each of the plurality of workers to visit the work locations identified by the combination of orders assigned to each of the plurality of workers satisfies a first predetermined condition; a second specification process for selecting, from the plurality of candidate routes obtained by execution of the first specification process, a candidate route for which a total sum of travel distances of each of the plurality of workers satisfies a second predetermined condition, and for specifying, among the combinations of the orders assigned to each of the plurality of workers, a tour order by rearranging the order, such that a total sum of travel distances of each of the plurality of workers satisfies a third predetermined condition. (Appendix 2) The computer, a third identification process for repeatedly selecting, from the plurality of candidate routes obtained by execution of the first identification process, a candidate route in which the total sum of the travel distances of the plurality of workers satisfies the second predetermined condition, identifying one combination of the orders included in the selected candidate route, and identifying and adding combinations for the remaining orders from the plurality of candidate routes; and a fourth specification process for specifying a tour order, among the combinations of orders obtained by execution of the third specification process, such that the total sum of travel distances of the plurality of workers satisfies the second predetermined condition, by rearranging the tour order. (Appendix 3) the first identification process identifies a combination of orders for each of the plurality of candidate routes so that a total travel distance of each of the plurality of workers traveling around work locations identified by the combination of orders assigned to each of the plurality of workers is minimized. (Appendix 4) The work plan creation program according to claim 1, characterized in that in the second identification process, a candidate route that minimizes the total travel distance of each of the plurality of workers is selected from the plurality of candidate routes obtained by the first identification process, and a tour order is identified among the combinations of the orders assigned to each of the plurality of workers by rearranging the tour order to minimize the total travel distance of each of the plurality of workers. (Appendix 5) The work plan creation program according to claim 2, wherein the fourth specification process specifies a tour order that minimizes the total travel distance of each of the plurality of workers by rearranging the tour order among the combinations of the orders obtained by executing the third specification process. (Appendix 6) On the computer, a first identification process for identifying a combination of orders for each of a plurality of candidate routes that sequentially visit a plurality of work locations included in a plurality of orders, such that when the plurality of orders are assigned to a plurality of workers, a total of travel distances traveled by each of the plurality of workers to visit the work locations identified by the combination of orders assigned to each of the plurality of workers satisfies a first predetermined condition; a third identification process for repeatedly selecting a candidate route from the plurality of candidate routes obtained by executing the first identification process, in which the total sum of the travel distances of the plurality of workers satisfies a second predetermined condition, identifying one combination of the orders included in the selected candidate route, and identifying and adding combinations for the remaining orders from the plurality of candidate routes; and a fourth specification process for specifying a tour order, among the combinations of orders obtained by execution of the third specification process, such that the total sum of travel distances of the plurality of workers satisfies the second predetermined condition, by rearranging the tour order. (Appendix 7) The computer a first identification process for identifying a combination of orders for each of a plurality of candidate routes that sequentially visit a plurality of work locations included in a plurality of orders, such that when the plurality of orders are assigned to a plurality of workers, a total of travel distances traveled by each of the plurality of workers to visit the work locations identified by the combination of orders assigned to each of the plurality of workers satisfies a first predetermined condition; a second specification process for selecting, from the plurality of candidate routes obtained by execution of the first specification process, a candidate route for which the sum of the travel distances of each of the plurality of workers satisfies a second predetermined condition, and for specifying, among the combinations of the orders assigned to each of the plurality of workers, a tour order by rearranging the order so that the sum of the travel distances of each of the plurality of workers satisfies a third predetermined condition. (Appendix 8) The computer a third identification process for repeatedly selecting, from the plurality of candidate routes obtained by execution of the first identification process, a candidate route in which the total sum of the travel distances of the plurality of workers satisfies the second predetermined condition, identifying one combination of the orders included in the selected candidate route, and identifying and adding combinations for the remaining orders from the plurality of candidate routes; and a fourth specification process for specifying a tour order, among the combinations of orders obtained by execution of the third specification process, by rearranging the tour order so that the total sum of travel distances of the plurality of workers satisfies the second predetermined condition. (Appendix 9) 8. The work planning method according to claim 7, wherein in the first identification process, a combination of orders assigned to each of the plurality of workers is identified for each of the plurality of candidate routes so that a total travel distance of each of the plurality of workers traveling around work locations identified by the combination of orders assigned to each of the plurality of workers is minimized. (Appendix 10) The work planning method according to claim 7, characterized in that in the second identification process, a candidate route that minimizes the total travel distance of each of the plurality of workers is selected from the plurality of candidate routes obtained by the first identification process, and a tour order is identified among the combinations of the orders assigned to each of the plurality of workers so as to minimize the total travel distance of each of the plurality of workers by rearranging the tour order. (Appendix 11) 9. The work plan creation method according to claim 8, wherein the fourth specification process specifies a tour order that minimizes the total travel distance of each of the plurality of workers by rearranging the tour order among the combinations of orders obtained by executing the third specification process. (Appendix 12) The computer a first identification process for identifying a combination of orders for each of a plurality of candidate routes that sequentially visit a plurality of work locations included in a plurality of orders, such that when the plurality of orders are assigned to a plurality of workers, a total of travel distances traveled by each of the plurality of workers to visit the work locations identified by the combination of orders assigned to each of the plurality of workers satisfies a first predetermined condition; a third identification process for repeatedly selecting a candidate route from the plurality of candidate routes obtained by executing the first identification process, in which the total sum of the travel distances of the plurality of workers satisfies a second predetermined condition, identifying one combination of the orders included in the selected candidate route, and identifying and adding combinations for the remaining orders from the plurality of candidate routes; and a fourth specification process for specifying a tour order, among the combinations of orders obtained by execution of the third specification process, such that the total sum of travel distances of the plurality of workers satisfies the second predetermined condition. (Appendix 13) an execution unit that executes: a first identification process that identifies a combination of multiple orders for each of multiple candidate routes that sequentially visit multiple work locations included in multiple orders such that a sum of travel distances traveled by each of the multiple workers to visit the work locations identified by the combination of orders assigned to each of the multiple workers when the multiple orders are assigned to the multiple workers satisfies a first predetermined condition; and a second identification process that selects, from the multiple candidate routes obtained by executing the first identification process, a candidate route whose sum of travel distances traveled by each of the multiple workers satisfies a second predetermined condition, and rearranges the order order of the combinations of orders assigned to each of the multiple workers to identify a patrol order such that a sum of travel distances traveled by each of the multiple workers satisfies a third predetermined condition. (Appendix 14) The information processing device described in Appendix 13, wherein the execution unit executes a third identification process to select, from the plurality of candidate routes obtained by execution of the first identification process, a candidate route in which the sum of the travel distances of each of the plurality of workers satisfies the second predetermined condition, identify one combination of the orders included in the selected candidate route, and repeatedly identify and add combinations for the remaining orders on the plurality of candidate routes; and a fourth identification process to rearrange the order of visits among the combinations of orders obtained by execution of the third identification process to identify a visit order in which the sum of the travel distances of each of the plurality of workers satisfies the second predetermined condition. (Appendix 15) The information processing device described in Appendix 13, wherein in the first identification process, the execution unit identifies a combination of orders for each of the plurality of candidate routes so that a total travel distance traveled by each of the plurality of workers to travel around work locations identified by the combination of orders assigned to each of the plurality of workers is minimized. (Appendix 16) The information processing device described in Appendix 13 is characterized in that, in the second identification process, the execution unit selects, from the plurality of candidate routes obtained by the first identification process, a candidate route that minimizes the total sum of travel distances of each of the plurality of workers, and, among the combinations of the orders assigned to each of the plurality of workers, rearranges the patrol order to identify a patrol order that minimizes the total sum of travel distances of each of the plurality of workers. (Appendix 17) The information processing device described in Appendix 14, wherein the execution unit, in the fourth identification process, rearranges the patrol order among each combination of orders obtained by execution of the third identification process to identify a patrol order that minimizes the total travel distance of each of the multiple workers. (Appendix 18) an execution unit that executes: a first identification process that identifies a combination of orders for each of a plurality of candidate routes that sequentially visit a plurality of work locations included in a plurality of orders, such that a sum of travel distances traveled by each of the plurality of workers to visit the work locations identified by the combination of orders to be assigned to each of the plurality of workers when the plurality of orders are assigned to the plurality of workers satisfies a first predetermined condition; a third identification process that selects, from the plurality of candidate routes obtained by execution of the first identification process, a candidate route for which a sum of travel distances traveled by each of the plurality of workers satisfies a second predetermined condition, identifies one combination of orders included in the selected candidate route, and repeatedly identifies and adds combinations for the remaining orders to the plurality of candidate routes; and a fourth identification process that rearranges the order order order sequence among the combinations of orders obtained by execution of the third identification process to identify a circulating order for which a sum of travel distances traveled by each of the plurality of workers satisfies the second predetermined condition. [Explanation of symbols]

[0050] 10 Layout storage section 20 Order storage section 30 Candidate route generation unit 40 Optimization Section 50 Output section 100 Information processing device 101 CPU 102 RAM 103 Storage device 104 Input Device 105 Display device

Claims

1. On the computer, a first identification process for identifying a combination of orders for each of a plurality of candidate routes that sequentially visit a plurality of work locations included in a plurality of orders, such that when the plurality of orders are assigned to a plurality of workers, a total of travel distances traveled by each of the plurality of workers to visit the work locations identified by the combination of orders assigned to each of the plurality of workers satisfies a first predetermined condition; a second specification process for selecting, from the plurality of candidate routes obtained by execution of the first specification process, a candidate route in which the sum of the travel distances of each of the plurality of workers satisfies a second predetermined condition, and for specifying, among the combinations of the orders assigned to each of the plurality of workers, a tour order in which the sum of the travel distances of each of the plurality of workers satisfies a third predetermined condition by rearranging the tour order.

2. The computer, a third identification process for repeatedly selecting, from the plurality of candidate routes obtained by execution of the first identification process, a candidate route in which the total sum of the travel distances of the plurality of workers satisfies the second predetermined condition, identifying one combination of the orders included in the selected candidate route, and identifying and adding combinations for the remaining orders on the plurality of candidate routes; and a fourth specification process for specifying a patrol order among the combinations of orders obtained by execution of the third specification process, so that the total sum of travel distances of the plurality of workers satisfies the second predetermined condition.

3. 2. The work plan creation program according to claim 1, wherein in the first identification process, a combination of orders assigned to each of the plurality of workers is identified for each of the plurality of candidate routes so that a total travel distance traveled by each of the plurality of workers to travel around work locations identified by the combination of orders assigned to each of the plurality of workers is minimized.

4. 2. The work plan creation program according to claim 1, wherein in the second identification process, a candidate route that minimizes the total travel distance of each of the plurality of workers is selected from the plurality of candidate routes obtained by the first identification process, and a tour order is identified among the combinations of the orders assigned to each of the plurality of workers by rearranging the tour order to minimize the total travel distance of each of the plurality of workers.

5. 3. The work plan creation program according to claim 2, wherein in the fourth specification process, the order of visits is rearranged among the combinations of the orders obtained by execution of the third specification process to specify an order of visits that minimizes the total travel distance of each of the plurality of workers.

6. On the computer, a first identification process for identifying a combination of orders for each of a plurality of candidate routes that sequentially visit a plurality of work locations included in a plurality of orders, such that when the plurality of orders are assigned to a plurality of workers, a total of travel distances traveled by each of the plurality of workers to visit the work locations identified by the combination of orders assigned to each of the plurality of workers satisfies a first predetermined condition; a third identification process for repeatedly selecting a candidate route from the plurality of candidate routes obtained by executing the first identification process, in which the total sum of the travel distances of the plurality of workers satisfies a second predetermined condition, identifying one combination of the orders included in the selected candidate route, and identifying and adding combinations for the remaining orders on the plurality of candidate routes; and a fourth specification process for specifying a patrol order, among the combinations of orders obtained by execution of the third specification process, such that the total sum of travel distances of the plurality of workers satisfies the second predetermined condition by rearranging the patrol order.

7. The computer a first identification process for identifying a combination of orders for each of a plurality of candidate routes that sequentially visit a plurality of work locations included in a plurality of orders, such that when the plurality of orders are assigned to a plurality of workers, a total of travel distances traveled by each of the plurality of workers to visit the work locations identified by the combination of orders assigned to each of the plurality of workers satisfies a first predetermined condition; a second specification process for selecting, from the plurality of candidate routes obtained by execution of the first specification process, a candidate route for which the sum of the travel distances of each of the plurality of workers satisfies a second predetermined condition, and for specifying, among the combinations of the orders assigned to each of the plurality of workers, a patrol order by rearranging the order so that the sum of the travel distances of each of the plurality of workers satisfies a third predetermined condition.

8. The computer a first identification process for identifying a combination of orders for each of a plurality of candidate routes that sequentially visit a plurality of work locations included in a plurality of orders, such that when the plurality of orders are assigned to a plurality of workers, a total of travel distances traveled by each of the plurality of workers to visit the work locations identified by the combination of orders assigned to each of the plurality of workers satisfies a first predetermined condition; a third identification process for repeatedly selecting a candidate route from the plurality of candidate routes obtained by executing the first identification process, in which the total sum of the travel distances of the plurality of workers satisfies a second predetermined condition, identifying one combination of the orders included in the selected candidate route, and identifying and adding combinations for the remaining orders on the plurality of candidate routes; and a fourth specification process for specifying a patrol order, among the combinations of orders obtained by execution of the third specification process, such that the total sum of travel distances of the plurality of workers satisfies the second predetermined condition.

9. an execution unit that executes: a first identification process that identifies a combination of multiple orders for each of multiple candidate routes that sequentially circulates multiple work locations included in multiple orders such that a sum of travel distances traveled by each of the multiple workers to circulate the work locations identified by the combination of orders assigned to each of the multiple workers when the multiple orders are assigned to the multiple workers satisfies a first predetermined condition; and a second identification process that selects, from the multiple candidate routes obtained by execution of the first identification process, a candidate route whose sum of travel distances traveled by each of the multiple workers satisfies a second predetermined condition, and rearranges the order order of the combinations of orders assigned to each of the multiple workers to identify a circulating order such that a sum of travel distances traveled by each of the multiple workers satisfies a third predetermined condition.

10. an execution unit that executes: a first identification process that identifies, for each of a plurality of candidate routes that sequentially visit a plurality of work locations included in a plurality of orders, a combination of orders that would be assigned to a plurality of workers when the plurality of orders are assigned to the plurality of workers, such that a sum of travel distances that each of the plurality of workers travels to visit the work locations specified by the combination of orders satisfies a first predetermined condition; a third identification process that selects, from the plurality of candidate routes obtained by execution of the first identification process, a candidate route for which a sum of travel distances of each of the plurality of workers satisfies a second predetermined condition, identifies one combination of orders included in the selected candidate route, and repeatedly identifies and adds combinations for the remaining orders to the plurality of candidate routes; and a fourth identification process that rearranges the order order order order order order order order for each combination of orders obtained by execution of the third identification process to identify a circulating order for which a sum of travel distances of each of the plurality of workers satisfies the second predetermined condition.

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