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

Abstract

Under the condition that, in relation to a plurality of articles, a plurality of columns each containing a plurality of article shelves, and a plurality of paths provided among the plurality of columns, first work is carried out in which a plurality of first articles identified by an identifier are placed on a moving body from an article shelf, and second work is carried out in which a plurality of second articles placed on the moving body are placed on an article shelf identified by an identifier specified for said articles: with regard to a work plan in which the moving body carries out the first work and the second work for specified articles from among the plurality of articles, either a first search for searching for such a work plan as to reduce overall travel distance in the direction of extension of the paths or a second search for searching for such a work plan as to reduce the overall travel distance between the plurality of columns; a work plan found in the search is divided into a plurality of partial plans; both the first search and the second search are performed on each of the plurality of partial plans; and the results of the first search and the second search for the plurality of partial plans are combined.　

Description

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

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

[0002] A technology for automatically performing picking work has been disclosed (see Patent Document 1).

[0003] Japanese Patent Application Laid-Open No. 2022-068557

[0004] In warehouses and other locations with rows of shelves, picking operations are sometimes performed using mobile vehicles, such as automated guided vehicle (AGV). However, because a maximum number of items can be placed on each mobile vehicle, the number of items that can be transported at one time is limited. Furthermore, a large number of shelves exist in a large warehouse. Therefore, creating a work plan for simultaneously performing collection and return operations is complex. This issue is not limited to cases where picking operations are performed automatically by a mobile vehicle, but can also arise when workers manually perform picking operations using a mobile vehicle such as a cart.

[0005] In one aspect, the present invention aims to provide an information processing device, a work planning method, and a work planning program that can plan efficient collection and return work.

[0006] In one aspect, the work planning program is configured to include a plurality of rows each including a plurality of items and a plurality of item shelves, and a plurality of aisles provided between the plurality of rows, and the program defines identifiers indicating a correspondence between each of the plurality of items and each of the plurality of item shelves, and a mobile body having a placement space for placing the items moves along one of the plurality of aisles as an outbound route and along one of the plurality of aisles as a return route, and includes at least one of a first task of placing a plurality of first items identified by the identifiers from the item shelves onto the mobile body, and a second task of placing a plurality of second items placed on the mobile body onto the item shelves identified by the identifiers defined for the second items. The computer is caused to execute a process of performing either a first search for a work plan in which the mobile body performs the first task and the second task on a specified item among the plurality of items, the first search being for searching a work plan that reduces the total movement distance in the direction in which the passage extends, or a second search being for searching a work plan that reduces the total movement distance between the plurality of rows, and dividing the searched work plan into a plurality of partial plans, a process of performing both the first search and the second search for each of the plurality of partial plans, and a process of formulating a work plan by combining the results of the first search and the second search for the plurality of partial plans.

[0007] Efficient collection and return operations can be planned.

[0008] 1 is a diagram for explaining an overview of an automatic picking operation; a diagram illustrating an example of a picking operation that allows efficient collection and return operations; a perspective view of a warehouse; a diagram illustrating the layout of a warehouse; (a) is a block diagram illustrating an example of the overall configuration of an information processing device, and (b) is a block diagram illustrating an example of the hardware configuration of the information processing device; a diagram illustrating an example of a stored layout; a diagram illustrating an example of an order; a flowchart showing an example of processing executed by the information processing device; a diagram illustrating an example of an aisle number; a diagram illustrating an example of the number of counted orders; a diagram illustrating the V-method; a diagram illustrating the P-method; a diagram illustrating the P-method; a flowchart showing details of step S3; a flowchart showing details of step S3; a flowchart showing details of optimization using the V-method; and (b) are flowcharts showing details of step S72, each illustrating an example of optimization. (a) and (b) are diagrams illustrating an example of optimization.

[0009] Before describing the embodiments, an outline of the automatic picking operation will be described.

[0010] In logistics warehouses, various products are stored in a vast space. In such warehouses, multiple types of products are stored. Each product case contains multiple items of the same product, and is stored on a designated shelf.

[0011] In the picking operation of these products, there are cases where a worker collects the products from product cases on each product shelf, and cases where a traveling vehicle (automated vehicle: AGV) automatically collects the products. Here, an example in which the traveling vehicle automatically collects the products will be described. For example, as illustrated in FIG. 1 , a traveling vehicle 201 travels to a product shelf 202 that stores a product case (hereinafter, a collected product case) containing a product designated for collection, and picks the collected product case from the product shelf 202. This operation will be referred to as the collection operation. After placing the collected product case in its own loading space, the traveling vehicle 201 transports the collected product case to a picking operation area 203 and collects it. In the picking operation area 203, a worker performs the final picking operation from the collected product case. The traveling vehicle 201 transports the product case (hereinafter, a return product case) after the final picking operation has been performed and returns it to the original product shelf 202.

[0012] However, the traveling machine 201 is specified to have a maximum number of product cases that can be loaded, limiting the number of product cases that can be transported at one time. Furthermore, a large number of product shelves exist in a vast warehouse. Therefore, creating a work plan for simultaneously performing collection and return operations is complex. Failure to consider the combination of product cases to be returned and collected in a single operation and the order of operations can actually reduce work efficiency. The specified maximum number of product cases may differ from the maximum number of product cases that the traveling machine 201 can accommodate. For example, even if the loading space of the traveling machine 201 can accommodate a maximum of seven product cases, the specified maximum number of product cases that can be loaded may be six. Thus, it is desirable for a work planning means to be able to handle large-scale problems.

[0013] 2 is a diagram illustrating an example of a picking operation that allows efficient collection and return operations. In the example of FIG. 2, the traveling machine 201 returns a return product case along the travel route indicated by the arrow, collects a collected product case in an empty storage space, and subsequently collects collected product cases while returning return product cases so as not to exceed the maximum number that can be stored. In this case, the traveling machine 201 can efficiently perform picking operations.

[0014] However, due to operational limitations of the traveling machine 201, aisles in warehouses are often one-way. Therefore, if the shelf holding the collection case is located before the shelf holding the return case, the vehicle must detour around the aisle after completing the return work and return to the shelf ahead to collect the items, resulting in an extra trip. Furthermore, because orders and work situations can change, it is necessary to create a plan in real time to adapt to the situation.

[0015] Here, the arrangement of product shelves 202 in the warehouse will be described in detail. FIG. 3 is a perspective view of the warehouse. As illustrated in FIG. 3, product shelves 202 are arranged in multiple columns (1, 2, 3, . . .) and multiple rows (1, 2, 3, . . .). That is, the position of each product shelf is defined using an identifier, a row / column number (= (row number, column number)) that has a column number and a row number. Each product shelf 202 houses multiple product cases. The numbers of the product cases housed in each product shelf 202 (hereinafter referred to as product numbers) are designated as identifiers. For example, the product numbers are designated such that the product shelf 202 with row / column number (1, 1) houses product cases with product numbers 1 to 6, and the product shelf 202 with row / column number (2, 1) houses product cases with product numbers 7 to 12. Product cases with the same product number house the same type of product. Product cases with different product numbers house different types of products.

[0016] The provision of gaps between the rows allows the traveling machine 201 to travel. A route with gaps between the rows is hereinafter referred to as an aisle. If there were no gaps between the rows, the traveling machine 201 would not be able to travel. The traveling machine 201 does not turn back midway through an aisle. For example, the traveling machine 201 does not travel partway down the aisle between row 1 and row 2 and then turn back. Note that the traveling machine 201 may travel back and forth along the same aisle in one round trip, but in the following example, the traveling machine 201 will not travel back and forth along the same aisle in one round trip. For example, after traveling down the aisle between row 1 and row 2, the traveling machine 201 will not return down that aisle, but will return down another aisle. However, if the return route is the aisle between row 1 and row 2, the traveling machine 201 can travel down that aisle as the outbound route of the next round trip.

[0017] The traveling machine 201 can perform both return and collection operations on the same product shelf. For example, while the traveling machine 201 remains stopped at the product shelf with queue number (1,1), it can return product cases with product numbers 1 to 3 and collect product cases with product numbers 4 to 6. In the example described here, when the traveling machine 201 is working on the product shelf with queue number (1,1), it does not perform work on the product shelf with queue number (1,2). However, it is also possible to collect product cases with queue numbers (1,1) and (1,2) in a single operation without distinguishing between odd and even rows.

[0018] FIG. 4 is a diagram illustrating the layout of a warehouse used in the following description. As illustrated in FIG. 4, an aisle is configured between row 1 and row 2, no aisle is configured between row 2 and row 3, and an aisle is configured between row 3 and row 4. Expressed in general terms, an aisle is configured between row (2m-1) and row (2m), and no aisle is configured between row (2m) and row (2m+1). In each row, the side of the work picking area (the side from which the outbound journey begins) is defined as the front side, and the side opposite the work picking area (the side from which the return journey begins) is defined as the back side. The outbound journey is when the traveling machine 201 travels from the front side to the back side, and the return journey is when it travels from the back side to the front side.

[0019] There are two types of optimization methods for creating work plans for such sites. The first is a method for reducing the total distance traveled in the direction perpendicular to the parts shelves (perpendicular to the direction in which the aisles extend) when viewing the warehouse from a plan view, and the second is a method for reducing the total distance traveled in the direction parallel to the parts shelves (parallel to the direction in which the aisles extend).

[0020] The effectiveness of these methods varies depending on factors such as the warehouse layout, the distribution of parts on each parts shelf, and the order contents, so in order to create an effective plan, it is desirable to select a method each time to suit the situation on site. However, it is not practical from an operational standpoint to consider and apply an appropriate method each time to suit the layout and order. Alternatively, it is possible to standardize the methods, plan efficient part placement and layout accordingly, and then change them as needed, but this is also difficult to say is a realistic operational method.

[0021] In the following embodiments, an information processing device, a work planning method, and a work planning program that can plan efficient collection and return work will be described.

[0022] Fig. 5A is a block diagram illustrating an example of the overall configuration of an information processing device 100. As illustrated in Fig. 5A, the information processing device 100 includes a layout storage unit 10, an order storage unit 20, an order creation unit 30, a first optimization unit 40, a second optimization unit 50, a division unit 60, a third optimization unit 70, an output unit 80, and the like.

[0023] Fig. 5B is a block diagram illustrating an example of the hardware configuration of the information processing device 100. As illustrated in Fig. 5B, 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.

[0024] 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, and the like. The storage device 103 is a non-volatile storage device. For example, the storage device 103 may be a read-only memory (ROM), a solid-state drive (SSD) such as a flash memory, or a hard disk driven by a hard disk drive. The storage device 103 stores a work plan creation 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). The CPU 101 executes the work planning program to realize a layout storage unit 10, an order storage unit 20, an order creation unit 30, a first optimization unit 40, a second optimization unit 50, a division unit 60, a third optimization unit 70, and an output unit 80. Note that the layout storage unit 10, the order storage unit 20, the order creation unit 30, the first optimization unit 40, the second optimization unit 50, the division unit 60, the third optimization unit 70, and the output unit 80 may be implemented using hardware such as dedicated circuits.

[0025] The layout storage unit 10 stores the layout of warehouse shelves. Specifically, as shown in Fig. 6, the layout storage unit 10 stores the product number of each product case in association with the row and column number of the product shelf where the product case is stored.

[0026] The order storage unit 20 stores orders input from an external source. FIG. 7 is a diagram illustrating an example of an order. As illustrated in FIG. 7, the order associates the product numbers of the items to be picked with the row numbers of the shelves where the product cases for each product number are located. Note that, since the layout storage unit 10 associates each product number with the row and column numbers of the shelves, the order may specify only the product number. Furthermore, the order does not yet specify the order for collection.

[0027] The following describes in detail the processing executed by the information processing device 100. FIG.

[0028] As illustrated in FIG. 8, the order creation unit 30 reads the order stored in the order storage unit 20 (step S1).

[0029] Next, the order making unit 30 refers to the orders read in step S1 and the layout stored in the layout storage unit 10, and counts the number of orders in each aisle (step S2).

[0030] As illustrated in FIG. 9 , the aisle between row (2m-1) and row (2m) is referred to as aisle m. Therefore, the aisle between row 1 and row 2 is aisle 1, the aisle between row 3 and row 4 is aisle 2, and the aisle between row 5 and row 6 is aisle 3. Also, in FIG. 9 , the number of product cases to be collected or returned at each product shelf is written on each shelf. FIG. 10 is a diagram illustrating the number of counted orders. In aisle 1, the number of product cases to be collected or returned is seven. In aisle 2, the number of product cases to be collected or returned is three. The order creation unit 30 creates an order of aisle numbers as a priority for collecting product cases. While the initial order of the aisle numbers is not particularly limited, in FIG. 10 the order is determined in ascending order of aisle numbers.

[0031] Here, the V method, which is a method for reducing the total moving distance in the vertical direction, and the P method, which is a method for reducing the total moving distance in the horizontal direction, will be described.

[0032] 11 is a diagram illustrating the V-method. In the V-method, in order to reduce the total vertical travel distance, a plan is created to collect and return orders in the same aisle as much as possible in one operation according to the order of aisle numbers.

[0033] For example, if the order is determined in ascending order of aisle numbers, priority is given to planning tasks starting from aisle 1. In the example of Figures 9 and 10, tasks (1) to (10) are required. Task (1) involves moving along aisle 1, collecting six product cases, and returning. Task (2) involves moving along aisle 1, returning the product cases collected in task (1), collecting product cases on the return journey along aisle 2, collecting product cases on the outbound journey along aisle 3, and collecting product cases on the return journey along aisle 4.

[0034] In order to reduce the total distance traveled in the vertical direction, task (2) is performed by going down aisle 1 and returning down aisle 2 without skipping aisle 2. The same applies to tasks (3) and onwards. When tasks (1) to (10) are tallied, they require 14 rows of vertical movement and 12 round trips in the horizontal direction.

[0035] 12 and 13 are diagrams explaining the P method. In the P method, in order to reduce the total travel distance in the parallel direction, a plan is created to complete one collection and return operation in as few round trips as possible according to the order of the aisle numbers. Figure 12 shows an example of planning for operations (1) and (2).

[0036] Task (1) simply involves going to collect items, so it involves picking up six items in aisle 1. For task (2), the item cases collected in task (1) are returned while moving along aisle 1. One item case is also collected in aisle 1. Next, more item cases can be collected by returning via aisle 4 than via aisles 2 and 3. Therefore, task (2) skips aisles 2 and 3 and returns via aisle 4. When planned in this way, as shown in Figure 13, it will require 22 rows of movement vertically and 10 round trips horizontally.

[0037] Referring again to Fig. 8, after executing step S2, the first optimization unit 40 optimizes the work plan using the P method (step S3).

[0038] Fig. 14 is a flowchart showing details of step S3. As illustrated in Fig. 14, the first optimization unit 40 reads an order from the order storage unit 20 (step S21).

[0039] Next, the first optimization unit 40 creates a work plan using the P method in accordance with the order of the passage numbers (step S22). Details of step S22 will be described later.

[0040] Next, the first optimization unit 40 calculates the total travel distance for the work plan created in step S22 (step S23).

[0041] Next, the first optimization unit 40 records the sequence of the passage numbers used and the total travel distance from the results of step S23 (step S24).

[0042] Next, the first optimization unit 40 determines whether the number of executions of steps S22 to S24 has reached the upper limit (step S25).

[0043] If the determination in step S25 is "No," the first optimization unit 40 changes the order of the passage numbers (step S26), and then the process is executed again from step S22.

[0044] If the determination in step S25 is "Yes," the first optimization unit 40 outputs the optimal work plan from among the work plans obtained by repeating steps S22 to S26 (step S27). The optimal work plan here is the work plan with the shortest total movement distance in the parallel direction.

[0045] The optimization algorithm of the optimization calculation portion of steps S22 to S26 is not particularly limited, but may be, for example, an evolutionary algorithm such as a genetic algorithm. These algorithms are used as optimization algorithms for changing the order of the passage numbers.

[0046] 15 and 16 are flowcharts showing the details of step S3. As illustrated in FIGS. 15 and 16, the first optimization unit 40 reads the order for the odd-numbered column and sets n=1 (step S31). Note that if the odd-numbered column and the even-numbered column can be collected in one operation without distinguishing between odd-numbered and even-numbered columns, step S31 is not executed. Next, the first optimization unit 40 determines whether there are any products (product numbers) remaining that have not been allocated to the work set (step S32). When step S32 is executed for the first time, no products have yet been allocated to the work set, so step S32 returns "Yes."

[0047] If step S32 returns "Yes," the first optimization unit 40 calculates the number of products Pn(t) that have not yet been allocated in column n (step S33). The number of products corresponds to the number of product numbers. Next, the first optimization unit 40 determines whether the number of products Pn(t) is equal to or greater than the specified maximum number of products that can be placed M (step S34).

[0048] If the answer in step S34 is "Yes," the first optimization unit 40 creates a collection set A of a single row A having M items from the items in row n that have not yet been allocated to a working set (step S35).

[0049] Next, the first optimization unit 40 determines whether Pn(t)! is a natural number N times M (step S36). In step S36, it is determined whether the remaining number of products in row n is a multiple of the maximum number of products that can be placed, M. If it is a multiple, a set consisting of only n rows is created by repeating it with D. If the determination in step S36 is "No," the first optimization unit 40 creates a collection set A for row n, which is a single row A and has M products, from the products that have not yet been allocated (step S37).

[0050] Next, the first optimization unit 40 calculates the number of products Pn(t) that have not yet been allocated (step S38). Next, the first optimization unit 40 determines whether Pn(t) calculated in step S38 is zero (step S39). If the determination in step S39 is "No," the process is repeated from step S37. If the determination in step S39 is "Yes," the first optimization unit 40 adds 1 to n (step S40). Thereafter, the process is repeated from step S32.

[0051] If step S36 returns "Yes," the first optimization unit 40 determines whether the number of products Pn(t) is 2M or greater (step S41). If step S41 returns "Yes," the first optimization unit 40 selects a column k that is different from column n (step S42). Next, the first optimization unit 40 creates a collection set of two columns (A+B) from columns n and k (step S43).

[0052] Next, the first optimization unit 40 creates a collection set A for a single row A from row n (step S44). Next, the first optimization unit 40 determines whether there are any products that have not yet been allocated to rows 1 to n (step S45). If the determination in step S45 is "No," step S40 is executed.

[0053] If the determination in step S45 is "Yes," the first optimization unit 40 identifies the column L of products (product numbers) that have not yet been allocated (step S46). Next, the first optimization unit 40 determines whether n=L (step S47). If the determination in step S47 is "Yes," step S37 is executed.

[0054] If the determination in step S34 is "No," the first optimization unit 40 selects a column k different from column n (step S48). Next, the first optimization unit 40 creates a collection set B of a single column B from column k (step S49). Next, the first optimization unit 40 creates a collection set (A+B) of two columns (A+B) from columns n and k (step S50).

[0055] Next, the first optimization unit 40 creates a collection set B of a single column B from column k (step S51). Next, the first optimization unit 40 identifies column L of the lowest product (product number) that has not yet been allocated (step S52). Next, the first optimization unit 40 determines whether k=L (step S53). If the determination in step S53 is "No," step S54 is executed. Step S54 is also executed if the determination in step S47 is "No."

[0056] If step S53 returns "Yes," the first optimization unit 40 sets n = k (step S55). Next, the first optimization unit 40 calculates the number of products Pn(t) that have not yet been allocated for column n (step S56). Next, the first optimization unit 40 determines whether the number of products Pn(t) is equal to or greater than M (step S57). If step S57 returns "No," S48 is executed. If step S57 returns "Yes," step S36 is executed.

[0057] If step S32 returns "No," the first optimization unit 40 determines whether the even-numbered columns have been processed (whether product numbers have been allocated) (step S59). If step S59 returns "No," the first optimization unit 40 reads the even-numbered columns from the orders stored in the order storage unit 20 and sets n=2 (step S60). Then, the process is executed again from step S32. If odd-numbered columns and even-numbered columns are not distinguished, the process proceeds to step S61 if step S32 returns "No." If step S39 returns "Yes," the first optimization unit 40 outputs the creation results of each collection set as the creation results of the work set (S41). Then, execution of the flowchart ends.

[0058] Referring again to Figure 8, after step S3 is executed, the dividing unit 60 divides the work plan formulated in step S2 into a plurality of partial plans (step S4). For example, a work plan in which tasks (1) to (10) are performed in order is divided into a partial plan for tasks (1) to (3), a partial plan for tasks (4) to (6), and a partial plan for tasks (7) to (10) without changing the order of the tasks.

[0059] Next, for each partial plan divided in step S4, the first optimization unit 40 optimizes it using the P method, and the second optimization unit 50 optimizes it using the V method (step S5). Optimization using the P method can be performed by the processes shown in Figures 14 to 16. Optimization using the V method will be described below.

[0060] Fig. 17 is a flowchart showing details of optimization using the V method. As illustrated in Fig. 17, the second optimization unit 50 reads an order from the order storage unit 20 (step S71).

[0061] Next, the second optimization unit 50 creates a work plan using the V-method in accordance with the path number order (step S72). Details of step S72 will be described later.

[0062] Next, the second optimization unit 50 calculates the total travel distance for the work plan created in step S72 (step S73).

[0063] Next, the second optimization unit 50 records the passage number order and the total movement distance from the result of step S73 (step S74).

[0064] Next, the second optimization unit 50 determines whether the number of times steps S72 to S74 have been executed has reached the upper limit (step S75).

[0065] If the determination in step S75 is "No", the order of the passage numbers is changed (step S76), and then the process is executed again from step S72.

[0066] If the determination in step S75 is "Yes," the second optimization unit 50 outputs the optimal work plan from among the work plans obtained by repeating steps S72 to S76 (step S77). The optimal work plan here is the work plan with the shortest vertical movement distance.

[0067] The optimization algorithm of the optimization calculation portion of steps S72 to S76 is not particularly limited, but may be, for example, an evolutionary algorithm such as a genetic algorithm. These algorithms are used as optimization algorithms for changing the order of the passage numbers.

[0068] 18 is a flowchart showing the details of step S72. As shown in FIG. 18, the second optimization unit 50 calculates the maximum value Na of the aisle number, the order sequence S Ｎ , aisle S Ｎ The remaining quantity of the product in Ｎ , and the maximum number M of the traveling machine 201 are read (step S81). Note that the maximum value Na and the maximum number M of the traveling machine 201 are integers. Ｎ and remaining product quantity P Ｎ is a matrix, and N is an integer.

[0069] Next, the second optimization unit 50 sets N to "0" (step S82).

[0070] Next, the second optimization unit 50 calculates the remaining product quantity P Ｎ is greater than 0 (step S83). If the determination in step S83 is "Yes", the second optimization unit 50 Ｎ (Step S84) The combination G' is a matrix.

[0071] Next, the second optimization unit 50 calculates the remaining product quantity P Ｎ (Step S85) Then, the process is executed again from Step S83.

[0072] If the determination in step S83 is "No", the second optimization unit 50 determines whether N is smaller than the maximum value Na (step S86).

[0073] If the determination in step S86 is "Yes", the second optimization unit 50 adds 1 to N (step S87). After that, the process is executed again from step S83.

[0074] Steps S83 to S87 are loop processes that are repeated M times. However, if the determination in step S86 is "No," the loop process ends. If step S83 has been repeated M times, step S85 or step S87 is executed, and then the loop process ends.

[0075] After the loop processing is completed, the second optimization unit 50 adds the combination G' to the work plan G (step S88). Next, the second optimization unit 50 determines whether the remaining number of products has reached 0 (step S89). If the determination in step S89 is "No," the loop processing is executed again from step S83. If the determination in step S89 is "Yes," the execution of the flowchart ends.

[0076] Next, as an example, the travel distance required to complete all 180 orders was calculated using the method according to this embodiment. The data used shows the distribution of orders in each aisle, as illustrated in Figure 19(a). Furthermore, as illustrated in Figure 19(b), the shelves storing parts are arranged at 1m intervals, with each row of shelves measuring 1m wide and 15m long.

[0077] When the order in Figure 19(a) was optimized using the V method and the P method individually, the total travel distance associated with the work plan was 2,358 m (optimized result using the V method) and 2,320 m (optimized result using the P method). The total travel distance in the work plan before optimization was 2,406 m, and the reduction rate using the V method was 3.9%, and the reduction rate using the P method was 7.0%.

[0078] First, an example of a work plan optimized using the P method is shown in Figure 20(a). In this case, the order of the path numbers, which are input variables, was 1, 2, 3, 4, 7, 15, 8, 5, 6, 9, 10, 11, 12, 13, 14, and 16. The total travel distance was 2,320 m.

[0079] Next, this work plan was divided into partial plan A "1, 2, 3, 4," partial plan B "7, 15, 8, 5, 6," and partial plan C "9, 10, 11, 12, 13, 14, 16." Each of partial plans A to C was optimized using both the P method and the V method to minimize the total travel distance. The results of the path number sequence optimized using each method are shown in Figure 20(b).

[0080] 21 is a diagram for explaining a preferred example of dividing a work plan. It is preferable that the work plan satisfies the following two conditions.

[0081] The first condition is that the division position should be the boundary between units when creating a plan using the P method. The boundary between task units in this case is shown in Figure 21. A unit is a grouping of task numbers created during the planning process so that a single collection and return task can be completed in one trip. Even if units are swapped, it is possible to complete all tasks in one trip, and it is also possible to reverse the order of task numbers within a unit.

[0082] The second condition is that the divided groups must all contain orders that are in the same aisle. The aisle numbers of the orders contained in each of the divided partial plans A to C are shown on the right side of Figure 21.

[0083] Figure 22(a) shows an example of the results of optimizing the combination of aisle number sequence groups to minimize the total travel distance. Figure 22(b) shows an example of the resulting work plan. The total travel distance in this plan is 2,314 m, which is a further 0.5% reduction compared to the P method.

[0084] In the above example, the work plan obtained by optimization using the P method is divided into a plurality of partial plans, but this is not limiting. For example, the work plan obtained by optimization using the V method may be divided into a plurality of partial plans.

[0085] According to this embodiment, either optimization by the P method or optimization by the V method is executed. The obtained work plan is divided into a plurality of partial plans, and both optimization by the P method and optimization by the V method are executed for each of the plurality of partial plans. The work plan is formulated by combining the optimization results for the plurality of partial plans. With this configuration, it is possible to formulate a more efficient collection and return work compared to when either the P method or the V method is executed alone.

[0086] In the above example, a product case is an example of an item. A product shelf is an example of a product shelf. The product number of each product case is an example of an identifier for identifying the product shelf on which it is stored. The traveling machine 201 is an example of a mobile body. Collecting the product cases is an example of a first task of placing multiple first items identified by identifiers from a product shelf onto a mobile body. Returning the product cases is an example of a second task of placing multiple second items placed on the mobile body onto a product shelf identified by identifiers specified for the items. The P method is an example of a first search that searches for a work plan to reduce the total movement distance in the direction in which the aisle extends. The V method is an example of a second search that searches for a work plan to reduce the total movement distance between multiple rows.

[0087] The dividing unit 60 is an example of a dividing unit that divides a work plan obtained by performing either a first search or a second search for a work plan in which a mobile object performs a first task and a second task on a specified item among a plurality of items into a plurality of partial plans. The first optimization unit 40 and the second optimization unit 50 are examples of an executing unit that performs both the first search and the second search for each of the plurality of partial plans. The third optimization unit 70 is an example of a planning unit that formulates a work plan by combining the results of the first search and the second search for the plurality of partial plans.

[0088] 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 changes are possible within the scope of the gist of the present invention as described in the claims.

[0089] REFERENCE SIGNS LIST 10 Layout storage unit 20 Order storage unit 30 Order creation unit 40 First optimization unit 50 Second optimization unit 60 Division unit 70 Third optimization unit 80 Output unit 100 Information processing device 101 CPU 102 RAM 103 Storage device 104 Input device 105 Display device 201 Traveling machine 202 Product shelf 203 Picking work area

Claims

1. With respect to a plurality of items, a plurality of rows each including a plurality of item shelves, and a plurality of aisles provided between the plurality of rows, an identifier is defined which indicates a correspondence between each of the plurality of items and each of the plurality of item shelves, and a mobile body having a loading space for loading the items moves along any one of the plurality of aisles as an outbound route and along any one of the plurality of aisles as a return route, and performs at least one of a first task of loading a plurality of first items identified by the identifiers from the item shelves onto the mobile body, and a second task of loading a plurality of second items loaded onto the mobile body onto item shelves identified by the identifiers defined for the items, On the computer, a process of performing either a first search for searching a work plan in which the moving body performs the first work and the second work on a specified item among the plurality of items, so as to reduce a total moving distance in a direction in which the passage extends, or a second search for searching a work plan for reducing a total moving distance between the plurality of rows, and dividing the searched work plan into a plurality of partial plans; performing both the first search and the second search for each of the plurality of partial plans; and a process of creating a work plan by combining results of the first search and the second search for the plurality of partial plans.

2. 2. The work plan creation program according to claim 1, wherein the plurality of partial plans are delimited by work units that enable the second work to be performed and the first work to be performed with one forward movement and one return movement.

3. 3. The work plan creation program according to claim 1, wherein an upper limit is set for the number of the articles that can be placed in the placement space at the same time.

4. 3. The work plan creation program according to claim 1 or 2, wherein, under the conditions, each item shelf stores a plurality of items, and both the first task and the second task can be performed on the same item shelf.

5. 3. The work plan creation program according to claim 1, wherein the condition is that the article is a product case in which a plurality of products of the same type are stored.

6. With respect to a plurality of items, a plurality of rows each including a plurality of item shelves, and a plurality of aisles provided between the plurality of rows, an identifier is defined which indicates a correspondence between each of the plurality of items and each of the plurality of item shelves, and a mobile body having a loading space for loading the items moves along any one of the plurality of aisles as an outbound route and along any one of the plurality of aisles as a return route, and performs at least one of a first task of loading a plurality of first items identified by the identifiers from the item shelves onto the mobile body, and a second task of loading a plurality of second items loaded onto the mobile body onto item shelves identified by the identifiers defined for the items, performing either a first search for searching a work plan in which the moving body performs the first work and the second work on a specified item among the plurality of items, the first search being for searching a work plan so as to reduce a total moving distance in a direction in which the passage extends, or a second search being for searching a work plan so as to reduce a total moving distance between the plurality of rows, and dividing the searched work plan into a plurality of partial plans; performing both the first search and the second search for each of the plurality of partial plans; creating a work plan by combining results of the first search and the second search for the plurality of partial plans; A work planning method, comprising the steps of:

7. With respect to a plurality of items, a plurality of rows each including a plurality of item shelves, and a plurality of aisles provided between the plurality of rows, an identifier is defined which indicates a correspondence between each of the plurality of items and each of the plurality of item shelves, and a mobile body having a loading space for loading the items moves along any one of the plurality of aisles as an outbound route and along any one of the plurality of aisles as a return route, and performs at least one of a first task of loading a plurality of first items identified by the identifiers from the item shelves onto the mobile body, and a second task of loading a plurality of second items loaded onto the mobile body onto item shelves identified by the identifiers defined for the items, a division unit that performs either a first search for a work plan in which the moving body performs the first work and the second work on a specified item among the plurality of items, the first search being for searching a work plan so as to reduce a total moving distance in a direction in which the passage extends, or a second search being for searching a work plan so as to reduce a total moving distance between the plurality of rows, and divides the searched work plan into a plurality of partial plans; an execution unit that executes both the first search and the second search for each of the plurality of partial plans; a planning unit that plans a work plan by combining results of the first search and the second search for the plurality of partial plans.