Transportation planning method, transportation planning device, and program

Abstract

To provide a transportation planning method capable of efficiently making a transportation plan of a product having high transportation efficiency.SOLUTION: A transportation planning method for creating a transportation plan when a transportation vehicle V1 transports products in pallets from a first base S1 to a second base S2, comprises at least one of: a first optimizing step of minimizing the number of transportation vehicles V1 when transporting a minimum number of incoming pallets required to maintain a minimum number of inventory days in the second base S2 by using a first mathematical optimizing model; and a second optimizing step of maximizing the number of pallets when transporting pallets by a predetermined number of transportation vehicles V1 by using a second mathematical optimizing model.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to a transportation planning method, a transportation planning device, and a program. [Background technology]

[0002] Conventionally, there are known techniques related to transportation planning when transporting products to bases such as warehouses. For example, Patent Document 1 discloses a transportation planning program, a transportation planning method, and a transportation planning device that aim to reduce logistics costs, including inventory costs and transportation and delivery costs, in planning the transportation of goods to warehouses. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-020885 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the conventional technology described in Patent Document 1, sufficient consideration has not been given to efficiently formulating a transportation plan for a product with high transportation efficiency with a small number of steps.

[0005] The present disclosure provides a technology that can efficiently formulate transportation plans for products with high transportation efficiency. [Means for solving the problem]

[0006] A transportation planning method according to an embodiment of the present disclosure includes: (1) A transportation planning method for generating a transportation plan for transporting products on pallets from a first location to a second location by a transportation vehicle, comprising: a first optimization step of minimizing the number of transport vehicles when transporting the minimum number of pallets required to maintain a minimum inventory day at the second base using a first mathematical optimization model; and a second optimization step of maximizing the number of pallets when transporting the pallets using a predetermined number of the transport vehicles using a second mathematical optimization model; It includes at least one of the following. This allows efficient formulation of transportation plans for products with high transportation efficiency.

[0007] (2) The transportation planning method according to (1) above, The method may include the first optimization step and the second optimization step following the first optimization step, and the number determined in the second optimization step may be the number minimized in the first optimization step. As a result, the transportation planning device that executes the transportation planning method significantly achieves the above-mentioned effect of being able to efficiently formulate transportation plans for products with high transportation efficiency. The transportation planning device can provide users with transportation plans that can be more fully utilized in actual work in terms of both formulation time and formulation content.

[0008] (3) The transportation planning method according to (1) or (2) above, In the first optimization step, the minimum number of items to be received may be calculated for each product based on the minimum inventory days, the inventory quantity on the previous day, the planned number of items to be shipped on that day, the average planned number of items to be shipped in the future, and the number of products that can be placed on one pallet at the second location. This allows the transportation planning device to more accurately minimize the required number of first transportation vehicles by using constraints based on the minimum number of items to be delivered, such as those shown in Equation 5. Therefore, the transportation planning device can improve the accuracy of the resulting transportation plan.

[0009] (4) The transportation planning method according to any one of (1) to (3) above, In the first optimization step, the number may be minimized based on the size of each type of pallet and the size of the transport vehicle in addition to the minimum number of incoming pallets. This allows the transportation planning device to more accurately minimize the required number of first transport vehicles by using constraints based on the size of each type of pallet and the size of the first transport vehicle, for example, as shown in Equation 4. Therefore, the transportation planning device can improve the accuracy of the resulting transportation plan.

[0010] (5) The transportation planning method according to any one of (1) to (4) above, In the second optimization step, the maximum number of pallets arriving at the second location may be calculated for each product based on the maximum inventory days for the product at the second location, the inventory quantity on the previous day, the planned number of shipments for the day, the average planned number of shipments in the future, and the number of products that can be placed on one pallet. This allows the transportation planning device to more accurately maximize the number of pallets by using constraints based on the maximum number of items to be received, such as those shown in Equation 9. Therefore, the transportation planning device can improve the accuracy of the resulting transportation plan.

[0011] (6) The transportation planning method according to (5) above, In the second optimization step, the total number of pallets for each product may be maximized based on a constraint that the total number of pallets is equal to or greater than the minimum number of pallets received and equal to or less than the maximum number of pallets received. This allows the transportation planning device to set an upper limit on the number of pallets that can be received for each piece of product information so that only products with certain product information are not stored unevenly at the second base.The transportation planning device can provide the user with a transportation plan that allows as many products with various product information as possible to be loaded onto multiple pallets.

[0012] (7) The transportation planning method according to any one of (1) to (6) above, The first mathematical optimization model may include a model based on a bin packing problem. This allows the transportation planning device to accurately minimize the required number of first transport vehicles. The transportation planning device can accurately optimize the required number of first transport vehicles. Therefore, the transportation planning device can efficiently and accurately formulate transportation plans for products with high transportation efficiency.

[0013] (8) The transportation planning method according to any one of (1) to (7), The second mathematical optimization model may include a model based on a dual problem of bin packing. This allows the transportation planning device to accurately maximize the number of pallets.The transportation planning device can accurately optimize the number of pallets.Therefore, the transportation planning device can efficiently and accurately formulate transportation plans for products with high transportation efficiency.

[0014] A transportation planning device according to an embodiment of the present disclosure includes: (9) A transportation planning device that generates a transportation plan for when products are packed on pallets and transported by a transportation vehicle from a first location to a second location, A control unit is provided, the control unit a first optimization process that minimizes the number of transport vehicles when transporting the minimum number of pallets required to maintain a minimum inventory days at the second base using a first mathematical optimization model; and a second optimization process for maximizing the number of pallets when transporting the pallets using a predetermined number of the transport vehicles using a second mathematical optimization model; At least one of the following is executed. This allows efficient formulation of transportation plans for products with high transportation efficiency.

[0015] A program according to an embodiment of the present disclosure includes: (10) a transportation planning device that generates a transportation plan for when products are packed on pallets and transported by a transportation vehicle from a first location to a second location; a first optimization step of minimizing the number of transport vehicles when transporting the minimum number of pallets required to maintain a minimum inventory day at the second base using a first mathematical optimization model; and a second optimization step of maximizing the number of pallets when transporting the pallets using a predetermined number of the transport vehicles using a second mathematical optimization model; and causing the device to execute an operation including at least one of the above. This allows efficient formulation of transportation plans for products with high transportation efficiency. [Effects of the Invention]

[0016] According to a transportation planning method, a transportation planning device, and a program according to an embodiment of the present disclosure, a transportation plan for a product with high transportation efficiency can be efficiently formulated. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a schematic diagram for explaining an overview of a transportation plan generated by a transportation planning device according to an embodiment of the present disclosure. FIG. [Figure 2] 1 is a block diagram illustrating a schematic configuration of a transportation planning device according to an embodiment of the present disclosure. [Figure 3] 3 is a flowchart illustrating an example of a transportation planning method executed by the transportation planning device of FIG. 2. [Figure 4] FIG. 3 is a first diagram for explaining an example of processing executed by the transportation planning device of FIG. 2. [Figure 5] FIG. 2 is a second diagram for explaining an example of processing executed by the transportation planning device of FIG. [Figure 6] FIG. 3 is a third diagram for explaining an example of processing executed by the transportation planning device of FIG. [Figure 7] FIG. 4 is a fourth diagram for explaining an example of processing executed by the transportation planning device of FIG. [Figure 8] FIG. 5 is a fifth diagram for explaining an example of a process executed by the transportation planning device of FIG. DETAILED DESCRIPTION OF THE INVENTION

[0018] An embodiment of the present disclosure will be mainly described below with reference to the accompanying drawings. The following description also applies to a transportation planning method and a program executed by a transportation planning device 10 to which the present disclosure is applied.

[0019] 1 is a schematic diagram for explaining an overview of a transportation plan generated by a transportation planning device 10 according to an embodiment of the present disclosure. With reference to FIG. 1, an overview of a transportation plan that is the subject of calculation processing by the transportation planning device 10, which will be described later, will be explained.

[0020] In the manufacturing industry, it is common to store product inventory not only in one's own factory but also in an intermediate warehouse close to the delivery destination. In this disclosure, a case is assumed in which product inventory is replenished from a first location S1 to a second location S2. The series of steps involved in this is called product transportation. In this disclosure, the "first location S1" includes, for example, one's own factory. The "second location S2" includes, for example, an intermediate warehouse. The "product" includes, for example, tires.

[0021] As an example, the first transport vehicle V1 transports products bundled on a pallet from the first location S1 to the second location S2. In this disclosure, the "first transport vehicle V1" includes, for example, a truck. A "pallet" includes, for example, any platform for transportation that can carry multiple products together. The first transport vehicle V1 transports the products from the first location S1 to the second location S2, thereby shipping the products. In this disclosure, "shipping" means, for example, transporting products from the first location S1 to the second location S2. "Receiving" means, for example, receiving products transported from the first location S1 at the second location S2.

[0022] The second transport vehicle V2 transports the products stored at the second base S2 from the second base S2 to the third base S3. In this disclosure, the "second transport vehicle V2" includes, for example, a truck. The "third base S3" includes, for example, a delivery destination of the products stored at the second base S2. The second transport vehicle V2 may be the same vehicle as the first transport vehicle V1, or may be a different vehicle. The products are sold by the second transport vehicle V2 transporting them from the second base S2 to the third base S3.

[0023] The transportation planning device 10 generates a transportation plan for when a first transportation vehicle V1 transports products bundled on pallets from a first location S1 to a second location S2. In the present disclosure, a "transportation plan" includes, for example, a plan that defines the details of product transportation when transporting products bundled on pallets from the first location S1 to the second location S2. The transportation plan includes, for example, the number of first transportation vehicles V1 required to transport the products and the number of pallets to be transported. Without being limited to these, the transportation plan may also include any other information, such as a product transportation schedule and the number of products in the product transportation.

[0024] 2 is a block diagram showing a schematic configuration of a transportation planning device 10 according to an embodiment of the present disclosure. An example of the configuration of the transportation planning device 10 will be mainly described with reference to FIG. 2. The transportation planning device 10 includes a communication unit 11, a storage unit 12, an input unit 13, an output unit 14, and a control unit 15.

[0025] The transportation planning device 10 includes, for example, any general-purpose electronic device such as a personal computer (PC), tablet PC, smartphone, or smartwatch used by a user of a manufacturer who transports products based on a transportation plan generated by the transportation planning device 10. Without being limited to these, the transportation planning device 10 may include one or more server devices that can communicate with each other, or may include other electronic devices dedicated to generating transportation plans.

[0026] In one embodiment, the transportation planning device 10 executes a first optimization process using a first mathematical optimization model to minimize the number of first transport vehicles V1 required to transport the minimum number of pallets required to maintain the minimum inventory days at the second location S2. In this disclosure, "inventory days" refers to, for example, the number of products stored at the second location S2 divided by the average daily value obtained by dividing the total number of products scheduled to be shipped over a predetermined number of days by the predetermined number of days. "Shipping" refers to, for example, transporting products from the second location S2 to a third location S3. The "first mathematical optimization model" includes, for example, a model based on a bin packing problem.

[0027] The transportation planning device 10 executes a second optimization process using a second mathematical optimization model to maximize the number of pallets to be transported by a predetermined number of first transport vehicles V1. In the present disclosure, the "second mathematical optimization model" includes, for example, a model based on a dual problem of bin packing.

[0028] The transportation planning device 10 may execute both the first optimization process and the second optimization process, or may execute only one of them.

[0029] The communication unit 11 includes one or more communication interfaces communicatively connected to networks including a mobile communication network and the Internet. The communication interfaces are compatible with mobile communication standards such as 4G (4th Generation) and 5G (5th Generation), wired LAN (Local Area Network) standards, or wireless LAN standards, but are not limited thereto and may be compatible with any communication standard. For example, the communication interface may also be compatible with a short-range wireless communication standard. In one embodiment, the transportation planning device 10 is communicatively connected to a network via the communication unit 11. The communication unit 11 transmits and receives various information via the network.

[0030] The storage unit 12 includes storage modules such as a hard disk drive (HDD), a solid state drive (SSD), an electrically erasable programmable read-only memory (EEPROM), a read-only memory (ROM), and a random access memory (RAM). The storage unit 12 stores information necessary to realize the operation of the transportation planning device 10. The storage unit 12 stores information obtained by the operation of the transportation planning device 10. For example, the storage unit 12 stores system programs, application programs, and various data acquired by any means such as communication.

[0031] The storage unit 12 may function as a main storage module, an auxiliary storage module, or a cache memory. The storage unit 12 is not limited to being built into the transportation planning device 10, and may include an external storage module connected via a digital input / output port such as a Universal Serial Bus (USB).

[0032] The input unit 13 includes one or more input interfaces that detect user input and acquire input information based on the user's operation. The input interfaces include physical keys, capacitive keys, a touch screen that is integrated with the display of the output unit 14, an imaging module such as a camera, and a microphone that accepts audio input.

[0033] The output unit 14 includes one or more output interfaces that output information to provide it to the user, such as a display that visually outputs information as an image, a speaker that audibly outputs information as a sound, and a vibrator that tactilely outputs information as a vibration.

[0034] The control unit 15 includes one or more processors. In this disclosure, a "processor" refers to, but is not limited to, a general-purpose processor or a dedicated processor specialized for a specific process. The control unit 15 includes, for example, a CPU (Central Processing Unit). The control unit 15 is communicably connected to each component of the transportation planning device 10 and controls the overall operation of the transportation planning device 10.

[0035] Figure 3 is a flowchart for explaining an example of a transportation planning method executed by the transportation planning device 10 of Figure 2. The example of the transportation planning method executed by the transportation planning device 10 of Figure 2 will be mainly explained with reference to Figure 3. The flowchart shown in Figure 3 shows the basic processing flow of the transportation planning method executed by the transportation planning device 10.

[0036] In step S101, the control unit 15 of the transportation planning device 10 acquires information. For example, the control unit 15 may acquire the information based on an input operation by a user or the like using the input unit 13, or may acquire the information from another device different from the transportation planning device 10 through information communication using the communication unit 11. The control unit 15 stores the acquired information in the storage unit 12.

[0037] In this disclosure, "information" includes, for example, fixed parameters determined in advance when formulating a transportation plan and setting parameters determined by a user. "Fixed parameters" include, for example, product information such as the product code and size of the product, the first shipping location S1, the second shipping location S2, the type of pallet used for transportation, the pallet size for each pallet type, the number of products that can be loaded on one pallet, the inventory quantity at the second shipping location S2 the previous day, the planned number of products to be shipped from the second shipping location S2 today, and the average planned number of products to be shipped from the second shipping location S2 in the future. The "size" of the pallet includes, for example, its depth. Without being limited thereto, "size" may also include its width and height. "Setting parameters" include, for example, the minimum inventory days, maximum inventory days, and upper limit of product storage quantity at the second shipping location S2, as well as the size of the first transportation vehicle V1 used to transport the products. The "size" of the first transportation vehicle V1 includes, for example, its depth. Without being limited thereto, "size" may also include its width and height, corresponding to the pallet.

[0038] In step S102, the control unit 15 of the transportation planning device 10 calculates the minimum number of pallets required to maintain the minimum inventory days at the second location S2 based on the information acquired in step S101. The control unit 15 calculates the minimum number of pallets required to maintain the minimum inventory days at the second location S2 for each product based on the minimum inventory days, the inventory quantity on the previous day, the planned number of items to be shipped on that day, the average planned number of items to be shipped in the future, and the number of products that can be placed on one pallet. The control unit 15 calculates the minimum number of pallets required to be shipped y_min based on, for example, the following formula 1: i Calculate.

[0039]

number

[0040] In step S103, the control unit 15 of the transportation planning device 10 executes a first optimization process using a first mathematical optimization model to minimize the number of first transport vehicles V1 required to transport the minimum number of pallets required to maintain the minimum inventory days at the second location S2. The control unit 15 executes the first optimization process based on the minimum number of pallets calculated in step S102. At this time, the control unit 15 minimizes the number of first transport vehicles V1 based on the size of each type of pallet and the size of the first transport vehicle V1 in addition to the minimum number of pallets.

[0041] Steps S102 and S103 correspond to the first optimization step described in the claims.

[0042] In step S104, the control unit 15 of the transportation planning device 10 calculates the maximum number of pallets to be received at the second location S2 based on the information acquired in step S101. The control unit 15 calculates the maximum number of pallets to be received at the second location S2 for each product based on the maximum number of days of inventory for the product, the number of inventory items on the previous day, the number of items scheduled to be shipped on that day, the average number of items scheduled to be shipped in the past, and the number of products that can be placed on one pallet at the second location S2. The control unit 15 calculates the maximum number of pallets to be received at the second location S2, y_max, for example, based on the following formula 2: i Calculate.

[0043]

number

[0044] In step S105, the control unit 15 of the transportation planning device 10 executes a second optimization process using a second mathematical optimization model to maximize the number of pallets to be transported by the specified number of first transport vehicles V1. The control unit 15 executes the second optimization process based on the maximum number of pallets to be shipped calculated in step S104. At this time, the control unit 15 maximizes the number of pallets based on a constraint that, for example, the total number of pallets for each product is greater than or equal to the minimum number of pallets to be shipped and less than or equal to the maximum number of pallets to be shipped.

[0045] Steps S104 and S105 correspond to the second optimization step described in the claims. As described above, the transportation planning method includes a first optimization step and a second optimization step subsequent to the first optimization step. In step S105 included in the second optimization step, the determined number is the number of first transportation vehicles V1 minimized in step S103 included in the first optimization step.

[0046] In step S106, the control unit 15 of the transportation planning device 10 calculates the optimal number of products to be delivered to the second location S2 by multiplying the number of pallets maximized in step S105 by the number of products that can be placed on one pallet.

[0047] In step S107, the control unit 15 of the transportation planning device 10 outputs the calculation result of at least one of the calculation processes in steps S102 to S106 via the output unit 14 as necessary.

[0048] Figure 4 is a first diagram for explaining an example of processing executed by the transportation planning device 10 of Figure 2. Figure 4 conceptually shows how some of the information acquired in step S101 of Figure 3 is stored as data in the storage unit 12 in an associated state. Figure 4 conceptually shows how, of the information acquired in step S101 of Figure 3, the pallet types and pallet sizes for each pallet type are associated with each other.

[0049] As shown in FIG. 4, for example, a first numerical value of size is associated with pallet type A. A second numerical value of size is associated with pallet type B. A third numerical value of size is associated with pallet type C. The first to third numerical values ​​may be different from each other, or may be at least partially the same. For ease of explanation, FIG. 4 illustrates an example in which the pallet types include only three types, A, B, and C, but this is not limiting. In the data shown in FIG. 4, the number of pallet types may be any number other than three.

[0050] Fig. 5 is a second diagram for explaining an example of processing executed by the transportation planning device 10 of Fig. 2. Fig. 5 conceptually illustrates how some of the information acquired in step S101 of Fig. 3 is stored as data in the storage unit 12 in an associated state. Fig. 5 conceptually illustrates how, of the information acquired in step S101 of Fig. 3, the first base station S1, the second base station S2, and the size of the first transport vehicle V1 are associated with each other.

[0051] 5, for example, a first numerical value indicating the size of a first transportation vehicle V1 that transports products from a first location S1 to a second location S2 that is 0 is associated with the first location S1 and the second location S2. A second numerical value indicating the size of a first transportation vehicle V1 that transports products from the first location S1 to a second location S2 that is 0 is associated with the first location S1 and the second location S2. A third numerical value indicating the size of a first transportation vehicle V1 that transports products from the first location S1 to a second location S2 that is 0 is associated with the first location S1 and the second location S2. The first to third numerical values ​​may be different from each other, or at least some of them may be the same.

[0052] For ease of explanation, Fig. 5 illustrates an example in which the second location S2 includes only one location (0) and the first location S1 includes only three locations (a, b, and c), but this is not limiting. In the data illustrated in Fig. 5, the number of second locations S2 may be two or more. The number of first locations S1 may be any number other than three.

[0053] Fig. 6 is a third diagram for explaining an example of processing executed by the transportation planning device 10 of Fig. 2. Fig. 7 is a fourth diagram for explaining an example of processing executed by the transportation planning device 10 of Fig. 2. Figs. 6 and 7 conceptually show data used in the first optimization processing related to minimizing the number of first transportation vehicles V1 in the first optimization step, which was explained using Fig. 3.

[0054] As shown in FIG. 6, the control unit 15 of the transportation planning device 10 calculates the minimum number of items to be delivered y_min for each product size i using the corresponding pallet type and Equation 1. i The control unit 15 associates the minimum number of stocks y_min for each pallet type. i Aggregate the total value Y_MIN type For example, the control unit 15 calculates the minimum stock quantity y_min associated with product size i of 0, . . . , n-1, n. i From the sum of Y_MIN of palette type A type The control unit 15 calculates the minimum stock quantity y_min associated with the product size i from 1, . i From the sum of Y_MIN of palette type B type Calculate.

[0055] As shown in FIG. 7, the control unit 15 of the transportation planning device 10 associates, for each row number t of the first transport vehicle V1, the number of pallets to be transported in the corresponding row across all pallet types. The "row" in the row number t refers to, for example, the row when pallets are lined up on the loading platform of the first transport vehicle V1 along the traveling direction of the first transport vehicle V1. A single first transport vehicle V1 may have one or more rows. The control unit 15 associates, for each row number t, whether or not the corresponding row will be used in the transportation plan. For example, the control unit 15 assigns a variable 1 to a row that will be used and a variable 0 to a row that will not be used.

[0056] The control unit 15 minimizes the number of columns to be used as a bin packing problem using the objective function shown in the following equation 3, for example.

number

[0057] The control unit 15 uses the following formula: t The control unit 15 minimizes the sum of the number of rows across the row number t of the first transport vehicles V1. The control unit 15 divides the minimized sum of the number of rows by the number of rows per first transport vehicle V1, thereby minimizing the number of first transport vehicles V1 when transporting the minimum number of pallets required to maintain the minimum inventory days at the second base S2.

[0058] When the control unit 15 executes the first optimization process using the objective function based on Equation 3, the control unit 15 also uses some constraint conditions in addition to the objective function. For example, the control unit 15 uses the first constraint condition shown in Equation 4 below.

[0059]

number

[0060] For example, the control unit 15 uses the second constraint condition shown in the following equation 5.

number

[0061] For example, the control unit 15 uses a third constraint condition in addition to the first and second constraint conditions. t is a variable of 0 or 1, and on type,t is an integer greater than or equal to 0.

[0062] Fig. 8 is a fifth diagram for explaining an example of processing executed by the transportation planning device 10 of Fig. 2. Fig. 8 conceptually illustrates data used in the second optimization processing related to maximizing the number of pallets in the second optimization step, which was explained using Fig. 3.

[0063] As shown in FIG. 8, the control unit 15 of the transportation planning device 10 calculates, for each row number j, the size i of the corresponding product, the column number t, and the inventory quantity z of the previous day. j , and the number of items expected to be shipped on that day h j and the number of pallets to be loaded on the corresponding row number t, y jare associated with each other. Row number j is a newly assigned column number of the first transport vehicle V1 on which a pallet containing products of size i is to be loaded. For each product size i, there are provided rows equal to the number of columns of the first transport vehicle V1 on which products of size i are to be loaded. Row number j is a consecutive number. The number of row numbers j, i.e., the number of rows in Figure 8, corresponds to the sum of the number of columns of the first transport vehicle V1 corresponding to each product size i. For example, for product size 0, 10 columns of the first transport vehicle V1, from column numbers 0 to 9, are associated with row numbers 0 to 9, respectively.

[0064] Number of pallets y j is the number of pallets containing products of size i that are loaded in the column of column number t of the first transport vehicle V1 corresponding to row number j. j The objective function shown in Equation 6 below is used to maximize the total number of pallets loaded as a dual problem to bin packing.

[0065]

number

[0066] The control unit 15 calculates y j The control unit 15 maximizes the sum of the maximized y j The optimum number of products to be delivered to the second base S2 is calculated by multiplying the total by the number of products that can be placed on one pallet.

[0067] When the control unit 15 executes the second optimization process using the objective function based on Equation 6, the control unit 15 also uses some constraints in addition to the objective function. For example, the control unit 15 uses the fourth constraint shown in Equation 7 below.

[0068]

number

[0069] For example, the control unit 15 uses the fifth constraint condition shown in the following equation 8.

number

[0070] For example, the control unit 15 uses the sixth constraint condition shown in the following equation 9.

number

[0071] According to the above-described embodiment, a transportation plan for a product with high transportation efficiency can be efficiently formulated. The transportation planning device 10 executes at least one of a first optimization process and a second optimization process. In the first optimization process, the transportation planning device 10 uses a first mathematical optimization model to minimize the number of first transportation vehicles V1 when transporting the minimum number of pallets required to maintain the minimum inventory days at the second location S2. In the second optimization process, the transportation planning device 10 uses a second mathematical optimization model to maximize the number of pallets when transporting pallets using a specified number of first transportation vehicles V1.

[0072] This allows the transportation planning device 10 to optimize the number of first transport vehicles V1 required and the number of pallets to be loaded when the first transport vehicles V1 transport products of various sizes on pallets to the second base S2. The transportation planning device 10 can formulate a transportation plan with high transportation efficiency that enables the first transport vehicles V1 to transport the largest number of pallets with the fewest number of vehicles. The transportation planning device 10 can improve the accuracy of the resulting transportation plan by executing at least one of the first optimization process and the second optimization process.

[0073] In addition, the transportation planning device 10 can automatically formulate such transportation plans by applying mathematical optimization techniques to perform calculations. Therefore, users can reduce the amount of work time previously spent optimizing the numerical values ​​in transportation plans through automation using the transportation planning device 10. The man-hours involved in formulating a transportation plan are reduced. As a result, the time required to formulate one transportation plan is reduced. In addition, the accuracy of the transportation plan is further improved. The transportation planning device 10 can provide users with transportation plans that are fully usable in actual operations in terms of both formulation time and content.

[0074] The transportation planning device 10 executes calculation processing in accordance with a first optimization step and a second optimization step subsequent to the first optimization step. As a result, the transportation planning device 10 significantly achieves the aforementioned effect of being able to efficiently formulate transportation plans for products with high transportation efficiency. The transportation planning device 10 can provide users with transportation plans that can be fully utilized in actual operations in terms of both formulation time and content.

[0075] In the first optimization step, the transportation planning device 10 calculates the minimum number of products to be received for each product at the second base S2 based on the minimum inventory days, the inventory quantity on the previous day, the planned number of items to be shipped on that day, the average planned number of items to be shipped in the future, and the number of products that can be placed on one pallet. This allows the transportation planning device 10 to more accurately minimize the required number of first transport vehicles V1 using constraints based on the minimum number of items to be received, such as those shown in Equation 5. Therefore, the transportation planning device 10 can improve the accuracy of the resulting transportation plan.

[0076] In the first optimization step, the transportation planning device 10 minimizes the number of first transportation vehicles V1 based on the size of each type of pallet and the size of the first transportation vehicle V1 in addition to the minimum number of incoming pallets. This allows the transportation planning device 10 to more accurately minimize the required number of first transportation vehicles V1 using constraints based on the size of each type of pallet and the size of the first transportation vehicle V1, for example, as shown in Equation 4. Therefore, the transportation planning device 10 can improve the accuracy of the resulting transportation plan.

[0077] In the second optimization step, the transportation planning device 10 calculates the maximum number of pallets to be received at the second location S2 for each product based on the product's maximum inventory days, the inventory quantity on the previous day, the planned number of deliveries for that day, the average planned number of deliveries in the past, and the number of products that can be placed on one pallet at the second location S2. This allows the transportation planning device 10 to more accurately maximize the number of pallets by using constraints based on the maximum number of deliveries, such as those shown in Equation 9. Therefore, the transportation planning device 10 can improve the accuracy of the resulting transportation plan.

[0078] In the second optimization step, the transportation planning device 10 maximizes the number of pallets based on a sixth constraint that the total number of pallets for each product is equal to or greater than the minimum number of incoming pallets and equal to or less than the maximum number of incoming pallets. This enables the transportation planning device 10 to set an upper limit on the number of incoming pallets for each product information i so that products with specific product information i are not stored disproportionately at the second location S2. The transportation planning device 10 can provide the user with a transportation plan that loads as many products with various product information i as possible onto multiple pallets. For example, the transportation planning device 10 can also be applied to generating a transportation plan that loads as many products with multiple different product information i as possible onto a single pallet.

[0079] The first mathematical optimization model includes a model based on the bin packing problem. This allows the transportation planning device 10 to accurately minimize the required number of first transportation vehicles V1. The transportation planning device 10 can accurately optimize the required number of first transportation vehicles V1. Therefore, the transportation planning device 10 can efficiently and accurately formulate transportation plans for products with high transportation efficiency.

[0080] The second mathematical optimization model includes a model based on the dual problem of bin packing. This enables the transportation planning device 10 to accurately maximize the number of pallets. The transportation planning device 10 can accurately optimize the number of pallets. Therefore, the transportation planning device 10 can efficiently and accurately formulate transportation plans for products with high transportation efficiency.

[0081] Although the present disclosure has been described based on the drawings and examples, it should be noted that those skilled in the art can make various modifications and changes based on the present disclosure. Therefore, it should be noted that these modifications and changes are included in the scope of the present disclosure. For example, the functions included in each configuration or step can be rearranged so as not to be logically inconsistent, and multiple configurations or steps can be combined or divided into one.

[0082] For example, a general-purpose electronic device such as a smartphone or a computer can be configured to function as the transportation planning device 10 according to the embodiment. Specifically, a program describing the processing content for realizing each function of the transportation planning device 10 according to the embodiment is stored in the memory of the electronic device, and the program is read and executed by a processor of the electronic device. Therefore, the present disclosure can also be realized as a program executable by a processor.

[0083] Alternatively, the present disclosure may be realized as a non-transitory computer-readable medium storing a program that can be executed by one or more processors to cause the transportation planning device 10 according to one embodiment to execute each function, etc. It should be understood that these are also included within the scope of the present disclosure.

[0084] In the above embodiment, the transportation planning device 10 executes the calculation process in accordance with a first optimization step and a second optimization step subsequent to the first optimization step. However, this is not limiting. The transportation planning device 10 may execute the calculation process in accordance with only one of the first optimization step and the second optimization step. In other words, the transportation planning method may include only one of the first optimization step and the second optimization step.

[0085] In the above embodiment, the determined number in the second optimization step is the number of first transport vehicles V1 minimized in the first optimization step, but is not limited to this. The determined number may be based on any other process.

[0086] In the above embodiment, the transportation planning device 10 calculates the minimum number of items to be received for each product at the second base S2 in the first optimization step based on the minimum inventory days, the inventory quantity on the previous day, the planned number of items to be shipped on that day, the average planned number of items to be shipped in the future, and the number of products that can be placed on one pallet. However, this is not limiting. The transportation planning device 10 may calculate the minimum number of items to be received for each product based on any other combination of parameters, or may not calculate the minimum number of items to be received in the first optimization step at all.

[0087] In the above embodiment, the transportation planning device 10 minimizes the number of first transport vehicles V1 in the first optimization step based on the minimum number of pallets to be received, the size of each type of pallet, and the size of the transport vehicle. However, the transportation planning device 10 is not limited to this. The transportation planning device 10 may also minimize the number of first transport vehicles V1 based on any other combination of parameters.

[0088] In the above embodiment, the transportation planning device 10 calculates the maximum number of pallets arriving at the second location S2 for each product in the second optimization step based on the product's maximum inventory days, the inventory quantity on the previous day, the planned number of deliveries for that day, the average planned number of deliveries in the past, and the number of products that can be placed on one pallet at the second location S2. However, the present invention is not limited to this. The transportation planning device 10 may calculate the maximum number of deliveries for each product based on any other combination of parameters, or may not calculate the maximum number of deliveries in the second optimization step at all.

[0089] In the above embodiment, the transportation planning device 10 maximizes the number of pallets in the second optimization step based on the constraint that the total number of pallets for each product is equal to or greater than the minimum number of pallets received and equal to or less than the maximum number of pallets received. However, the transportation planning device 10 is not limited to this. Instead of or in addition to such a constraint, the transportation planning device 10 may maximize the number of pallets based on any other constraint.

[0090] In the above embodiment, the first mathematical optimization model is described as including a model based on the bin packing problem, but is not limited thereto. The first mathematical optimization model may also include a model based on an integer programming problem other than the bin packing problem, or a model based on any other mathematical optimization method.

[0091] In the above embodiment, the second mathematical optimization model is described as including a model based on the dual problem of bin packing, but is not limited thereto. The second mathematical optimization model may include a model based on an integer programming problem other than the dual problem of bin packing, or may include a model based on any other mathematical optimization method.

[0092] In the above embodiment, the first location S1 has been described as including, for example, the company's own factory, but is not limited to this. The first location S1 may also include any other location from which products are shipped to the second location S2. The second location S2 has been described as including, for example, an intermediate warehouse, but is not limited to this. The second location S2 may also include any other location to which products shipped from the first location S1 are received. The third location S3 has been described as including, for example, a delivery destination for products stored at the second location S2, but is not limited to this. The third location S3 may also include any other location to which products are shipped from the second location S2. The products have been described as including, for example, tires, but are not limited to this. The products may also include any other products other than tires as long as they can be transported on a pallet by the first transport vehicle V1.

[0093] In the above embodiment, the first transport vehicle V1 has been described as including, for example, a truck, but is not limited to this. The first transport vehicle V1 may also include any other vehicle capable of transporting pallets of products. The second transport vehicle V2 has been described as including, for example, a truck, but is not limited to this. The second transport vehicle V2 may also include any other vehicle capable of transporting products. [Industrial Applicability]

[0094] According to the present disclosure, it is possible to provide a transportation planning method, a transportation planning device, and a program that can efficiently formulate transportation plans for products with high transportation efficiency.

[0095] [Contribution to the United Nations-led Sustainable Development Goals (SDGs)] The SDGs have been proposed to realize a sustainable society. One embodiment of the present disclosure is believed to be a technology that can contribute to goals such as "No. 9 - Build infrastructure for industry and technological innovation," "No. 12 - Responsible consumption and production," and "No. 13 - Take concrete measures against climate change." [Explanation of symbols]

[0096] 10 Transportation planning device 11 Communications Department 12 Storage section 13 Input section 14 Output section 15 Control Unit S1 First base S2 Second base S3 Third base V1 First Transport Vehicle V2 Second Transport Vehicle

Claims

1. A transportation planning method for generating a transportation plan for transporting products on pallets from a first location to a second location by a transportation vehicle, comprising: a first optimization step of minimizing, using a first mathematical optimization model, the number of transport vehicles required to transport the minimum number of pallets required to maintain a minimum inventory day at the second base; and a second optimization step of maximizing the number of pallets when transporting the pallets using a predetermined number of the transport vehicles using a second mathematical optimization model; including at least one of Transportation planning methods.

2. The transportation planning method according to claim 1, the first optimization step and the second optimization step subsequent to the first optimization step, In the second optimization step, the determined number of vehicles is the number minimized in the first optimization step. Transportation planning methods.

3. 3. The transportation planning method according to claim 1 or 2, In the first optimization step, the minimum number of products to be received is calculated for each of the products based on the minimum inventory days, the inventory quantity on the previous day, the planned number of products to be shipped on the day, the average planned number of products to be shipped in the future, and the number of products that can be placed on one pallet at the second base. Transportation planning methods.

4. 3. The transportation planning method according to claim 1 or 2, In the first optimization step, the number is minimized based on the size of each type of pallet and the size of the transport vehicle in addition to the minimum number of incoming pallets. Transportation planning methods.

5. 3. The transportation planning method according to claim 1 or 2, In the second optimization step, a maximum number of products to be received on the pallet at the second location is calculated for each product based on the maximum inventory days of the product, the inventory quantity on the previous day, the planned number of products to be shipped on the day, the average planned number of products to be shipped in the future, and the number of products that can be placed on one pallet. Transportation planning methods.

6. The transportation planning method according to claim 5, In the second optimization step, the total number of pallets for each product is maximized based on a constraint that the total number is equal to or greater than the minimum number of pallets received and equal to or less than the maximum number of pallets received. Transportation planning methods.

7. 3. The transportation planning method according to claim 1 or 2, the first mathematical optimization model comprises a model based on a bin packing problem; Transportation planning methods.

8. 3. The transportation planning method according to claim 1 or 2, the second mathematical optimization model includes a model based on a dual problem of bin packing; Transportation planning methods.

9. A transportation planning device that generates a transportation plan for when products are packed on pallets and transported by a transportation vehicle from a first location to a second location, A control unit is provided, the control unit a first optimization process that minimizes the number of transport vehicles when transporting the minimum number of pallets required to maintain a minimum inventory days at the second base using a first mathematical optimization model; and a second optimization process for maximizing the number of pallets when transporting the pallets using a predetermined number of the transport vehicles using a second mathematical optimization model; Execute at least one of Transport planning device.

10. a transportation planning device that generates a transportation plan for when products are packed on pallets and transported by a transportation vehicle from a first location to a second location; a first optimization step of minimizing, using a first mathematical optimization model, the number of transport vehicles required to transport the minimum number of pallets required to maintain a minimum inventory day at the second base; and a second optimization step of maximizing the number of pallets when transporting the pallets using a predetermined number of the transport vehicles using a second mathematical optimization model; Execute an operation including at least one of program.

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