Planning system, planning method, and program

Abstract

To provide a planning system capable of planning where to place the cargo in consideration of future work efficiency after carrying in the cargo and changes in delivery schedule.SOLUTION: A planning system for planning where to place the cargo to be carried in an area in which arrangement lists are arranged in matrix, the arrangement lists indicating cargo locations in columns where the cargo is carried in or out from one end includes: a data acquisition unit which acquires layout information of the area, an upper limit of cargo to be arranged in one arrangement list, a plurality of scenarios indicating cargo carry-in schedule, and carry-out schedule information; a constraint condition definition unit which sets, as constraint conditions, constraint equation or the like for constraining an upper limit of the cargo to be arranged in the arrangement list; an objective function definition unit which sets, as objective function, a calculation formula for calculating the number of arrangement lists in which target cargo is arranged and a range in which the arrangement lists exist; and an arrangement plan optimization unit which calculates a cargo arrangement plan so as to satisfy the constraint conditions and minimize the objective function.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent Document 1 discloses a method for storing containers brought into a container yard by external chassis, in which, each time a container storage operation occurs, information such as the operating status of the container handling means and the number of containers stacked at each storage location is obtained, and based on this information, the optimal container storage location according to the situation is determined. According to the method of Patent Document 1, container storage operation can be carried out efficiently according to changes in the situation, making it possible to effectively utilize external chassis, improve logistics efficiency, and reduce logistics costs. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 4410515 Summary of the Invention [Problem to be solved by the invention]

[0004] Determining the storage location for a container based on the situation when the container arrives can improve work efficiency. However, if the location of each container is determined without considering the type and size of the container being carried in or out, the container locations may be dispersed, which could lead to reduced work efficiency in the future, for example, when the containers are loaded onto a ship. Furthermore, even if a plan is made to appropriately allocate containers according to the delivery schedule, if the amount of containers delivered by time slot changes, such as if the container delivery date is brought forward, other containers may still be stored in the reserved location, forcing the container to be placed in a different location, resulting in the location being dispersed.

[0005] The present disclosure provides a planning system, a planning method, and a program that can solve the above problems. [Means for solving the problem]

[0006] The planning system of the present disclosure is a planning system that plans the placement of luggage to be delivered to an area in which placement lists indicating placement locations of the luggage to be delivered and delivered in stack form are arranged in rows and columns, and includes a data acquisition unit that acquires layout information of the area, the number of luggage that can be placed on one of the placement lists, a first scenario that defines a schedule for bringing in the luggage, a second scenario that advances the schedule for bringing in the luggage compared to the first scenario, and planned delivery information of the luggage, a constraint equation that restricts the upper limit of the number of luggage to be placed on the placement list, a constraint equation that restricts that the luggage to be delivered must be placed on any of the placement lists, a constraint equation that restricts that the luggage to be delivered must be removed from any of the placement lists, and a constraint equation that restricts that the luggage to be delivered must be removed from any of the placement lists. a constraint condition definition unit that sets as constraint conditions: a constraint equation that constrains the consistency of the inventory quantities of the luggage determined based on the second scenario; and a constraint equation that constrains that the number of placement locations to place the luggage based on the second scenario in all time frames must be equal to or greater than the number of placement locations when the luggage is placed based on the first scenario; an objective function definition unit that sets as an objective function a weighted sum of: a calculation formula for calculating the number of placement lists in which the luggage to be placed is placed; and a calculation formula for calculating the number of columns in which the placement list in which the luggage to be placed is included when cargo handling equipment used to place the luggage moves in the row direction of the placement lists arranged in a matrix; and a placement plan optimization unit that calculates a placement plan for the luggage that minimizes the objective function while satisfying the constraint conditions.

[0007] The planning method of the present disclosure is a planning method for planning the placement of luggage to be delivered to an area in which placement lists indicating placement locations of the luggage to be delivered and delivered in stack form are arranged in rows and columns, and includes the steps of acquiring layout information of the area, the number of luggage that can be placed on one of the placement lists, a first scenario that defines a schedule for bringing in the luggage, a second scenario in which the schedule for bringing in the luggage is earlier than the first scenario, and delivery schedule information for the luggage, a constraint equation that restricts the upper limit of the number of luggage to be placed on the placement list, a constraint equation that restricts that the luggage to be delivered must be placed on any of the placement lists, a constraint equation that restricts that the luggage to be delivered must be delivered from any of the placement lists, and a constraint equation that restricts that the luggage to be delivered must be delivered from any of the placement lists. The method includes the steps of: setting, as constraint conditions, a constraint equation that constrains the consistency of the inventory quantity of the placed luggage; and a constraint equation that constrains that the number of placement locations where the luggage is placed based on the second scenario in all time frames must be equal to or greater than the number of placement locations when the luggage is placed based on the first scenario; setting, as an objective function, a weighted sum of a formula for calculating the number of placement lists in which the luggage to be placed is placed; and a formula for calculating the number of columns containing the placement lists in which the luggage to be placed is placed when loading and unloading equipment used to place the luggage moves in the row direction of the placement lists arranged in a matrix; and calculating a placement plan for the luggage that minimizes the objective function while satisfying the constraint conditions.

[0008] The program of the present disclosure is also a process for planning the placement of luggage to be delivered to an area in which placement lists indicating placement locations of luggage to be delivered and delivered in stack form are arranged in rows and columns, the program including steps of acquiring layout information of the area, the number of luggage that can be placed on one placement list, a first scenario that defines a schedule for bringing in the luggage, a second scenario that advances the schedule for bringing in the luggage compared to the first scenario, and delivery schedule information for the luggage, a constraint equation that restricts the upper limit of the number of luggage to be placed on the placement list, a constraint equation that restricts that the luggage to be delivered must be placed on any of the placement lists, a constraint equation that restricts that the luggage to be delivered must be delivered from any of the placement lists, and a constraint equation that restricts that the luggage to be delivered must be delivered from any of the placement lists. The system executes a process including the steps of: setting, as constraint conditions, a constraint equation that constrains the consistency of the inventory quantity of the placed luggage; and a constraint equation that constrains that the number of placement locations where the luggage is placed based on the second scenario in all time frames must be equal to or greater than the number of placement locations when the luggage is placed based on the first scenario; setting, as an objective function, a weighted sum of a formula for calculating the number of placement lists in which the luggage to be placed is placed; and a formula for calculating the number of columns in which the placement list in which the luggage to be placed is placed when the loading and unloading equipment used to place the luggage moves in the row direction in the placement lists arranged in a matrix; and calculating a placement plan for the luggage that minimizes the objective function while satisfying the constraint conditions. [Effects of the Invention]

[0009] According to the above-described planning system, planning method, and program, it is possible to plan the placement locations of cargoes taking into consideration future work efficiency and fluctuations in cargo delivery schedules. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram illustrating an example of a planning system according to an embodiment. [Figure 2A] FIG. 1 is a first diagram showing an example of a location where luggage is placed according to an embodiment. [Figure 2B] FIG. 2 is a second diagram showing an example of a location where luggage is placed according to the embodiment. [Figure 3] FIG. 10 is a diagram illustrating an example of a method for arranging luggage according to an embodiment. [Figure 4] FIG. 1 is a diagram illustrating problem modeling according to an embodiment. [Figure 5] FIG. 1 is a diagram illustrating a problem scenario according to an embodiment. [Figure 6] FIG. 10 is a diagram illustrating an example of variables used in a mathematical optimization problem according to the embodiment. [Figure 7A] FIG. 1 is a first diagram showing an example of constants used in a mathematical optimization problem according to an embodiment. [Figure 7B] FIG. 2 is a second diagram showing an example of constants used in the mathematical optimization problem according to the embodiment. [Figure 8] FIG. 10 is a diagram illustrating an example of a set used in a mathematical optimization problem according to the embodiment. [Figure 9A] FIG. 1 is a first diagram showing an example of constraint conditions used in a mathematical optimization problem according to an embodiment. [Figure 9B] FIG. 2 is a second diagram showing an example of constraints used in the mathematical optimization problem according to the embodiment. [Figure 9C] FIG. 3 is a third diagram showing an example of constraints used in the mathematical optimization problem according to the embodiment. [Figure 9D] FIG. 9D is a diagram illustrating the constraints of FIG. 9C. [Figure 9E] FIG. 4 is a fourth diagram showing an example of constraints used in the mathematical optimization problem according to the embodiment. [Figure 9F] FIG. 5 is a diagram showing an example of constraints used in the mathematical optimization problem according to the embodiment. [Figure 9G] FIG. 6 is a diagram showing an example of constraint conditions used in the mathematical optimization problem according to the embodiment. [Figure 9H] FIG. 9C is a diagram illustrating the constraints of FIG. 9G. [Figure 9I] FIG. 7 is a diagram showing an example of constraints used in the mathematical optimization problem according to the embodiment. [Figure 9J] FIG. 9B is a diagram illustrating the constraints of FIG. 9I. [Figure 10] FIG. 10 is a diagram illustrating an example of an objective function used in a mathematical optimization problem according to the embodiment. [Figure 11] 10 is a flowchart illustrating an example of a plan creation process according to the embodiment. [Figure 12] FIG. 1 is a diagram illustrating an example of a hardware configuration of a planning system according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] <Embodiment> The planning system according to each embodiment will be described in detail below with reference to FIGS. (composition) FIG. 1 is a block diagram showing an example of a planning system according to an embodiment of the present invention. In this embodiment, the planning system is configured with a computer device such as a single PC or a server device. The planning system 10 creates a cargo placement plan that takes into account future work efficiency when storing cargo delivered to a container terminal or the like at a port. More specifically, when cargo is to be placed in a cargo placement location where a row of cargo placement locations (sometimes referred to as placement lists) is arranged in a matrix format, where cargo is loaded and unloaded in a stacked format from one end, using a conveying device with high movement costs, the cargo placement plan is created taking into account which placement list the cargo should be placed in to enable future loading and unloading of cargo with minimal movement of the conveying device when loading and unloading.

[0012] As shown in FIG. 1, the planning system 10 includes a data acquisition unit 11, a planning unit 12, an output unit 13, and a storage unit 14. The data acquisition unit 11 acquires information necessary for creating a cargo placement plan, such as information on the layout of the cargo storage area (how many placement lists are set up in rows and columns, how many cargoes can be stored in one placement list, the distance from each placement list to the cargo entrance and exit at the container terminal, etc.), information on cargo to be brought in and out (the type and size of the cargo to be brought in and out, and the scheduled arrival and departure dates for the cargo), inventory information (for cargo already stored in the placement list, what type of cargo is placed where), and information such as a scenario that defines the cargo delivery schedule, and has the function of shaping the information so that it can be handled in the optimization process described below, and the function of writing and recording the acquired information and shaped information in the memory unit 14.

[0013] The planning unit 12 creates an optimal storage plan for the luggage. The planning unit 12 includes a constraint condition definition unit 121, an objective function definition unit 122, and a placement plan optimization unit 123. The constraint definition unit 121 has the function of defining seven types of constraints (described below) based on the information acquired by the data acquisition unit 11, such as always placing luggage in one of multiple placement lists, and not placing luggage beyond the upper limit of the number that can be placed. The objective function definition unit 122 has a function to define objective functions including minimizing the number of movements of cargo handling equipment (cranes) that carry in and out cargo, maintaining the balance of cargo stacking, and improving ease of cargo management. The layout plan optimization unit 123 has a function of observing the constraint conditions defined by the constraint condition definition unit 121, formulating the objective function defined by the objective function definition unit 122 as a mixed integer programming problem, and performing optimization calculations using a commonly available solver.

[0014] The output unit 13 has a function of outputting the placement location of each piece of luggage based on the results obtained by the placement plan optimization unit 123 performing optimization calculations. The storage unit 14 stores various information necessary for creating an optimal storage plan for luggage.

[0015] (Luggage placement location configuration) 2A and 2B are first and second diagrams, respectively, illustrating an example of a location where cargo is placed according to an embodiment. FIG. 2A shows a plan view of the cargo placement location. FIG. 2B shows a cross-sectional view of a lane 30. The lane 30 represents a set of locations where cargo is placed, divided by the operating range of the cargo handling equipment 20, the type of equipment (e.g., refrigeration equipment), and the like. While FIG. 2A shows one lane 30, there may be multiple lanes 30, or the lane 30 in FIG. 2A may be divided into, for example, two lanes 30A and 30B, each of which may be treated as a separate lane. The lane 30 is, for example, a set of locations where cargo can move without intersecting with the path of conveyance equipment (such as vehicles that carry cargo into and out of a container terminal). The cargo handling equipment 20 places cargo carried into a carry-in entrance 31 in one of the lanes 30 while moving the cargo horizontally on the paper (in the row direction of the lanes 30). The cargo handling equipment 20 also transports the cargo placed in the lane 30 to an exit 32. A bay 40 represents a set of locations where loading and unloading operations can be performed while the material handling equipment 20 is stationary. Each column of the lane 30, divided into a matrix in FIG. 2A, represents a bay 40. A row 50 represents a set of locations where cargo can be packed in one direction. Each cell in FIG. 2A represents a row 50. As shown in FIG. 2B, cargo can be loaded and unloaded in a stacked manner in a row 50. In a row 50, cargo such as containers is stacked vertically on the page. One row 50 includes multiple locations 60. Each location 60 is a location where cargo can be placed. One cargo can be placed in one location 60. There is an upper limit to the number of cargoes that can be loaded in a row 50 from the perspective of work efficiency and safety. The planning system 10 does not plan which location 60 in the lane 30 to place a certain cargo, but rather plans where within the lane 30 to secure a location for placing a group of the same type of cargo. Since cargo of the same type is often loaded close to the hull when being shipped, it is preferable for work efficiency to place cargo of the same type together at the container terminal. Also, if cargo is scattered across the lane 30, the number of movements and distance that the cargo handling equipment 20 must make increases, reducing work efficiency. For this reason, a plan is created to place cargo of the same type together as close as possible within the lane 30.For example, if cargo of the same type can be placed together in a certain row 50, the movement of cargo handling equipment 20 can be minimized. Similarly, if cargo of the same type can be placed together in one bay 40, there is no need to move cargo handling equipment 20. Furthermore, if cargo of the same type can be placed distributed across bays 40 in a small range (a range close to each other and with as few sub-areas as possible), the number of movements of cargo handling equipment 20 can be reduced. The planning system 10 creates a placement plan that allows cargo of the same type to be placed as close as possible.

[0016] FIG. 3 shows an example of a method for arranging packages. The schedule for the packages carried into lane 30 and the schedule for the packages carried out from lane 30 are set. For example, suppose that two packages from planning group G1 (referred to as package G1) are scheduled to be carried in every day from August 1 to August 3. Furthermore, suppose that before package G1 is carried in on August 3, package G2, which is located in row 50, the third and fourth rows from the top and the fourteenth column from the left, is scheduled to be carried out one by one. In pattern A of FIG. 3, each time package G1 is carried in, it is placed in the available placement location 60 at that time. Performing such processing may result in package G1 being scattered and placed in a scattered manner, which may result in a decrease in work efficiency in the future (increasing the number of times and distance that loading / unloading equipment 20 is moved). On the other hand, in pattern B, six packages must ultimately be placed based on the scheduled loading and unloading of packages, and it is foreseen that package G2 will be removed on August 3rd, leaving location 60 vacant. This calculation is made to place package G1 as close together as possible. In order to calculate an optimal package placement plan while taking into account factors such as changes in inventory in each row 50 over time, based on the scheduled loading and unloading of packages, a space-time network model is introduced to model the problem. For example, consider the problem of determining where in a container terminal, within a row 50 that can hold a maximum of four containers, containers scheduled to be brought in each day should be placed to minimize the number of movements of the loading and unloading equipment 20.

[0017] 4 is a diagram illustrating problem modeling according to the embodiment. A time frame t is a period such as a day or an hour, and the number of allocation areas for securing a certain type of luggage spg in row l during the time frame t is defined as a decision variable x spg、t、l Considering the sum of inventory and allocation area of ​​each row l for each time frame t, x spg、t、l For example, as shown in Figure 4, when the sum of inventory and allocation area of ​​rows l1 to l5 in time frame t1 to t5 is given as input, one of the solutions to the problem of reserving allocation area for four items of type spg in row l5 in time frame t5 is x spg、t5、l5 =4 is output. Based on this model, the planning system 10 searches for a placement plan that satisfies the conditions (such as minimizing movement of the cargo handling equipment 20) through optimization calculations from among various placement area candidates that satisfy the placement goal. As a result, for example, a placement area such as pattern B in FIG. 3 is secured.

[0018] However, in an actual container yard, if the amount of cargo delivered to each time slot fluctuates, for example, if the delivery date of the cargo delivered to lane 30 is earlier than scheduled, there is a possibility that other cargo may still be stored in the reserved placement area, forcing the cargo to be placed in a different placement area. Therefore, in this embodiment, a scenario is introduced so that a placement plan that meets the optimal placement goal can be found even if the scheduled delivery date of the cargo is earlier or more cargo is delivered than planned.

[0019] FIG. 5 is a diagram illustrating a problem scenario according to an embodiment. When considering cases where the amount of incoming goods delivered for each time slot is greater or less than expected, scenarios with different degrees of increase in the amount of incoming goods are considered, and the optimization calculation simultaneously considers the situations of multiple scenarios. In FIG. 5, the cross marks indicate the trend in the cumulative amount of incoming goods based on the planned amount of incoming goods for each time slot, while the circle marks indicate the trend in the cumulative amount of incoming goods when the amount of incoming goods is delivered earlier than expected or earlier and in larger quantities. The cross marks are called the expected scenario, and the circle marks are called the unexpected scenario. For example, the expected scenario may be calculated based on past performance or may be provided as planned data by the delivery company. The unexpected scenario may be generated using a predetermined probability distribution model based on the expected scenario. It is assumed that the cumulative incoming goods amounts for the expected scenario and the unexpected scenario will not be reversed midway. The planning system 10 searches for a placement plan that satisfies the conditions for both the expected scenario and the unexpected scenario. (However, as described below, constraints are set to ensure that there is no discrepancy in the placement locations of goods between the expected scenario and the unexpected scenario.)

[0020] (mixed integer problem) Based on the model in FIG. 4 and the scenario in FIG. 5, plans for when cargo is delivered as expected and when it is delivered earlier than expected are simultaneously created without any discrepancies, the degree of cargo distribution in all scenarios is evaluated, and a layout that satisfies the conditions is calculated. This problem can be formulated as a mathematical optimization problem (mixed integer programming problem) by defining the objective function as minimizing the layout range of the same type of cargo in order to reduce the movement of cargo handling equipment 20, and setting various constraints. Mathematical optimization problems can be solved using commonly available solvers. Specific constants, variables, constraints, objective functions, etc. are shown in FIGS. 6 to 10.

[0021] (variable) Figure 6 shows an example of variables used in a mathematical optimization problem. For example, x is a non-negative integer variable that represents the number of items of a parcel type placed in a row during a time frame. The arguments of the variable x are the scenario name, time frame, parcel type, and row. The scenario is the name of the scenario used to identify whether it is one of multiple expected scenarios or multiple unexpected scenarios. In the example shown in Figure 4, the time frame is one of t1 to t5. The parcel type refers to a certain type of parcel (the same type of parcel). In the example shown in Figure 4, the row is one of l1 to l5. y is a non-negative integer variable that represents the number of placement areas in which the parcel type exists in a row during the time frame. z is a non-negative integer variable that represents the number of inventory items existing in a row during the time frame. u represents whether the parcel type uses a row during the time frame. However, if the size is 2, it is a 0-1 variable that determines whether the reference row is used. A size of 2 indicates that the parcel type's size spans two rows. The other variables are as shown in Figure 6. These variables are calculated by optimization calculations. Of these, x is the decision variable, and the rest are auxiliary variables.

[0022] (constant) Figures 7A and 7B show examples of constants used in mathematical optimization problems. For example, CStackSize is a non-negative integer constant that represents the number of packages that can be placed simultaneously in a row, with a row as an argument. Similarly, CUnedLocationNum is a non-negative integer constant that represents the time frame, the placement area that occupies the row in the time frame, or the total number of inventory items. CReceivingNode is a string that represents the inbound port through which the package type is received, and CShippingNode is a string that represents the outbound port through which the package type is released. Receiving has the same meaning as receiving, and shipping has the same meaning as shipping. Other variables are as shown in Figures 7A and 7B.

[0023] (set) Figure 8 shows an example of a set used in a mathematical optimization problem. For example, T represents a set of time frames. PL represents a set of rows in which the planned cargo can be placed. PLI represents a set of rows in which a specified cargo type can be placed among the planned cargo, with the cargo type as an argument. Other variables are as shown in Figure 8.

[0024] (constraints) The following seven types of constraints are set. (1) Maximum number of items that can be placed at the same time: This specifies the maximum number of items that can be placed at the same time in the same row. (2) Work execution: Always complete the designated inbound and outbound work within each time frame. (3) Inventory flow conservation law: The change from the inventory and allocation area in the previous time frame is reflected in the next time frame. Inventory means the reserved area (location location). (4) Step reduction: The difference between the stock and the sum of the placement areas of adjacent rows reduces the step. (5) Restricting distributed placement: Restricting placement across multiple lanes, bays, or rows. (6) Placement restrictions: The rows that can be placed are determined by the type of cargo. (7) Consistency between scenarios: This specifies that the same law must be maintained between different scenarios.

[0025] 9A to 9G show details of the constraints used in the mathematical optimization problem. (1) Maximum number of simultaneous placements No.=1 in FIG. 9A is a constraint on the upper limit of the number of simultaneous placements. The constraint No=1, branch number=1 constrains that there is an upper limit to the number of packages that can be placed in a row at the same time.

[0026] (2) Work execution No. 2 in FIG. 9A is a constraint on the execution of work. The constraint expression No.=2, Branch No.=1 restricts that all goods scheduled for delivery must be delivered (stored) within the time frame. However, this only applies to the goods type that is the subject of the plan. The constraint expression No.=2, branch number=2 restricts all items scheduled for removal to be removed (shipped) within the time frame. However, this only applies to the type of item being planned.

[0027] (3) Law of conservation of inventory flow No. 3 in FIG. 9B is a constraint on the law of conservation of inventory flow rate. The constraint expression No.=3, branch number=1 constrains that the inventory at the start time must match the initial inventory specified in the input. The constraint equation No.=3, Branch No.=2 constrains that the inventory quantity of a cargo type placed in a row must be the inventory quantity at the beginning of the previous time frame plus the quantity received in the previous time frame minus the quantity shipped out. The constraint equation No.=3, branch number=3 constrains that the inventory amount for a time frame must be expressed as the sum of the inventory amount of the luggage type being optimized for that time frame (first term on the right-hand side) and the inventory amount of other luggage types (second term on the right-hand side). The constraint equation for No. 3 and Branch No. 4 is that when the type of goods to be optimized enters a row during a time frame, if the upper limit for consideration is exceeded, a penalty for the number of goods is applied. sl The right-hand side of the equation indicates that if the upper limit for consideration has already been exceeded and the stacking has been completed, the upper limit for consideration will be 0.

[0028] (4) Step reduction No. 4 in FIG. 9C is a constraint related to suppression of steps. The constraint expression No.=4, Branch No.=1 restricts that when an item can be placed in either of the adjacent rows, the difference in total inventory quantity between adjacent rows in the same bay must not exceed the current difference. Note that the above restriction applies when there are more items in locations closer to the transportation equipment's stop position. If this limit is exceeded, a penalty value is calculated. The constraint expression No.=4, Branch No.=2 restricts that when an item can be placed in either of the adjacent rows, the difference in the total inventory quantity between adjacent rows in the same bay must not exceed the current difference. Note that the above restriction applies when there are more locations farther from the transportation equipment's stop position. If the difference is exceeded, a penalty value is recorded. The constraint equation with No. 4 and Branch No. 3 restricts that the difference in total inventory between adjacent rows in the same bay must not exceed the current difference when it is not possible to place items in locations far from the transportation equipment's stop position in the adjacent row. Note that the above constraint applies when there are more locations far from the transportation equipment's stop position. If this limit is exceeded, a penalty value is calculated. The constraint expression No.=4, Branch No.=4 constrains that if it is not possible to place items in a location far from the stop position of the transport equipment in the adjacent row, the difference in the total inventory quantity between adjacent rows in the same bay must not exceed the current difference. Note that the above constraint comes into effect when there are more items in locations closer to the stop position of the transport equipment. If this is exceeded, a penalty value is recorded.

[0029] The constraint equation with No. 4 and Branch No. 5 restricts that if it is not possible to place items in a location close to the stop position of the transport equipment in the adjacent row, the difference in the total number of items stacked between adjacent rows in the same bay will not exceed the current difference. Note that the above constraint applies when there are more items in locations closer to the stop position of the transport equipment. If this limit is exceeded, a penalty value will be calculated. The constraint equation with No. 4 and Branch No. 6 restricts the stacking of inventory between adjacent rows in the same bay so that the total difference in inventory does not exceed the current difference when it is not possible to place inventory in a location close to the stop position of the transport equipment in the adjacent row. Note that the above restriction applies when there are more locations farther from the stop position of the transport equipment. If this limit is exceeded, a penalty value is calculated. The constraint equation No.=4, branch number=7 restricts the allocation area to be secured in rows farther away from the transportation equipment's stopping position so that it can be packed into the transportation equipment's stopping position if all the allocation areas between adjacent rows in the same bay are empty, rather than in rows closer to the transportation equipment's stopping position.

[0030] Regarding the constraint (4), the difference in the total inventory (hereinafter referred to as the number of steps) between two adjacent rows belonging to the same bay can be calculated by subtracting the row with the fewer steps from the row with the larger step count. In other words, as shown in Figure 9D(a), if the row closest to the transport equipment is l1 and the row farther away is l2, the difference in the number of steps when looking at the row farther away from the row l1 closest to the transport equipment is vStep. s、t、l The result will depend on which row is more prevalent, as follows: Row l1 is more common:z s、t、l1 -z s、t、l2 =vStep s、t、l There are more low l2:z s、t、l2 -z s、t、l1 =vSteps、t、l However, which is more can only be known during the optimization process, so the constraints are not valid if the situation does not occur. As shown below, if each equation is treated as an inequality, under the opposite situation, the left side will take a negative value, and vStep will take a value greater than or equal to 0. s、t、l By defining these two expressions, we can evaluate the step height even if either is larger. z s、t、l1 -z s、t、l2 ≦vStep s、t、l z s、t、l2 -z s、t、l1 ≦vStep s、t、l Here, for a certain time frame, if there is a fixed inventory, the number of rows in row l1 minus the number of rows in row l2 is |COrgStep t、l1、l2 | can be rewritten as follows: z s、t、l1 -z s、t、l2 +COrgStep t、l1、l2 ≦ vStep s、t、l +|COrgStep t、l1、l2 | z s、t、l2 -z s、t、l1 +COrgStep t、l1、l2 ≦ vStep s、t、l +|COrgStep t、l1、l2 | By transforming it into the above formula, if one of the rows cannot be placed, there is no need to prepare a variable for that row. There are three patterns as shown below, and an example is shown in Figure 9D(b). Case 1: When both adjacent rows can be placed Case 2: When the transport equipment cannot be parked on the far side Case 3: When it is not possible to place the equipment on the side closest to where it is parked

[0031] (5) Restricting decentralized placement No. 5 in FIGS. 9E and 9F is a constraint on suppressing distributed placement. The constraint equation No.=5, branch number=1 in Figure 9E constrains that if there is at least one piece of luggage of a luggage type placed in any row, that list is used by that luggage type. The constraint equation No.=5, branch number=2 in Figure 9E constrains that if the luggage type to be optimized is placed in any list, it is also determined to be placed in the bay to which that list belongs. The constraint expression No.=5, branch number=3 in FIG. 9E constrains that if a piece of luggage of size size and type spg is placed, then that piece of luggage of that size will be using row. The constraint expression No. 5, branch number 4 in Figure 9E constrains a planning group to determine that a list is in use if a package type belonging to that group is placed in any list. A planning group represents a set of package types.

[0032] The constraint equation No.=5, branch number=5 in FIG. 9F constrains the planning group so that if it is placed in any bay, it is determined that it is also placed in the lane 30 to which that bay belongs. The constraint equation No.=5, branch number=6 in Figure 9F constrains that for a time slot planning group, if a cargo type belonging to any bay is placed in that bay, then that bay is also determined to be placed. The constraint equation No.=5, branch number=7 in Figure 9F constrains the planning group such that if a cargo type belonging to any bay is placed in that bay, then that bay is also determined to be placed. The constraint expression No.=5, branch number=8 in FIG. 9F constrains that if a piece of luggage of size size and type spg is placed, then that piece of luggage of that size will be using row. The constraint expression for No. 5 and branch number 9 in Figure 9F is that if a baggage of type spg is placed in the bay, the baggage group CItem to which that group belongs spg This limits what can be determined as using lane 30. The constraint equation No.=5, branch number=10 in Figure 9F constrains that for the planning group to be optimized, if it is placed in any lane 30, then lane 30 is determined to be used for attribute 1 of that group (the type of work, which is either loading or unloading). The constraint equation No.=5, branch number=11 in Figure 9F constrains that for the planning group to be optimized, if it is placed in any lane 30, then lane 30 is determined to be in use for attribute 2 of that group (indicating the general contractor that manages the cargo, etc.). For the constraint equation No. 5, branch number 12 in Figure 9F, this equation is defined for lane 30 that is already in stock or planned for the planning group being planned. The variable on the right side indicating whether lane 30 is in use or not is forcibly set to 1 by the value 1 on the left side, which means that lane 30 is considered to be in use. For the constraint equation No. 5, branch number 13 in Figure 9F, this equation is defined for bays that are already in stock or planned for the planning group being planned. The variable on the right side indicating whether the bay is in use or not is forcibly set to 1 by the value 1 on the left side, which means that the bay is considered to be in use. For the constraint equation No. 5, branch number 14 in Figure 9F, this equation is defined for the lane 30 where the package group is already in stock or planned. The variable on the right side indicating whether lane 30 is in use is forcibly determined to be in use by the value 1 on the left side.

[0033] (6) Placement regulations No. 6 in FIG. 9G is a constraint related to placement restrictions. The constraint No=6, branch number=1 constrains that items of size 1 and size 2 cannot be placed in the same bay. The constraint expression No.=6, branch number=2 restricts that when a piece of luggage of size 2 is placed, a piece of luggage of size 1 cannot be placed in the adjacent bay. The constraint expression No.=6, branch number=3 constrains that a bag of size 2 cannot be placed in a bay adjacent to a bay where a bag of size 2 is placed. The meaning of No. 6 and branch numbers 1 to 3 is shown in Fig. 9H. Fig. 9H(a) is a diagram explaining the constraints for branch number 1, Fig. 9H(b) is a diagram explaining the constraints for branch number 2, and Fig. 9H(c) is a diagram explaining the constraints for branch number 3.

[0034] (7) Consistency between scenarios No. 7 in FIG. 9I is a constraint on consistency between scenarios. The constraint equation with No. 7 and branch number 1 restricts placement so that there are no inconsistencies in the placement period of luggage between scenarios. Specifically, for the same type of luggage, the constraint is that a location reserved as part of the placement plan in an assumed scenario in which the luggage stays for a short time must also be reserved for the same time period in an unexpected scenario in which the luggage stays for a longer time.

[0035] The problem that occurs when the No. 7 constraint is not present is explained using Figure 9J. In optimization calculations, it is necessary to compare the adverse effects of different scenarios when a certain location is reserved as a placement area. To do this, it is necessary to constrain the areas reserved for each scenario so that there are no discrepancies. Therefore, by imposing the No. 7 constraint, in an unexpected scenario in which cargo arrives (entered) earlier, the number of areas reserved for the same type of cargo in the same row is forced to be greater than in an expected scenario in which cargo arrives later. Furthermore, the latter scenario is constrained so that the total volume in a given time frame cannot be greater than the former scenario, thereby preventing discrepancies. For example, in the layout illustrated in Figure 9J, there are two bays, SA1 and SA2, each with three rows. If cargo is delivered over five days, when planning placement areas under the unexpected scenario, areas are reserved in three rows in bay SA2 (left diagram in Figure 9J). Next, when planning for a hypothetical scenario in which the goods arrive later than that, if the constraint equation No=7 is not set, there is no constraint to prevent discrepancies between scenarios, so there is a possibility that areas will be reserved in completely different locations (right diagram (a) in Figure 9J). On the other hand, if the constraint equation No=7 is set, the same lane will be reserved as shown in the right diagram (b) in Figure 9J, and discrepancies can be prevented.

[0036] (Objective function) The objective function is the sum of the weights assigned to each of No. 1 to 7 in Figure 10. s、o , the weights for each scenario are CObjectiveWeight o , and assign weights to each scenario as CScenarioWeight s , OF is a set of objective function items consisting of No. 1 to 7 in Fig. 10, it is defined by the following formula (1). In the optimization calculation, the combination of variables x, y, z, u, v, etc. that minimizes this objective function is calculated.

[0037]

number

[0038] For example, the objective function items are the following seven.

[0039] (Objective function item 1) The equation for No=1 in Figure 10 represents the total number of lane usages for the luggage group to be optimized.

[0040] (Objective function item 2) The equation for No=2 in Figure 10 represents the total number of bays used for the cargo type being optimized. The first term represents the number of bays used by time slot. The second term indicates that the smallest bays are used first. This is intended to avoid symmetry (having similar solutions). The coefficient is normalized to the range 0 to 1, so the bay number is divided by the number of bays. Also, since it has a lower priority than the first term, it is multiplied by 0.1.

[0041] (Objective function item 3) The equation No=3 in FIG. 10 represents the total number of row usages for the luggage type to be optimized.

[0042] (Objective function item 4) The equation for No=4 in Figure 10 represents the total number of packages that exceed the Row upper limit.

[0043] (Objective function item 5) The equation for No. 5 in Figure 10 represents the sum of the differences between lanes in adjacent rows in the same bay. Note that we consider the difference in time until the time frame actually adopted.

[0044] (Objective function item 6) The equation No=6 in Fig. 10 represents the total number of lanes used by items with a specific attribute 1 among the cargoes targeted for planning.

[0045] (Objective function item 7) The equation No=7 in Fig. 10 represents the total number of lanes used by items with a specific attribute 2 among the cargoes targeted for planning.

[0046] (operation) Next, the flow of the process for creating a package placement plan according to this embodiment will be described. FIG. 11 is a flowchart illustrating an example of a plan creation process according to the embodiment. First, the data acquisition unit 11 acquires data necessary for creating a plan, such as the layout of the lanes 30, one expected scenario and one or more unexpected scenarios for the planned loading of luggage, the outgoing schedule, inventory information for luggage already placed in the lanes 30, the expected scenario, the multiple unexpected scenarios, and the weight of the objective function in equation (1) (step S1). For example, the data acquisition unit 11 acquires the above-mentioned constants, sets, etc. The data acquisition unit 11 writes and stores the various acquired data in the storage unit 14.

[0047] Next, the user performs an operation to instruct the planning system 10 to create a package placement plan. The planning system 10 accepts this instruction operation and instructs the planning unit 12 to execute placement plan creation processing. In response, the planning unit 12 executes the following processing using the constraint condition definition unit 121, objective function definition unit 122, and placement plan optimization unit 123. First, the constraint condition definition unit 121 sets constraint conditions (step S2). For example, the constraint condition definition unit 121 sets the 32 constraint equations described with reference to Figs. 9A to 9J as constraint conditions. Next, the objective function definition unit 122 sets an objective function (step S3). The objective function definition unit 122 reads the weights of the equation (1) stored in the storage unit 14 in step S1, and sets the objective function of the equation (1).

[0048] Next, the allocation plan optimization unit 123 executes calculations to optimize the package allocation plan (step S4). Here, the allocation plan optimization unit 123 is a general solver that solves optimization problems. The allocation plan optimization unit 123 solves a mixed integer programming problem formulated by the constraint conditions set in step S2 and the objective function set in step S3, and calculates variables x, y, z, u, r, vArea, vSubArea, vSubAreaOnTimeWindow, vPlaceList, vSizevlnSubArea, vSizevlnPlaceList, vStep, vWorkArea, vCompanyArea, and vItemGrpArea that minimize the value of the objective function set in step S3 while satisfying the constraint conditions set in step S2.

[0049] Next, the output unit 13 outputs the created plan (step S5). For example, based on the variable x, the output unit 13 displays, for each period (time frame), which row 50 should be allocated to which planned group of packages to be delivered during that period.

[0050] (effect) As described above, this embodiment makes it possible to plan cargo placement locations that take future work efficiency into consideration. More specifically, (1) it is possible to calculate a placement plan that places cargo of the same type as close together as possible. This reduces the number of movements and distances of the cargo handling equipment 20, thereby improving work efficiency. (This improves not only future work efficiency during loading and unloading, but also work efficiency during delivery and placement.) (2) It is also possible to create a placement plan that minimizes the maximum number of cargoes to be placed in one row 50 and minimizes the height difference between adjacent rows 50. This prevents cargo from collapsing. Furthermore, by limiting the maximum number of cargoes that can be placed, it is possible to prevent a decrease in work efficiency (e.g., stacking cargoes too high reduces the efficiency of retrieving cargoes stacked below). (3) It is possible to create a placement plan that minimizes the number of lanes 30 where cargoes of a specific work type or cargoes of a prime contractor are placed. This makes it easier to understand where cargoes are placed by grouping them by work type or prime contractor. (4) By preparing an expected scenario and an unexpected scenario and constraining the number of allocation areas reserved for the unexpected scenario, in which the luggage stays longer during a certain time frame, to be greater than the number reserved for the expected scenario, the effects of (1) to (3) above can be achieved for both the expected scenario and the unexpected scenario, and a consistent allocation plan can be created. This eliminates the need to temporarily place an item in a different location and then place it back in its original location, even if the amount of luggage delivered varies depending on the time frame, such as when the luggage arrives earlier than planned. This improves work efficiency. (5) Furthermore, this embodiment makes it possible to calculate an exact solution for luggage placement using mixed integer programming.

[0051] In the above embodiment, an example of a cargo placement plan for a container terminal was given, but the present invention can also be applied to processes such as determining the placement location for cargo being brought in and out of a general logistics warehouse, in addition to container terminals.

[0052] FIG. 12 is a diagram illustrating an example of a hardware configuration of the planning system according to the embodiment. The computer 900 includes a CPU 901, a main memory device 902, an auxiliary memory device 903, an input / output interface 904, and a communication interface 905. The planning system 10 described above is implemented in the computer 900. The above-described functions are stored in the auxiliary memory device 903 in the form of a program. The CPU 901 reads the program from the auxiliary memory device 903, loads it into the main memory device 902, and executes the above-described processing in accordance with the program. The CPU 901 also allocates a storage area in the main memory device 902 in accordance with the program. The CPU 901 also allocates a storage area in the auxiliary memory device 903 for storing data being processed in accordance with the program.

[0053] A program for implementing all or part of the functions of the planning system 10 may be recorded on a computer-readable recording medium, and the program may be loaded into a computer system and executed to perform processing by each functional unit. The term "computer system" herein includes hardware such as an OS and peripheral devices. If a WWW system is used, the term "computer system" also includes the homepage provision environment (or display environment). The term "computer-readable recording medium" refers to portable media such as CDs, DVDs, and USBs, as well as storage devices such as hard disks built into the computer system. If the program is distributed to the computer 900 via a communication line, the computer 900 may load the program into the main storage device 902 and execute the above-described processing. The program may be for implementing part of the above-described functions, or may be capable of implementing the above-described functions in combination with a program already stored in the computer system.

[0054] As described above, several embodiments according to the present disclosure have been described, but all of these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as defined in the claims, as well as in the scope and spirit of the invention.

[0055] <Additional Notes> The planning system 10, the planning method, and the program described in each embodiment can be understood, for example, as follows.

[0056] (1) A planning system 10 according to a first aspect is a planning system for planning the placement of cargo to be delivered to an area in which placement lists indicating placement locations of the cargo to be delivered and delivered in stack form are arranged in rows and columns, and includes a data acquisition unit that acquires layout information of the area, the number of the cargo that can be placed on one of the placement lists, a first scenario that defines a schedule for bringing in the cargo, a second scenario that advances the schedule for bringing in the cargo compared to the first scenario, and scheduled delivery information of the cargo, a constraint equation that restricts the upper limit of the number of the cargo to be placed on the placement list, a constraint equation that restricts that the cargo to be delivered must be placed on any of the placement lists, a constraint equation that restricts that the cargo to be delivered must be delivered from any of the placement lists, and a constraint equation that restricts that the cargo to be delivered must be delivered from any of the placement lists. and a constraint condition definition unit that sets as constraint conditions: a constraint equation that constrains the consistency of the inventory quantities of the luggage placed in the second scenario, and a constraint equation that constrains that the number of placement locations where the luggage is placed based on the second scenario in all time frames must be equal to or greater than the number of placement locations when the luggage is placed based on the first scenario; an objective function definition unit that sets as an objective function a weighted sum of a calculation formula for calculating the number of placement lists in which the luggage to be placed is placed, and a calculation formula for calculating the number of columns in which the placement list in which the luggage to be placed is included when cargo handling equipment used to place the luggage moves in the row direction in the placement lists arranged in a matrix; and a placement plan optimization unit that calculates a placement plan for the luggage that minimizes the objective function while satisfying the constraint conditions. This allows for planning of cargo placement locations that take into account future work efficiency and accommodate future changes in the delivery schedule.

[0057] (2) The planning system 10 according to the second aspect is the planning system of (1), wherein the objective function definition unit sets a new objective function as a weighted sum of the objective function and a formula for calculating the total number of luggage items placed in the placement list in excess of the upper limit. This prevents a decrease in work efficiency due to excessive luggage being placed on the placement list, and luggage being blown over by the wind.

[0058] (3) The planning system 10 according to the third aspect is a planning system of (1) to (2), wherein the objective function definition unit sets a new objective function as a weighted sum of the objective function and a formula for calculating the total number of placement lists in which luggage with different attributes are placed. This makes it possible to prevent the mixing of luggage with different attributes in the same placement list, and to create a luggage placement plan that minimizes the need to temporarily move one type of luggage in order to transport another type of luggage.

[0059] (4) The planning system 10 according to a fourth aspect is the planning system of (3), wherein the different attribute is at least one of the type of work related to the package or the manager of the package. This allows items of the same type of work (carrying in or carrying out) and belonging to the same manager to be kept together, improving work efficiency.

[0060] (5) The planning system 10 according to the fifth aspect is a planning system of (1) to (4), wherein the constraint condition definition unit further sets a constraint equation that constrains the difference between the numbers of luggage placed in adjacent placement lists to not exceed a predetermined value as much as possible, and the objective function definition unit sets a weighted sum of the objective function and a calculation equation that calculates the sum of the differences as a new objective function. This allows for the creation of a luggage placement plan that minimizes the difference in height between adjacent luggage, and prevents luggage from collapsing due to factors such as wind.

[0061] (6) A planning method according to a sixth aspect is a planning method for planning the placement of luggage to be delivered to an area in which placement lists indicating placement locations of the luggage to be delivered and delivered in stack form are arranged in rows and columns, the method including the steps of acquiring layout information of the area, the number of luggage that can be placed on one of the placement lists, a first scenario that defines a schedule for bringing in the luggage, a second scenario in which the schedule for bringing in the luggage is earlier than the first scenario, and information on the schedule for taking out the luggage, a constraint equation that restricts the upper limit of the number of luggage to be placed on the placement list, a constraint equation that restricts that the luggage to be delivered must be placed on any of the placement lists, a constraint equation that restricts that the luggage to be delivered must be taken out from any of the placement lists, and a constraint equation that restricts that all of the placement lists and a constraint equation that constrains the consistency of the inventory quantities of the luggage placed in locations where the luggage is placed based on the second scenario in all time frames to be equal to or greater than the number of placement locations when the luggage is placed based on the first scenario; setting as constraint conditions a weighted sum of a formula for calculating the number of placement lists in which the luggage to be placed is placed and a formula for calculating the number of columns in which the luggage to be placed is placed when cargo handling equipment used to place the luggage moves in the row direction of the placement lists arranged in a matrix as an objective function; and a step of calculating a placement plan for the luggage that minimizes the objective function while satisfying the constraint conditions.

[0062] (7) A program according to a seventh aspect is a process of planning the placement of luggage to be delivered to an area in which placement lists indicating placement locations of the luggage to be delivered and delivered in stack form are arranged in rows and columns, in a computer 900, the program including steps of acquiring layout information of the area, the number of luggage that can be placed on one of the placement lists, a first scenario that defines a schedule for bringing in the luggage, a second scenario that advances the schedule for bringing in the luggage compared to the first scenario, and information on the schedule for taking out the luggage, a constraint equation that restricts the upper limit of the number of luggage to be placed on the placement list, a constraint equation that restricts that the luggage to be delivered must be placed on any of the placement lists, a constraint equation that restricts that the luggage to be delivered must be taken out from any of the placement lists, and a constraint equation that restricts all of the placement lists. The system executes a process including the steps of: setting, as constraint conditions, a constraint equation that constrains the consistency of the inventory quantities of the luggage placed in the list; and a constraint equation that constrains that the number of placement locations where the luggage is placed based on the second scenario in all time frames must be equal to or greater than the number of placement locations when the luggage is placed based on the first scenario; setting, as an objective function, a weighted sum of a formula for calculating the number of placement lists in which the luggage to be placed is placed; and a formula for calculating the number of columns in which the placement list in which the luggage to be placed is placed when the loading and unloading equipment used to place the luggage moves in the row direction in the placement lists arranged in a matrix; and calculating a placement plan for the luggage that minimizes the objective function while satisfying the constraint conditions. [Explanation of symbols]

[0063] 10. Planning System 11. Data acquisition section 12. Planning Department 121... Constraint definition section 122...Objective function definition part 123 Layout Planning Optimization Unit 13. Output section 14...Storage section 900···Computer 901 CPU 902...Main memory 903...Auxiliary storage device 904 Input / Output Interface 905···Communication Interface

Claims

1. A planning system for planning the placement of cargo to be delivered to an area where a placement list indicating placement locations of the cargo to be delivered and removed in a stack format is arranged in a matrix, a data acquisition unit that acquires layout information of the area, the number of the luggage that can be arranged in one of the arrangement lists, a first scenario that determines a schedule for bringing in the luggage, a second scenario that advances the schedule for bringing in the luggage compared to the first scenario, and information on a schedule for taking out the luggage; a constraint condition definition unit that sets the following constraint conditions: a constraint equation that constrains the upper limit of the number of items to be placed on the placement list; a constraint equation that constrains that the items to be brought in must be placed on any of the placement lists; a constraint equation that constrains that the items to be removed must be removed from any of the placement lists; a constraint equation that constrains the consistency of the inventory numbers of the items placed on all of the placement lists; and a constraint equation that constrains that the number of placement locations where the items are placed based on the second scenario in all time frames must be equal to or greater than the number of placement locations when the items are placed based on the first scenario; an objective function definition unit that sets, as an objective function, a weighted sum of a formula that calculates the number of arrangement lists in which the objects to be arranged are arranged, and a formula that calculates the number of columns in which the arrangement lists in which the objects to be arranged are arranged when cargo handling equipment used to arrange the objects moves in the row direction in the arrangement lists arranged in a matrix; an allocation plan optimization unit that calculates an allocation plan of the luggage that minimizes the objective function while satisfying the constraints; A planning system that includes:

2. the objective function definition unit sets a weighted sum of the objective function and a formula for calculating a sum of differences in the numbers of the luggage placed on the adjacent placement lists as a new objective function; The planning system of claim 1 .

3. the objective function definition unit sets a weighted sum of the objective function and a formula for calculating the total number of arrangement lists in which the packages with different attributes are arranged as a new objective function; A planning system according to claim 1 or claim 2.

4. The different attribute is at least one of a type of work related to the package or a manager of the package. The planning system of claim 3.

5. the objective function definition unit sets a weighted sum of the objective function and a formula for calculating the total number of the luggage items placed in the placement list in excess of the upper limit as a new objective function; A planning system according to claim 1 or claim 2.

6. A planning method for planning the placement of cargo to be delivered to an area in which a placement list indicating placement locations of the cargo to be delivered and delivered in a stacked format is arranged in a matrix, comprising: acquiring layout information of the area, the number of the luggage that can be arranged in one of the arrangement lists, a first scenario that defines a schedule for bringing in the luggage, a second scenario that advances the schedule for bringing in the luggage compared to the first scenario, and information on a schedule for taking out the luggage; a step of setting the following constraint conditions: a constraint equation that constrains the upper limit of the number of items to be placed on the placement list; a constraint equation that constrains that the items to be brought in must be placed in any of the placement lists; a constraint equation that constrains that the items to be removed must be removed from any of the placement lists; a constraint equation that constrains the consistency of the inventory numbers of the items placed on all of the placement lists; and a constraint equation that constrains that the number of placement locations where the items are placed based on the second scenario in all time frames must be equal to or greater than the number of placement locations when the items are placed based on the first scenario; a step of setting a weighted sum of a formula for calculating the number of arrangement lists in which the objects to be arranged are arranged and a formula for calculating the number of columns in which the arrangement lists in which the objects to be arranged are arranged when cargo handling equipment used to arrange the objects moves in the row direction in the arrangement lists arranged in a matrix, as an objective function; calculating an allocation plan for the luggage that minimizes the objective function while satisfying the constraints; A planning method having:

7. On the computer, A process of planning the placement of luggage to be carried into an area in which a placement list indicating placement locations of the luggage to be carried in and out in stack form is arranged in a matrix, acquiring layout information of the area, the number of the luggage that can be arranged in one of the arrangement lists, a first scenario that defines a schedule for bringing in the luggage, a second scenario that advances the schedule for bringing in the luggage compared to the first scenario, and information on a schedule for taking out the luggage; a step of setting the following constraint conditions: a constraint equation that constrains the upper limit of the number of items to be placed on the placement list; a constraint equation that constrains that the items to be brought in must be placed in any of the placement lists; a constraint equation that constrains that the items to be removed must be removed from any of the placement lists; a constraint equation that constrains the consistency of the inventory numbers of the items placed on all of the placement lists; and a constraint equation that constrains that the number of placement locations where the items are placed based on the second scenario in all time frames must be equal to or greater than the number of placement locations when the items are placed based on the first scenario; a step of setting a weighted sum of a formula for calculating the number of arrangement lists in which the objects to be arranged are arranged and a formula for calculating the number of columns in which the arrangement lists in which the objects to be arranged are arranged when cargo handling equipment used to arrange the objects moves in the row direction in the arrangement lists arranged in a matrix, as an objective function; calculating an allocation plan for the luggage that minimizes the objective function while satisfying the constraints; A program that executes processing including

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