Information processing method, information processing device, and program

Abstract

This information processing method pertains to production of a plurality of objects and is executed by a computer. The information processing method includes: a step (S120) for generating production plan information indicating a production plan for each of the plurality of objects; and a step for generating, on the basis of the generated production plan information, an order plan for ordering a common component to be used for production of each of the plurality of objects.

Description

Information processing method, information processing device, and program

[0001] This disclosure relates to an information processing method, an information processing device, and a program.

[0002] Patent Document 1 discloses a production planning device capable of formulating production plans.

[0003] Patent No. 7410345

[0004] By the way, there is room for improvement when there are multiple objects, that is, when it comes to optimizing the production of multiple objects.

[0005] Therefore, this disclosure provides an information processing method, etc., that can make the production of multiple objects more appropriate.

[0006] An information processing method according to one aspect of the present disclosure is a computer-based information processing method relating to the production of a plurality of objects, comprising the steps of: generating production plan information indicating the production plan for each of the plurality of objects; and generating an order plan relating to the ordering of common parts used in the production of any of the plurality of objects, based on the generated production plan information.

[0007] An information processing device according to one aspect of the present disclosure includes a first generation unit that generates production plan information indicating the production plans for each of the plurality of objects, and a second generation unit that generates an order plan for ordering common parts used in the production of any of the plurality of objects based on the generated production plan information.

[0008] A program relating to one aspect of this disclosure is a program that causes the computer to execute the information processing method described above.

[0009] According to one aspect of this disclosure, it is possible to realize an information processing method, etc., that can make the production of multiple objects more appropriate.

[0010] Figure 1 is a block diagram showing the configuration of the information processing device according to the embodiment. Figure 2A is a diagram showing an example of past PSI performance data. Figure 2B is a diagram showing an example of information indicating various costs. Figure 3A is a diagram showing an example of the uncertainty of the demand forecast according to the embodiment being expressed probabilistically in a scenario tree. Figure 3B is a diagram showing an example of a production plan including production volume for each demand fluctuation scenario according to the embodiment. Figure 4A is a diagram for explaining the demand fluctuation scenario when there are three branching levels. Figure 4B is a diagram showing a list of demand fluctuation scenarios with an occurrence probability of a predetermined value or higher. Figure 5 is a flowchart showing the operation of the information processing device according to the embodiment. Figure 6 is a diagram for explaining an example of grouping according to the embodiment. Figure 7 is a diagram for explaining an example of grouping according to the embodiment. Figure 8 is a flowchart showing the detailed operation of step S120 shown in Figure 5. Figure 9 is a flowchart showing the detailed operation of step S70 shown in Figure 8.

[0011] (Background to this disclosure) Before explaining this disclosure, we will explain the background to this disclosure.

[0012] In formulating PSI plans (Production, Sales, and Inventory), the traditional MinMax method often requires setting a higher safety stock level because demand forecasts can be inaccurate. Supply chain management (SCM) employs this safety stock management method, which can result in excess inventory and a deterioration of cash flow. Conversely, holding too little inventory can lead to stockouts and lost sales opportunities. Cash flow can be calculated by subtracting the increase or decrease in costs and inventory from sales (wholesale price x actual sales volume). Costs are calculated as (cost of goods sold + production cost + transportation cost) x production volume + inventory storage cost x warehouse inventory volume. The increase or decrease in inventory represents the impact of inventory fluctuations on cash flow and is calculated as wholesale price x (ending warehouse inventory volume - beginning warehouse inventory volume). For example, a decrease in inventory is added to cash flow, while an increase in inventory is subtracted. The production volume can refer to either the number of units produced or the weight of the units produced.

[0013] Traditionally, it has been necessary to optimize the PSI plan for each individual object. However, at the production plant level, multiple objects are often produced in parallel, and there may be parts (common parts) that are used in the production of both the first and second objects. Since part inventory is determined according to the production plan of the objects in which the parts are produced, common parts, with safety margins, are stocked according to the production plan of the first object, and common parts, with safety margins, are stocked according to the production plan of the second object. If it is possible to generate a production plan that standardizes common parts, it becomes unnecessary to stock common parts with safety margins considered multiple times for each production plan. In other words, by generating a production plan that standardizes common parts, it is possible to create a more appropriate inventory, i.e., ordering plan for common parts, and thus it is possible to make the production of multiple objects more appropriate from the perspective of suppressing excess inventory of common parts. In view of the above, this disclosure provides an information processing device, etc., that enables the expansion of overall profits at the production plant level by suppressing excess inventory of common parts.

[0014] Furthermore, in order to account for the uncertainty of demand forecasts when generating production plans, it is important to comprehensively cover demand fluctuation scenarios (hereinafter simply referred to as "scenarios") that show time-series information on future demand fluctuations, and to calculate the probability of each occurring. By considering multiple demand fluctuation scenarios and their probabilities, it is possible to generate production plans that can respond to any actual demand fluctuation scenario and are aligned with the branching of the demand fluctuation scenarios. This makes it possible to formulate a PSI plan that can respond to a wide range of demand fluctuation scenarios and branch flexibly.

[0015] Furthermore, it is necessary to optimize the PSI plan so that the expected value of the objective function, such as profit or cash flow, is maximized. By creating such a PSI plan, it becomes possible to achieve both improved cash flow through inventory reduction and prevention of stockouts.

[0016] However, if we were to flexibly branch the plan according to demand fluctuation scenarios and constantly try to maximize the expected value thereafter, the calculations would become extremely complex, making it difficult to resolve within a realistic timeframe.

[0017] In this case, for example, the processing load of the information processing device that outputs a production plan that takes into account the uncertainty of demand forecasts may be reduced. Specifically, it is possible to reduce the processing load of the information processing device by dividing multiple demand fluctuation scenarios into subproblems using a scenario aggregation method that applies Lagrangian relaxation, and further by applying LP relaxation using a capacity scaling method to each subproblem.

[0018] An information processing method according to a first aspect of this disclosure is a computer-based information processing method relating to the production of a plurality of objects, comprising the steps of: generating production plan information indicating the production plan for each of the plurality of objects; and generating an order plan relating to the ordering of common parts used in the production of any of the plurality of objects based on the generated production plan information.

[0019] This allows for the generation of production plan information showing the production plans for each of multiple objects, and based on the generated production plan information, it is possible to generate an ordering plan for common parts used in the production of any of the multiple objects. For example, instead of generating production plan information in parallel, it is possible to generate production plan information for multiple objects at once, taking into account common parts used by multiple objects. Instead of generating separate ordering plans for common parts for one object and for another object, it is possible to generate an ordering plan for common parts for both one object and another object. In other words, it is possible to reduce the overlapping occurrence of surplus inventory of common parts, such as surplus inventory of common parts considering the safety margin for one object and surplus inventory of common parts considering the safety margin for another object. In this way, by generating an ordering plan for common parts used in the production of any of the multiple objects based on production plan information showing the production plans for each of the multiple objects, it becomes easier to make the ordering plan for common parts more appropriate, and thus the production of multiple objects becomes more appropriate.

[0020] Furthermore, for example, the information processing method according to the second embodiment is the information processing method according to the first embodiment, wherein in the step of generating the production plan information, the plurality of objects are grouped into one or more clusters based on the degree of commonality of parts, which indicates the number of common parts among the plurality of objects, and the production plan information for the objects included in each cluster is generated.

[0021] This allows for the generation of production plan information for multiple objects at once for each cluster, rather than generating production plan information in parallel. This is achieved by considering common parts used by multiple objects included in each cluster.

[0022] Furthermore, for example, the information processing method according to the third embodiment is an information processing method according to the first or second embodiment, wherein in the step of generating the production plan information, the number of dimensions of the component usage information is compressed to three dimensions or less using component usage information for each object, which is expressed in a dimension equal to the number of components, indicating whether or not each of the multiple components is a component used in the production of each object, and the multiple objects are grouped into one or more clusters based on the component commonality degree, which indicates the number of common components based on the compressed component usage information, and a change is accepted to include objects included in the first cluster in a second cluster different from the first cluster, and the production plan information for objects included in each cluster after applying the accepted change is generated.

[0023] This allows for the compression of part usage information for each object, which is represented in dimensions corresponding to the number of parts, into three dimensions or less, and the visualization of that part usage information. When grouping multiple objects into one or more clusters based on the part commonness score, which indicates the number of common parts based on the part usage information, the multiple objects included in each cluster can be visualized, allowing for visual confirmation of the movement of objects between clusters.

[0024] Furthermore, for example, the information processing device according to the fourth embodiment includes a first generation unit that generates production plan information indicating the production plans for each of a plurality of objects, and a second generation unit that generates an order plan for ordering common parts used in the production of any of the plurality of objects based on the generated production plan information.

[0025] This produces the same effect as the information processing method described above.

[0026] Furthermore, for example, the program relating to the fifth embodiment is a program that causes a computer to execute the information processing method described in any one of the first to third embodiments.

[0027] This produces the same effect as the information processing method described above.

[0028] An information processing method relating to another first aspect of the present disclosure is an information processing method relating to any one of the first to third aspects described above, wherein a first demand performance is obtained, including the actual demand performance of an object during a first period; the first demand performance is input to a demand forecasting model that has been trained to take the actual demand performance of the object as input and output a plurality of demand fluctuation scenarios showing time-series information of demand fluctuations of the object, and a plurality of occurrence probabilities for each of the plurality of demand fluctuation scenarios, and a plurality of occurrence probabilities for each of the plurality of demand fluctuation scenarios is output; and production plan information showing a production plan for the object is output based on the obtained plurality of demand fluctuation scenarios and the plurality of occurrence probabilities.

[0029] This allows for the output of production planning information that shows production plans based on multiple demand fluctuation scenarios and multiple probability occurrences. Compared to production plans created from only one demand fluctuation scenario, this method enables the output of production plans that take into account the uncertainty of demand forecasts.

[0030] Furthermore, for example, an information processing method relating to another second embodiment is an information processing method relating to another first embodiment, wherein the unpredictability constraint imposed on the time series forecast of production volume using two or more demand fluctuation scenarios from the plurality of demand fluctuation scenarios and two or more occurrence probabilities for each of the two or more demand fluctuation scenarios is relaxed to calculate the production volume, the extent to which the unpredictability constraint is violated is calculated, and the production volume is recalculated by adding a penalty term corresponding to the relaxation of the unpredictability constraint to the objective function for calculating the production plan of the object.

[0031] This allows production volume to be calculated without having to calculate unpredictability constraints, thus reducing the processing load compared to when unpredictability constraints are calculated.

[0032] Furthermore, for example, an information processing method relating to another third embodiment is an information processing method relating to another second embodiment, wherein, with respect to a mathematical model that uses the unpredictability constraint in calculations, the unpredictability constraint is relaxed in a Lagrangian manner, divided into subproblems for each of the two or more demand fluctuation scenarios, and the production quantity is recalculated by adding the penalty term, which may include resolving the subproblems by adding the penalty term.

[0033] This allows for an effective reduction in processing load using Lagrangian relaxation.

[0034] Furthermore, for example, an information processing method relating to another fourth embodiment may be an information processing method relating to another third embodiment, where the mathematical model divided into subproblems is a mathematical model in which the penalty term is added to the objective function.

[0035] This allows us to reduce processing load by using a mathematical model in which a penalty term is added to the objective function.

[0036] Furthermore, for example, the information processing method relating to another fifth embodiment is an information processing method relating to any one of the other second to fourth embodiments, wherein the penalty term may include the difference between the provisional solution of production volume calculated without using the unpredictability constraint in the calculation and the statistical value of the provisional solution corresponding to each of the two or more demand fluctuation scenarios, and a weight.

[0037] This allows us to obtain a solution that satisfies the unpredictability constraint without having to compute the unpredictability constraint itself, by adding penalty terms that include differences and weights.

[0038] Furthermore, for example, an information processing method relating to another sixth embodiment is an information processing method relating to another fifth embodiment, wherein the weights are calculated by updating the Lagrange multiplier using the subgradient method based on the difference, and the addition of the penalty term and recalculating the production quantity may include recalculating the production quantity using the updated weights.

[0039] This allows the production quantity to be recalculated using weights corresponding to the difference, potentially leading to a more feasible solution.

[0040] Furthermore, for example, the information processing method relating to another seventh embodiment may be an information processing method relating to another fifth embodiment or another sixth embodiment, in which the process of adding the penalty term and recalculating the production quantity is repeatedly performed until the difference becomes zero.

[0041] This ensures that a solution satisfying the unpredictability constraint can be obtained.

[0042] Furthermore, for example, an information processing method relating to another eighth embodiment is an information processing method relating to any one of the fifth to seventh embodiments, wherein in each of the two or more demand fluctuation scenarios, a provisional solution for the production quantity calculated without using the unpredictability constraint is calculated by relaxing the integer constraint imposed on the time series forecast using a capacity scaling method via Linear Programming (LP).

[0043] This reduces the processing load required to calculate production quantities while considering integer constraints.

[0044] Furthermore, for example, an information processing method relating to another ninth embodiment is an information processing method relating to another eighth embodiment, wherein the integer constraint includes the predetermined coefficient for calculating the feasibility constraint relating to the execution of production taking an integer value of either a first value or a second value higher than the first value, and the LP relaxation may include relaxing the possible values ​​of the predetermined coefficient to be between the first value and the second value.

[0045] This allows for a reduction in processing load by relaxing the integer constraint on a given coefficient.

[0046] Furthermore, for example, an information processing method according to another tenth embodiment is an information processing method according to another ninth embodiment, wherein the maximum production quantity for producing the target product is updated using the production quantities corresponding to each of the two or more demand fluctuation scenarios obtained by the LP relaxation, a provisional solution is calculated using the updated maximum production quantity, the updating of the maximum production quantity and the calculation of the provisional solution are repeatedly performed until the predetermined coefficient becomes either the first value or the second value, and the provisional solution may be the provisional solution obtained when the predetermined coefficient becomes either the first value or the second value.

[0047] This makes it easier to calculate a provisional solution where a predetermined coefficient is 1, thereby reducing the amount of processing required to calculate a provisional solution that satisfies the integer constraint.

[0048] Furthermore, for example, an information processing method relating to another 11th embodiment is an information processing method relating to any one of the other 2nd to 10th embodiments, wherein two or more demand fluctuation scenarios have an occurrence probability of or greater than a predetermined value among the plurality of demand fluctuation scenarios.

[0049] This allows for the use of two or more demand fluctuation scenarios with a high probability of occurrence, making it possible to output an effective production plan that takes into account the uncertainty of demand forecasts.

[0050] Furthermore, for example, an information processing method relating to another 12th embodiment is an information processing method relating to any one of another 2nd to 11th embodiment, and the production plan information may include information for displaying the two or more demand fluctuation scenarios and the two or more occurrence probabilities side by side on the same screen.

[0051] This allows the production plan to be displayed to the user, thereby supporting the user in making decisions about the production plan.

[0052] Furthermore, for example, the information processing method relating to another 13th embodiment is an information processing method relating to any one of the other 2nd to 12th embodiments, and the production plan corresponding to the two or more demand fluctuation scenarios may be displayed in a tree format.

[0053] This allows for an intuitive and easy-to-understand tree-like display, effectively supporting users in making production plan decisions.

[0054] Furthermore, for example, an information processing method relating to another 14th embodiment is an information processing method relating to any one of another 1st to 13th embodiment, wherein the demand forecasting model is a Markov model, and the Markov model may be constructed by obtaining a second demand record of the object in a third period prior to the first period, and setting at least one of the transition probability between nodes in the Markov model and the number of branches for the nodes based on the second demand record.

[0055] This makes it possible to automatically build a Markov model as a demand forecasting model.

[0056] Furthermore, an information processing device according to another 15th aspect of the present disclosure is an information processing device according to the 7th aspect described above, comprising: a performance acquisition unit that acquires a first demand performance including the demand performance of an object during a first period; a demand acquisition unit that takes the demand performance of the object as input and acquires a plurality of demand fluctuation scenarios for a second period following the first period, and a plurality of occurrence probabilities for each of the plurality of demand fluctuation scenarios, which are output by inputting the first demand performance into a demand forecasting model that has been trained to output a plurality of demand fluctuation scenarios showing time-series information of demand fluctuations of the object, and a plurality of occurrence probabilities for each of the plurality of demand fluctuation scenarios; and an output unit that outputs production plan information showing a production plan for the object based on the acquired plurality of demand fluctuation scenarios and the plurality of occurrence probabilities.

[0057] This produces the same effect as the information processing method described above.

[0058] Furthermore, a program relating to another sixteenth aspect of this disclosure is a program that causes a computer to execute an information processing method relating to any one of the other first to fourteenth aspects.

[0059] This produces the same effect as the information processing method described above.

[0060] These general or specific embodiments may be implemented using a system, method, integrated circuit, computer program, or a non-temporary recording medium such as a computer-readable CD-ROM, or any combination of a system, method, integrated circuit, computer program, or recording medium. The program may be pre-stored on the recording medium or supplied to the recording medium via a wide-area communication network, including the Internet.

[0061] The embodiments will be described in detail below with reference to the drawings.

[0062] The embodiments described below are all comprehensive or specific examples. The numerical values, shapes, components, arrangement and connection configurations of components, steps, and the order of steps shown in the following embodiments are examples only and are not intended to limit this disclosure. Furthermore, any components in the following embodiments that are not described in an independent claim will be described as optional components.

[0063] Furthermore, in this specification, terms indicating relationships between elements such as agreement, as well as numerical values ​​and numerical ranges, are not expressions that represent only strict meanings, but also expressions that include substantially equivalent ranges, for example, differences of a few percent (or about 10%).

[0064] Furthermore, in this specification, ordinal numbers such as "first," "second," etc., do not mean the number or order of components unless otherwise specified, but are used to avoid confusion and to distinguish similar components.

[0065] (Embodiment) The information processing method and the like according to this embodiment will be described below with reference to Figures 1 to 9.

[0066] [1. Configuration of the Information Processing Device] First, the configuration of the information processing device according to this embodiment will be explained with reference to Figures 1 to 2B. Figure 1 is a block diagram showing the configuration of the information processing device 100 according to this embodiment. Note that Figure 1 shows an exemplary functional configuration of the information processing device 100, and the functional configuration of the information processing device 100 is not limited to Figure 1. The information processing device 100 outputs information regarding the PSI plan or cash flow for the second period, which is after the first period, based on the PSI performance data for the first period. In this embodiment, an example of outputting a production plan will be described as an example of a PSI plan. The first period is, for example, a period in the past from the present, and the second period is, for example, a period in the future from the present, but is not limited to these.

[0067] As shown in Figure 1, the information processing device 100 comprises an input unit 10, a processing unit 20, and an output unit 30 as its functional configuration. The information processing device 100 also comprises a processor and memory as its hardware configuration. The memory is ROM (Read Only Memory) and RAM (Random Access Memory), and can store programs executed by the processor. Each function of the information processing device 100 is realized by the processor and other components that execute the programs stored in memory. The information processing device 100 may be realized by a stationary PC (Personal Computer), a mobile terminal such as a smartphone or tablet, a server device, or a combination of two or more of these.

[0068] The input unit 10 acquires various information for outputting a PSI plan (in this case, a production plan). The input unit 10 may be configured to include a communication interface and acquire various information through communication, or it may have a reception unit such as a button, touch panel, or sound collection device and acquire various information by receiving operations from the user. The communication may be wireless or wired.

[0069] Here, an example of the various types of information acquired by the input unit 10 will be explained with reference to Figures 2A and 2B. Figure 2A is a diagram showing an example of past PSI performance data. Figure 2B is a diagram showing an example of information indicating various costs.

[0070] As shown in Figure 2A, the input unit 10 may acquire historical PSI performance data including module number, date, production volume, sales volume, and inventory volume. In the example in Figure 2A, PSI performance data for a total of three months, one month at a time, is shown. Three months is an example of the first period, but the first period is not limited to three months and can be any period appropriate to the product type. The first period may be, for example, one month or four months or more.

[0071] Figure 2A shows historical PSI performance data obtained when forecasting demand using a demand forecasting model. The input unit 10 also obtains historical PSI performance data used when constructing the demand forecasting model. This PSI performance data may include, for example, PSI performance data for a period longer than the first period.

[0072] As shown in Figure 2B, the input unit 10 may acquire information including various costs. In Figure 2B, transportation + cost, inventory storage cost, production cost, and selling price are shown as examples, but the input unit 10 only needs to acquire at least one of the transportation + cost, inventory storage cost, production cost, and selling price. This information can be used to calculate cash flow.

[0073] In this embodiment, the input unit 10 only needs to acquire past PSI performance data as shown in Figure 2A. The input unit 10 may also acquire various parameters used in processing by the processing unit 20. These parameters may include, for example, parameters for constructing a demand forecasting model, parameters for setting constraints, and parameters for grouping (clustering) when generating production plans for multiple objects (each model). Parameters for constructing a demand forecasting model may include, for example, the number of levels at the branching point. Parameters for setting constraints may include, but are not limited to, minimum production quantity and maximum production quantity. Parameters for grouping may include, for example, information on parts used in the production of each model (BOM: Bill of Materials).

[0074] Referring again to Figure 1, the processing unit 20 performs various processes to predict the production volume included in the production plan. The processing unit 20 includes a demand forecasting model construction unit 21, a demand forecasting unit 22, and an optimization calculation unit 23.

[0075] The demand forecasting model construction unit 21 executes a process to construct a demand forecasting model capable of predicting the time-series information of the amount of demand and its probability of occurrence in the second period, based on the PSI actual data (in this case, at least actual demand) for the first period. The demand forecasting model construction unit 21 constructs a demand forecasting model based on, for example, the PSI actual data for a third period that is longer than the first period. The third period may be, for example, several months or several years.

[0076] In this embodiment, the demand forecasting model construction unit 21 will be described as using a Markov model (demand fluctuation Markov model) as the demand forecasting model, but other mathematical models may also be used.

[0077] The demand forecasting unit 22 uses the demand fluctuation Markov model and pathfinding algorithm constructed by the demand forecasting model construction unit 21 to predict multiple demand fluctuation scenarios for the second period and multiple occurrence probabilities corresponding to each of the multiple demand fluctuation scenarios, based on the PSI actual data (in this case, at least actual demand) for the first period. There is a one-to-one correspondence between each demand fluctuation scenario and one occurrence probability.

[0078] The optimization calculation unit 23 calculates the optimal solution in the PSI plan that satisfies one or more constraints for creating the PSI plan, and performs a process to output the PSI plan based on the optimal solution. In this embodiment, the optimization calculation unit 23 predicts the production volume for each demand fluctuation scenario that satisfies one or more constraints for predicting production volume as the optimal solution. The optimal solution means a solution that satisfies one or more constraints (in this case, at least the production volume).

[0079] Furthermore, the optimization calculation unit 23 calculates a provisional production quantity (a provisional solution) for each demand fluctuation scenario by first relaxing one or more constraints, because performing predictions while imposing one or more constraints would increase the processing load. It then calculates the optimal solution by adjusting (recalculating) the provisional production quantity for each demand fluctuation scenario so that it satisfies one or more constraints.

[0080] Here, the PSI plan based on the optimal solution calculated by the optimization calculation unit 23 includes one PSI plan for each product (object) being produced. However, if several objects are grouped together to form a cluster, the PSI plan based on the optimal solution calculated by the optimization calculation unit 23 includes one PSI plan for that cluster.

[0081] In calculating the PSI plan, information on the parts used in the production of each model is used to group them for the purpose of forming clusters as described above. More specifically, in this grouping, models with a relatively high degree of common parts (parts commonness) are grouped into the same cluster based on the number of common parts used in the production of each model. Then, a PSI plan based on the optimal solution is generated for the clusters formed in this way. To this end, the optimization calculation unit 23 includes a grouping unit 23a, a change acceptance unit 23b, and a generation unit 23c as functional configurations related to grouping and generating PSI plans for the grouped clusters.

[0082] The grouping unit 23a is a function that acquires parameters for grouping and performs automatic grouping. The grouping unit 23a acquires information on the parts used in the production of each model from the parameters input to the input unit 10. Then, it groups each model into one of the clusters according to the degree of commonality of parts among the models. In this embodiment, for models that do not have a high degree of commonality of parts with any other models, it is explained that only that model forms a cluster in which it is grouped. In other words, each model is clustered into at least one of the clusters.

[0083] The change acceptance unit 23b is a function for accepting requests to move included models from one cluster to another for clusters that have been grouped by the automatic processing of the grouping unit 23a. For example, the change acceptance unit 23b receives a change instruction from the user obtained via the input unit 10 and changes the target object included in the first cluster to be included in a second cluster that is different from the first cluster. This allows the user to arbitrarily increase or decrease the total number of clusters, thereby adjusting the load on the PSI plan generation process for each cluster, which will be described later. In order to realize the change of models included in a cluster by the change acceptance unit 23b, the grouping unit 23a has a function for visualizing the relationship between component information and clusters. This visualization function will be described later. However, the change of models included in a cluster by the change acceptance unit 23b is not mandatory, and PSI plans may be generated directly for clusters grouped by the grouping unit 23a. In that case, the grouping unit 23a does not need to have the above visualization function.

[0084] The generation unit 23c calculates and generates a PSI plan based on the initial ratios, using a process described later with reference to Figures 5 to 8. The generation unit 23c also generates an ordering plan for each component based on the generated PSI plan. Thus, the generation unit 23c combines the functions of the first and second generation units.

[0085] The output unit 30 outputs production plan information based on multiple demand fluctuation scenarios and multiple probability occurrences predicted by the demand forecasting unit 22, as well as at least one of the ordering plan. The output unit 30 will be described as outputting both production plan information and the ordering plan. The production plan information is related to the PSI plan, and may be, for example, a PSI plan or a cash flow. In this embodiment, the production plan information includes at least the production quantity for each demand fluctuation scenario. The ordering plan is generated for each component and includes information such as the ordering timing and order quantity for the component in question.

[0086] The output unit 30 may include a communication interface and transmit production plan information and order plans via communication, or it may have a display unit such as an LCD panel or sound output device and display production plan information and order plans.

[0087] [2. Demand Fluctuation Scenarios and Production Plans] Next, the demand forecast using the demand forecasting model configured as described above by the demand forecasting unit 22 will be explained with reference to Figures 3A to 4B. Figure 3A is a diagram showing an example of the uncertainty of the demand forecast according to this embodiment being expressed probabilistically in a scenario tree. In Figure 3A, the horizontal axis represents time, and the vertical axis represents the relationship between the magnitudes of demand. Time t=0 represents the present time, and time t=1 to 3 represents future time. Note that time t may be a specific point in time, or it may be a time with a predetermined range (for example, one day, one month, etc.).

[0088] In Figure 3A, for illustrative purposes, an example is shown where the demand at the current time point branches into two levels (large and small) for the demand at the next time point. "Large" means that the demand at the next time point will be higher than at the current time point, and "small" means that the demand at the next time point will be lower than at the current time point. The number of levels in the branching is set by the user, but it may be a fixed value, or it may be automatically set by the demand forecasting model construction unit 21 based on past performance, etc. Figure 3A also shows the probability of occurrence of each demand fluctuation scenario (p1, p2, ...). "Large" and "small" are examples of time-series information. "Large" and "small" also represent nodes in the demand forecasting model. In the example in Figure 3A, one node branches into two nodes.

[0089] As shown in Figure 3A, at the present time (time t=0), the demand for the following month (time t=1) is at two levels, large and small, relative to the present time. Similarly, at the next month (time t=1), the demand for the month after that (time t=2) is also at two levels, large and small, relative to the month after that. The demand for three months later (time t=3) is also at two levels, large and small, relative to the month after that. For example, the branch point for high demand at time t=1 is at two levels: large and small.

[0090] In this case, there are eight possible demand fluctuation scenarios starting from time t=0. Furthermore, the probability that the demand at the next time point will be high (e.g., a transition probability using a Markov model) and the probability that the demand will be low (e.g., a transition probability using a Markov model) can be calculated from past demand data. For example, taking demand high at time t=1 as an example, the first probability that demand will increase at time t=2 and the second probability that demand will decrease at time t=2 can be calculated from past demand data. In the example in Figure 3A, the sum of the two probabilities is 1. For example, if there is a decreasing trend from high to low demand, the probability that it will remain low can be calculated from past demand data. Using these probabilities, the probability of each of the eight demand fluctuation scenarios occurring can be calculated.

[0091] In this way, the demand forecasting unit 22 uses a demand forecasting model to predict multiple pairs of possible demand fluctuation scenarios and their probabilities for future demand fluctuations that are difficult to predict. By creating a production plan using these multiple demand fluctuation scenarios and their probabilities, it is possible to cover a certain extent of future demand fluctuation scenarios, thus enabling the creation of a production plan that takes into account the uncertainty of demand forecasting. Since it becomes possible to create a production plan that covers a wide range of demand fluctuations, it is possible to maximize the expected value of profits.

[0092] Furthermore, the solution (production quantity) at time = 0 may be the solution that maximizes the expected value of profit at this point for all branches from time t = 1 onwards. Similarly, the solution (production quantity) at time = 1 may be the solution that maximizes the expected value of profit at this point for all branches from time t = 2 onwards.

[0093] Furthermore, the production plan may also change according to the branching of the demand fluctuation scenario. For example, a production plan that maximizes profit for subsequent demand fluctuation scenarios is always derived. If this month's demand is 80 units, for example, if next month's demand is low, instead of suddenly reducing production, it may be best to produce 70 units, considering the probability that demand will recover afterward, as this would yield the highest expected profit. In this way, the demand for the following month may be determined by considering demand in the month after next and beyond. The derivation of the production plan may be performed by the information processing device 100 or by another device.

[0094] Furthermore, the branching is not limited to two levels. Also, the demand fluctuation scenarios are not limited to eight; two or more scenarios are sufficient.

[0095] Now, let me explain another advantage of grouping each model as in this embodiment. For example, if we consider all models as one cluster regardless of the degree of commonality of parts, and create scenarios for the increase or decrease in demand for each model, assuming 20 models and 2 levels of scenarios, even just the branching at time t=1 results in 2^20 = 1,048,576 possibilities. If we also consider branching up to time t=3, or 3 or more levels, or 20 or more models, the computational load becomes enormous (explodes). Therefore, from the perspective of reducing computational load, it is effective to narrow down to clusters where a certain degree of commonality of parts is guaranteed and create scenarios in which parts are used in common. Conversely, if there are ample processing resources available for computation, models with low degrees of commonality of parts may also be included in the same cluster. Thus, the degree of commonality of parts that serves as the criterion for grouping may be set appropriately according to the processing resources.

[0096] Furthermore, the tree-like diagram of the demand fluctuation scenario shown in Figure 3A, along with its probability of occurrence, may be output as a production plan and displayed to the user. For example, a production plan corresponding to two or more demand fluctuation scenarios may be displayed in tree format.

[0097] Figure 3B is a diagram showing an example of a production plan including production volume for each demand fluctuation scenario according to this embodiment. In Figure 3B, an example of a production plan output from the information processing device 100 is shown when there are two branching levels, large and small. Figure 3B shows the results of predicting monthly production volume for a three-month period.

[0098] As shown in Figure 3B, the production plan includes a scenario (demand fluctuation scenario), a month, and its probability of occurrence. Thus, the production plan includes production levels for each of the two levels for each month. The production plan may also be presented in tabular form.

[0099] As shown in Figures 3A and 3B, the production plan information may include information for displaying two or more demand fluctuation scenarios and two or more occurrence probabilities on the same screen. The production plan only needs to include production quantities for the two or more demand fluctuation scenarios. Furthermore, the production plan does not need to include occurrence probabilities.

[0100] Figure 4A is a diagram illustrating demand fluctuation scenarios when there are three branching levels. Figure 4B is a diagram showing a list of demand fluctuation scenarios where the probability of occurrence is above a predetermined value.

[0101] As shown in Figure 4A, the branching can be at three levels: large, medium, and small. Medium means that the demand at the next time point will be equal to the demand at that time point. In this case, 81 different demand fluctuation scenarios will be generated up to time t=4.

[0102] As shown in Figure 4B, a demand forecasting model and a pathfinding algorithm may extract two or more demand fluctuation scenarios from among multiple demand fluctuation scenarios, each with a probability of occurrence above a predetermined value (0.05 or higher in the example in Figure 4B), in order to create a PSI plan. Alternatively, a predetermined number of demand fluctuation scenarios with the highest probability of occurrence may be extracted from among multiple demand fluctuation scenarios, or the user may extract the demand fluctuation scenarios. In this way, a demand forecasting model and a pathfinding algorithm may enumerate demand fluctuation scenarios whose probability of occurrence becomes relatively high when they follow recent demand performance.

[0103] In addition, the "large," "medium," and "small" categories shown in Figure 4B may include specific production volumes. Furthermore, a table containing information on the production volumes (large, medium, small) corresponding to each demand fluctuation scenario shown in Figure 4B, along with their probability of occurrence, may be output as production planning information and displayed to the user.

[0104] Furthermore, if the optimization period is set to six months, with demand fixed at a predetermined level for the first two months and fluctuating at five levels for the remaining four months, the number of demand fluctuation scenarios becomes 625, which would result in an enormous amount of computation and may prevent obtaining an ideal solution.

[0105] [3. Challenges of the mathematical model of the PSI plan to maximize the expected value of profits] As described above, in this embodiment, multiple demand fluctuation scenarios are created. The probability that a demand fluctuation scenario s occurs (corresponding to the occurrence probability shown in Figure 3A, etc.) is p ｓ Let k be the unit price of the object, let variable 1 be the quantity demanded at time t in demand fluctuation scenario s, let variable 2 be the quantity in stock at time t in demand fluctuation scenario s, let variable 3 be the presence or absence of production at time t in demand fluctuation scenario s (an integer variable that is 1 if production is produced and 0 if no production is produced), and let variable 4 be the quantity out of stock at time t in demand fluctuation scenario s. Then the objective function that maximizes the expected value of profit for all demand fluctuation scenarios is expressed by equation 1 below.

[0106]

[0107]

[0108]

[0109]

[0110]

[0111] By solving Equation 1, we can obtain the value of the production quantity that maximizes profit (corresponding to variable 5 shown below) as the solution. Furthermore, the following part of Equation 1 (Equation 2) shows the profit obtained by subtracting various costs (here, inventory storage costs, production costs, and sales losses due to stockouts) from sales.

[0112]

[0113] Variable 2 is calculated by the following equation 3, where the production quantity at time t in the demand fluctuation scenario s is defined as variable 5.

[0114]

[0115]

[0116] Note that l represents the production lead time, and S represents the number of demand fluctuation scenarios for which a production plan is to be created. Also, variable 2 is assumed to be greater than or equal to 0. By solving Equation 1, it is possible to obtain the production quantity value that maximizes the expected profit for each demand fluctuation scenario.

[0117] In each demand fluctuation scenario, the following constraints exist.

[0118] If the minimum output per batch is q and the maximum output is Q, then there is a feasibility constraint, as shown in Equation 4 below, that relates to the production capacity allocated to the object. The feasibility constraint is a constraint on the maximum and minimum output.

[0119]

[0120] Here, variable 3 satisfies the following equation 5, which is an integer constraint imposed on time series forecasting in the subproblem. The integer constraint is a constraint imposed on the production of the object, and includes the fact that variable 3, which is used to calculate the feasibility constraint regarding the execution of production, takes either an integer value of 0 (first value) or 1 (second value). Note that variable 3 is an example of a predetermined coefficient.

[0121]

[0122] In other words, in equation 4, the variable 3 (simply u ｔ When the variable 5 (also written as x) is zero, that is, when it indicates that no production takes place, ｔ (Also written as) becomes zero. Also, in equation 4, the variable u ｔ When indicating that is 1, that is, that production takes place, the quantity produced x ｔ The constraint must be that the value is greater than or equal to the minimum output q and less than or equal to the maximum output Q. ｔ is a variable that can take either 0 or 1, and hereafter it is an integer variable u ｔ It is also written as follows. Furthermore, time t=1 here indicates the most recent time for creating the production plan (for example, the next month), and time t=T indicates the furthest future time for creating the production plan (for example, one month six months from now), but is not limited to these.

[0123] Equation 1 is a mixed integer programming (MIP) problem subject to integer constraints such as such feasibility constraints (where u ｔ takes an integer value of 0 or 1), and thus is NP (Non-deterministic Polynomial) hard.

[0124] Also, at stage t, since a decision cannot be made (is unpredictable) anticipating that the first demand variation scenario s １ and the second demand variation scenario s ２ will branch into two different future demand variation scenarios, the demand quantity values at time t for the two demand variation scenarios should be the same value. Also, similarly for the variable u ｔ , the values at time t for the two demand variation scenarios should be the same value. The same applies to the production quantity x ｔ , so there are unpredictability constraints shown in Equation 6 below. The unpredictability constraints are constraints that, in a plurality of demand variation scenarios, at the same time t, the production quantity x ｔ and the variable u ｔ take the same values, and are constraints imposed on the time series prediction of the production quantity using the demand variation scenario and the occurrence probability. These unpredictability constraints are shown in Equation 6 below. Time series prediction means predicting two or more production quantities at each predetermined period.

[0125]

[0126] Incidentally, B(s, t) is a set of demand variation scenarios whose history up to time t is equal to the demand variation scenario s.

[0127] Since Equation 1 is mainly subject to two constraint conditions, such as these integer constraints and unpredictability constraints, the computational complexity is enormous and there is a risk that an ideal solution cannot be obtained.

[0128] In view of the above, the information processing apparatus 100 according to the present embodiment is devised to obtain the production quantity x ｔ that satisfies the two constraint conditions while reducing the computational complexity while executing the information processing method shown below.

[0129] [4. Operation of the Information Processing Device] Next, the operation of the information processing device 100 configured as described above will be explained with reference to Figures 5 to 9. Figure 5 is a flowchart showing the operation (information processing method) of the information processing device 100 according to this embodiment. Steps S10 and S20 shown in Figure 5 show the process of constructing a demand forecasting model (here, a demand fluctuation Markov model), steps S30 and S40 show the process of estimating multiple demand fluctuation scenarios and multiple occurrence probabilities, and steps S110 to S130 show the process of calculating a more appropriate optimal solution, including grouping each model. In particular, step S120 shows the process of calculating an optimal solution that satisfies two constraints (for example, by simulation).

[0130] As shown in Figure 5, first, the demand forecasting model building unit 21 acquires PSI performance data for the past several years (S10). The demand forecasting model building unit 21 may acquire PSI performance data stored in the storage unit (not shown) of the information processing device 100, or it may acquire PSI performance data via the input unit 10. Note that in step S10, it is sufficient to acquire at least performance data related to demand (second demand performance).

[0131] Several years is just one example of the third period. For example, the third period may be longer than the first period. The period for the acquired PSI performance data is not limited to several years, but may be any period for which a demand forecasting model can be constructed. The storage unit is implemented by, for example, a non-volatile storage device (SSD (Solid State Drive) or HDD (Hard Disk Drive)).

[0132] Next, the demand forecasting model construction unit 21 constructs a demand fluctuation Markov model from the PSI actual data (S20). Constructing a demand fluctuation Markov model includes, for example, setting at least one of the transition probabilities between nodes in the Markov model and the number of branches for the nodes, based on the second demand actual data. The method for constructing the demand fluctuation Markov model from the PSI actual data is not particularly limited, and any known method may be used. A demand fluctuation Markov model is constructed for each type (model) of object. The demand fluctuation Markov model corresponding to the model is constructed from the PSI actual data of that model, but is not limited to this.

[0133] Next, the input unit 10 acquires the demand data for the most recent two months (first demand data) for the specified model to be optimized (S30). Here, for example, multiple objects produced within a production plant are designated as the models to be optimized. For each model, the input unit 10 acquires the demand data for the most recent two months from the storage unit of the information processing device 100, or from an external device via the input unit 10. Note that the period for which the demand data is acquired is not limited to the most recent two months; it may be the most recent one month, a period of three months or more, or a period corresponding to the period for which the production plan is created. The input unit 10 functions as a data acquisition unit that acquires demand data. The most recent two months is an example of the first period. Note that the first period is not limited to months; it may be hours, days, weeks, years, etc.

[0134] Next, the demand forecasting unit 22 uses a demand fluctuation Markov model and a pathfinding algorithm to estimate K (K: an integer of 2 or more) demand fluctuation scenarios for the next N months (for example, N is an integer of 1 or more) based on the demand performance of the most recent two months, in order of decreasing probability of occurrence (S40). A demand fluctuation Markov model is constructed for each machine, and here, a demand fluctuation Markov model corresponding to each machine to be optimized is used. Also, N months is an example of a second period. Note that the second period is not limited to months, but may be hours, days, weeks, years, etc.

[0135] Furthermore, the demand forecasting unit 22 creates multiple demand fluctuation scenarios from a demand fluctuation Markov model using a pathfinding algorithm. In the pathfinding algorithm, one of the levels is selected at each time point, and the time-series information of the selected level is extracted as a demand fluctuation scenario. The demand forecasting unit 22 also calculates the probability of occurrence of a demand fluctuation scenario from the transition probabilities between nodes that pass through that demand fluctuation scenario, and obtains, for example, K demand fluctuation scenarios with a high probability of occurrence. The demand forecasting unit 22 functions as a demand acquisition unit.

[0136] In step S40, an example was described in which K demand fluctuation scenarios with a high probability of occurrence are extracted from among multiple demand fluctuation scenarios, but this is not limited to this example, and the extraction process does not have to be performed. If the extraction process is not performed, all of the multiple demand fluctuation scenarios become the subject of steps S110 and beyond.

[0137] Next, the optimization calculation unit 23 obtains information on the parts used in the production of each model by the grouping unit 23a, and groups each model into one or more clusters based on the information on the parts (S110). Specifically, the grouping is performed as shown in Figures 6 and 7. Figures 6 and 7 are diagrams illustrating an example of grouping according to the embodiment.

[0138] Figure 6 shows a conceptual diagram of the information sequentially generated by the processing executed by the grouping unit 23a. Figure 6 indicates that the information is generated in the order of the top left, top right, bottom left, and bottom right of the page. As shown in the top left of Figure 6, the acquired information on the parts to be used is first arranged into a matrix with the part types in the rows and the models in the columns. Therefore, focusing on each model, the information on the parts used has a number of dimensions equal to the total number of parts. For example, the part in the first row is shown to be used once in the model in the first column.

[0139] The grouping unit 23a compresses the number of dimensions of the matrix by a dimensionality reduction process such as t-SNE, reducing (compressing) the number of dimensions to a visible dimension of 3 or less. Here, t-SNE is used as an example, but any existing dimensionality reduction process is not limited to t-SNE and can be applied. As a result, information with a compressed number of dimensions (for example, to 2 dimensions in the figure) is obtained, as shown in the upper right figure of Figure 6. The information after dimensionality reduction can be visualized by plotting, as shown in the lower left figure of Figure 6. In this plot diagram, the closer the plots are to each other, the more similar the information is, indicating that the original information of the parts used is similar. In other words, by grouping plots that are close to each other into a single cluster, it is possible to generate clusters with a high degree of part commonality where the information of the parts used is similar. In other words, the degree of part commonality in this embodiment is represented by the closeness of the distance between plots after dimensionality reduction.

[0140] In the example shown in the figure, each model is grouped into five clusters, as indicated by the dashed circles in the lower right of Figure 6, using a clustering method such as k-means. Alternatively, instead of k-means, cosine similarity can be calculated as the proximity of the plots, and grouping can be performed simply by thresholding the calculated values. Furthermore, grouping can be performed by directly applying k-means to the matrix data without performing dimensionality reduction using t-SNE.

[0141] To verify the validity of the above clustering method, we repeated the same process multiple times, mixing random numbers into the information of the components used. In each case, we confirmed that nine clusters, as shown in Figure 7, could be reliably obtained. In Figure 7, the components used in each of the 20 models, from model A to model T, are arranged on the horizontal axis, and the components common to each cluster are indicated by dot hatching.

[0142] Then, a visualization of the grouped clusters, as shown in the lower right of Figure 6, is presented to the user. The user can change the models included in each cluster while looking at the presented diagram. For example, if the user wants to change the models included in a cluster, they input instructions for the change via the input unit 10 while visually checking the proximity of the plots of each model. The grouping unit 23a then changes the models included in the cluster based on the input instructions and visualizes the changed clusters again, as shown in the lower right of Figure 6. In this way, by repeatedly applying user input and changes, it is possible to generate clusters that are highly satisfactory to the user.

[0143] Then, the optimization calculation unit 23 optimizes the PSI plan for each cluster using the generation unit 23c to generate the optimal PSI plan (S120). Here, Figure 8 is a flowchart showing the detailed operation (information processing method) of step S120 shown in Figure 5. Hereafter, each step shown in Figure 8 is performed similarly for all of the specified clusters to be optimized.

[0144] As shown in Figure 8, the optimization calculation unit 23 obtains a lower bound value by annealing (S50). The lower bound value here is the solution (production quantity) for Equation 1, and since the search is performed while the two constraints are satisfied, a relatively good solution (lower bound value) can be obtained among the feasible solutions. A feasible solution is a solution in the feasible region of the search space in which the value of the objective function satisfies the constraints. Annealing is an example of an approximate search method and is also called simulated annealing. Note that the method for obtaining the lower bound value is not limited to annealing, and may also be, for example, a poor solution or a genetic algorithm.

[0145] The lower bound is a fixed value and is obtained only once. Furthermore, the lower bound is independent of the demand fluctuation scenario estimated in step S40 and is used when calculating the magnitude of the Lagrange multiplier (weight) described later.

[0146] Next, the optimization calculation unit 23 divides the unpredictability constraint (the mathematical model including the unpredictability constraint) into subproblems for each scenario (demand fluctuation scenario) by Lagrangian relaxation (S60). The optimization calculation unit 23 divides the unpredictability constraint into subproblems for each demand fluctuation scenario by Lagrangian relaxation using the scenario aggregation method. In this specification, Lagrangian relaxation means relaxing (for example, ignoring) the unpredictability constraint.

[0147] The optimization calculation unit 23 uses the following equation 7 as a subproblem for the demand fluctuation scenario s.

[0148]

[0149] F ｓ (X) represents the profit for a demand fluctuation scenario s and is calculated, for example, using Equation 2. X is a feasible solution included in the set of feasible solutions C that satisfy the minimum production quantity, etc.

[0150] The portion of Equation 8 shown in Equation 7 is a term (penalty term) added to the objective function as a result of Lagrangian relaxation (relaxing a portion of the constraints), representing a violation of the constraints. A problem to which a penalty term has been added to the objective function is also called a Lagrangian relaxation problem. Equation 7 can also be described as a mathematical model in which the unpredictability constraint used in the calculation is Lagrangian relaxed, and the model is divided into subproblems for each of two or more demand fluctuation scenarios.

[0151]

[0152] lol ｓｔ This is the Lagrange multiplier. The Lagrange multiplier w ｓｔ This will be discussed later. Using the following equation 9, a solution (variable 6 below) that satisfies the unpredictability constraint at that point in the calculation process is calculated.

[0153]

[0154]

[0155] The solution that satisfies the unpredictability constraint shown in Equation 9 is the production quantity x corresponding to each demand fluctuation scenario.ｓｔ And the probability p of occurrence of the demand fluctuation scenario in question. ｓ It is expressed as the average of the values ​​obtained by multiplying by and, but is not limited to this. Note that A is a set of scenarios in which it is unpredictable for the demand fluctuation scenarios to branch into different scenarios at time t, and includes demand fluctuation scenario s.

[0156] Equation 8 is: Production volume x ｓｔ This formula calculates how far the provisional solution for production volume (calculated without using the unpredictability constraint) deviates from the mean value by taking the difference between (for example, a provisional solution) and the mean value shown in Equation 9, and then calculates the value to be added to the objective function by multiplying it by the Lagrange multiplier. Note that the mean value is just one example of a statistical value. Furthermore, the statistical value is not limited to the mean value; it may also be the median, mode, etc.

[0157] Note that production volume x ｔ If the unpredictability constraint is satisfied, then the integer variable u is necessarily ｔ It is also considered that the unpredictability constraint is satisfied.

[0158] Next, the optimization calculation unit 23 executes the process of solving the integer mixed programming (MIP) problem for each scenario (each demand fluctuation scenario) (S70).

[0159] Step S70 will be explained in detail with reference to Figure 9. Figure 9 is a flowchart showing the detailed operation (information processing method) of step S70 shown in Figure 8. Figure 9 shows the operation of LP mitigation using the capacity scaling method for subproblems (MIP and NP hard) for each demand fluctuation scenario.

[0160] As shown in Figure 9, the optimization calculation unit 23 solves the LP relaxation problem by relaxing the integer constraint (see, for example, equation 10 below) to the relaxation constraint (see, for example, equation 11 below), and the obtained solution is (x ＬＰ u ＬＰ ) (S71). In other words, the optimization calculation unit 23 calculates the integer variable u ｔ Normally, it can only take the values ​​of 0 or 1, but the integer variable u can take values ​​other than integers between 0 and 1 (inclusive). ｔ Relax the constraints on and solve equation 7 (or equation 1). When using equation 7, the Lagrange multiplier w ｓｔThe value may be set to any value (for example, 1), and the term in Equation 8 may be ignored during the calculation. The solution obtained in step S71 includes the production quantities corresponding to each of the two or more demand fluctuation scenarios obtained by LP relaxation.

[0161]

[0162] 0 ≤ u ｔ ≦1...(Formula 11)

[0163] Thus, the optimization calculation unit 23 imposes the relaxation constraint shown in Equation 11 and solves Equation 7 to obtain the solution (x ＬＰ u ＬＰ ) Let the variable u ＬＰ Since can take on decimal values ​​such as 0.2 or 0.8, the solution obtained here does not satisfy the integer constraint.

[0164] Next, the optimization calculation unit 23 calculates the integer variable u ｔ Whether all of them are integers or not, that is, the integer variable u ｔ Determine whether all of them are either 0 or 1 (S72). Integer variable u ｔ This is the variable u for all demand fluctuation scenarios at time t. ＬＰ This includes. Since relaxation constraints are imposed, it is assumed that the determination in step S72 will be No if it is performed for the first time.

[0165] The optimization calculation unit 23 calculates the integer variable u ｔ If it is determined that all of them are not integers (No in S72), then for t∈T that does not satisfy the integer constraint (see equation 12 below), the maximum production quantity Q ｔ The constraint is updated as shown in Equation 13 below (S73). However, Equation 13 satisfies Equation 14 shown below.

[0166]

[0167]

[0168]

[0169] The optimization calculation unit 23 calculates the maximum output Q at time t. ｔ The constraint is set by the following variable 7, which represents the output quantity (output quantity x at time t). ＬＰReplace with ). For example, the variable at time t shown in variable 8 below (the variable u at time t) ＬＰ If the ratio is 0.8 and the production volume is 8000 units, then the maximum production volume Q ｔ The constraint is set to 8000, regardless of the original value.

[0170]

[0171]

[0172] Production volume x ＬＰ This should be a relatively good solution, so the production quantity x of equation 13 that we will solve from now on ｔ The value can also be close to 8000. In that case, the integer variable u ｔ This inevitably becomes 1. By using equation 13, the integer variable u ｔ This makes it easier to obtain a solution of =1.

[0173] Then, the optimization calculation unit 23 calculates the solution (x ＬＰ u ＬＰ ) is obtained. The solution here is (x ＬＰ u ＬＰ ) is shown in equation 13 (x ｔ u ｔ ) The optimization calculation unit 23 calculates the solution (x ＬＰ u ＬＰ ) is the provisional solution (x) calculated by equation 13. ｔ u ｔ It can also be said that it updates to the updated solution (x). Then, the optimization calculation unit 23 calculates the updated solution (x ＬＰ u ＬＰ Using ), the maximum production volume Q ｔ Update the constraints again.

[0174] The optimization calculation unit 23 calculates the integer variable u ｔ Steps S72 and S73 are repeatedly executed until all values ​​become integers (either 0 or 1). The optimization calculation unit 23 also calculates the integer variable u ｔ If it is determined that all values ​​are integers (Yes in S72), proceed to step S74.

[0175] Next, the optimization calculation unit 23 calculates the integer variable u ｔThe original subproblem is solved by fixing the integer variable u to a value that satisfies the integer constraint (S74). The optimization calculation unit 23 calculates the integer variable u, which is an integer (i.e., can take the value of either 0 or 1). ｔ Using this, we solve equation 7. This gives us the production quantity x for each demand fluctuation scenario that satisfies the integer constraint. ｔ The following is calculated. The solution (provisional solution) calculated in step S74 is an integer variable u ｔ This is a provisional solution for when the value is either 0 or 1, and it does not satisfy the unpredictability constraint.

[0176] Referring again to Figure 8, the optimization calculation unit 23 then calculates the provisional solution (x) calculated in step S74 of Figure 9. ｔ u ｔ ) determines whether the unpredictability constraint is satisfied (S80). The optimization calculation unit 23 determines whether the same node in each demand fluctuation scenario (x ｔ u ｔ The optimization calculation unit 23 determines whether the production volume x ｔ You may also determine whether the unpredictability constraint is satisfied for that.

[0177] The optimization calculation unit 23 calculates a solution that satisfies the unpredictability constraint (i.e., the production volume at the same node is equal in the demand fluctuation scenario) for the provisional solution using equation 9 above.

[0178] The optimization calculation unit 23 uses equation 9 to determine the production quantity x corresponding to each demand fluctuation scenario. ｓｔ And the probability p of occurrence of the demand fluctuation scenario in question. ｓ The average of the values ​​obtained by multiplying by and is calculated as a solution that satisfies the unpredictability constraint. Production quantity x ｓｔ This is the production quantity obtained as the solution to step S74 shown in Figure 9.

[0179] The optimization calculation unit 23 calculates the production volume x corresponding to each demand fluctuation scenario. ｓｔ The unpredictability constraint is satisfied if the solution that satisfies the unpredictability constraint calculated in Equation 9 matches all of the above (for each demand fluctuation scenario and for each time). Satisfying the unpredictability constraint means that the (x) of the same node in each demand fluctuation scenario ｔ u ｔ This means that they match.

[0180] Next, the optimization calculation unit 23 satisfies the unpredictability constraint, that is, the same node in each demand fluctuation scenario (x ｔ u ｔ If the two conditions match (Yes in S80), the provisional solution (x ｔ u ｔ The optimal solution is set to (S90). A determination of Yes in step S80 indicates that the problems (subproblems and LP relaxation problems) for all demand fluctuation scenarios have been solved, that is, a solution that satisfies the unpredictability constraint and integer constraint has been obtained. Step S90 corresponds to generating production plan information for each cluster generated in step S110 as shown in Figure 5.

[0181] Furthermore, the optimization calculation unit 23 determines that the unpredictability constraint is not satisfied, that is, at least one (x) of the same node in each demand fluctuation scenario. ｔ u ｔ If they do not match (No in S80), the Lagrange multiplier is updated using the subgradient method (S100), and the processing from step S70 onwards is executed.

[0182] In step S100, the optimization calculation unit 23 updates the Lagrange multiplier to be used next using the following subgradient method with the following equation 15, assuming that the variable 9 is the Lagrange multiplier to be used next. ＬＢ The improvement from the previous value is used to update the Lagrange multiplier. Note that the initial value of the Lagrange multiplier may be set beforehand.

[0183]

[0184]

[0185] Hereafter, the Lagrange multiplier calculated in Equation 15 is w ｓｔ It is also written as: Lagrange multiplier w ｓｔ It has a scaling function that converts units of production volume into units of money. Lagrange multiplier w ｓｔ This is updated each time step S100 is executed.

[0186] Here, the current Lagrange multiplier is the following variable 10, and λ represents the update coefficient of the Lagrange multiplier. Specifically, λ indicates how much better the current solution is with respect to the lower bound value F ＬＢ and how much it affects (how important it is) the update of the Lagrange multiplier in terms of how much better it is with respect to the lower bound value F. λ is set by the user, for example. F ＵＢ represents the upper bound value, and specifically, it represents the value of the objective function at the best solution at that time. Initially, a huge number is given, and the value is updated each time a better solution appears during the repeated calculations. F ＬＢ represents the lower bound value obtained in step S50, and r(w) is the subgradient vector shown in the following equation 16.

[0187]

[0188]

[0189] Note that the method for calculating the Lagrange multiplier is not limited to the subgradient method, and other known methods may be used.

[0190] Next, the optimization calculation unit 23 proceeds to step S70 and executes the process of step S70 again using the updated equation 8 for the Lagrange multiplier w ｓｔ The optimization calculation unit 23 repeatedly executes steps S70 and S100 until it is determined to be Yes in step S80 (for example, until the difference becomes zero).

[0191] In this way, the optimization calculation unit 23 calculates the production volume while ignoring (relaxing) the prediction impossibility constraint, then calculates how much it violates the prediction impossibility constraint, adds it to the objective function as a penalty term, and repeats solving the problem (recalculating the production volume) again. The penalty term is calculated each time step S70 is executed.

[0192] As a result, the production volume x, which is the solution at the current time ｓｔThis method allows us to approach a solution that satisfies the unpredictability constraint, thus obtaining a solution that satisfies the unpredictability constraint even though the unpredictability constraint is not used in the calculation. Furthermore, since the calculation is performed using Lagrange relaxation and LP relaxation, meaning that the unpredictability constraint and integer constraint are not used in the calculation, the amount of computation can be reduced compared to when the unpredictability constraint and integer constraint are used in the calculation. In addition, since multiple demand fluctuation scenarios are considered (for example, the production quantity for one demand fluctuation scenario is affected by the production quantities for one or more other demand fluctuation scenarios), it is possible to calculate a production quantity that can withstand fluctuations in actual demand to some extent (suppressing the occurrence of large losses).

[0193] Referring again to Figure 5, a PSI plan based on the optimal solution has been obtained for each cluster, and this PSI plan is output as the optimal solution (S130). The optimization calculation unit 23 further generates and outputs an ordering plan for each component based on the obtained PSI plan, along with the PSI plan. For example, since the PSI plan for each cluster based on the optimal solution includes the production plan for each model, the lead time of the components is calculated according to the production plan (especially by bringing forward the ordering timing for common components) to generate an ordering plan for each component, including common components. In this way, even for multiple models (objects) with common components, an appropriate PSI plan and component ordering plan are generated and output, and production of each model can be carried out using this appropriate PSI plan and component ordering plan. Therefore, it becomes possible to make the production of multiple models more appropriate.

[0194] (Other Embodiments) Although information processing methods, etc., relating to one or more embodiments have been described above based on embodiments, this disclosure is not limited to these embodiments. Without departing from the spirit of this disclosure, various modifications that a person skilled in the art could conceive of may be applied to these embodiments, and forms constructed by combining components from different embodiments may also be included in this disclosure.

[0195] For example, in step S73 shown in Figure 9 above, production quantity x ｔTo suppress the maximum production quantity Q per time from becoming below the optimal solution due to the monotonic decrease, at least one of the following processes may be executed: (i) changing the relaxation constraint shown in Expression 11 to 0 ≦ u ｔ ; (ii) changing Expression 14 to the following Expression 17; (iii) adding x ｔ ≦ Q ｔ as a constraint condition. （ｉｎｉｔｉａｌ）

[0196]

[0197] Note that Q （ｉｎｉｔｉａｌ） indicates the original maximum production quantity.

[0198] In the above embodiment, the demand acquisition unit has been described as an example of acquiring a plurality of demand fluctuation scenarios and a plurality of occurrence probabilities for each of the plurality of demand fluctuation scenarios by inputting demand实绩 into a demand prediction model. However, the present invention is not limited to this, and other devices may calculate a plurality of demand fluctuation scenarios and a plurality of occurrence probabilities calculated by inputting demand实绩 into a demand prediction model, and acquire the plurality of demand fluctuation scenarios and the plurality of occurrence probabilities (or one or more demand fluctuation scenarios and one or more occurrence probabilities) via the other device.

[0199] In the above, a method considering the uncertainty of demand prediction has been used in the generation of the PSI plan. However, the generation of the PSI plan is not limited to this. An information processing apparatus for performing the transfer of production capacity may be realized using any existing method for generating a PSI plan.

[0200] In the above embodiment, each component may be constituted by dedicated hardware or may be realized by executing a software program suitable for each component. Each component may be realized by a program execution unit such as a CPU or a processor reading and executing a software program recorded on a recording medium such as a hard disk or a semiconductor memory.

[0201] ​Furthermore, the order in which each step in the flowchart is performed is illustrative for the purpose of specifically illustrating this disclosure, and may be in a different order. Also, some of the above steps may be performed simultaneously (in parallel) with other steps, and some of the above steps may not be performed.

[0202] Furthermore, the division of functional blocks in the block diagram is just one example; multiple functional blocks can be implemented as a single functional block, a single functional block can be divided into multiple parts, or some functions can be moved to other functional blocks. In addition, the functions of multiple functional blocks with similar functions can be processed in parallel or time-sharing by a single piece of hardware or software.

[0203] Furthermore, the information processing device according to the above embodiments may be implemented as a single device or as a plurality of devices (for example, an information processing system). When the information processing device is implemented as a plurality of devices, the components of the information processing device may be distributed among the plurality of devices in any manner. When the information processing device is implemented as a plurality of devices, the method of communication between the plurality of devices is not particularly limited and may be wireless communication or wired communication. In addition, wireless communication and wired communication may be combined between the devices.

[0204] Furthermore, each component described in the above embodiment may be implemented as software, or typically as an integrated circuit (LSI). These may be individually integrated onto a single chip, or some or all of them may be integrated onto a single chip. Here, we refer to it as an LSI, but depending on the degree of integration, they may also be called ICs, system LSIs, super LSIs, or ultra LSIs. Moreover, the method of integrated circuit implementation is not limited to LSIs; it may also be implemented using dedicated circuits (general-purpose circuits that execute dedicated programs) or general-purpose processors. After LSI manufacturing, a programmable FPGA (Field Programmable Gate Array) or a reconfigurable processor that can reconfigure the connections or settings of circuit cells inside the LSI may be used. Furthermore, if an integrated circuit implementation technology that replaces LSIs emerges due to advances in semiconductor technology or other derived technologies, it is natural that the components may be integrated using that technology.

[0205] A system LSI is a highly functional LSI manufactured by integrating multiple processing units onto a single chip. Specifically, it is a computer system composed of a microprocessor, ROM, RAM, and other components. The ROM stores the computer program. The system LSI achieves its function by having the microprocessor operate according to the computer program.

[0206] Furthermore, one aspect of this disclosure may be a computer program that causes a computer to perform each characteristic step included in the information processing method shown in any of Figures 5, 8, and 9.

[0207] Furthermore, for example, the program may be a program to be executed by a computer. Also, in one aspect of this disclosure, such a program may be recorded on a computer-readable non-temporary recording medium. For example, such a program may be recorded on a recording medium and distributed or made available. For example, by installing the distributed program on a device having another processor and having that processor execute the program, it becomes possible to have that device perform the above-mentioned processes.

[0208] This disclosure is useful for information processing devices, etc., related to the creation of PSI plans.

[0209] 10 Input unit (performance acquisition unit) 20 Processing unit 21 Demand forecasting model construction unit 22 Demand forecasting unit (demand acquisition unit) 23 Optimization calculation unit 23a Grouping unit 23b Change acceptance unit 23c Generation unit 30 Output unit 100 Information processing device

Claims

1. A computer-based information processing method for the production of multiple objects, comprising: generating production plan information indicating the production plan for each of the multiple objects; and generating an order plan for ordering common parts used in the production of any of the multiple objects, based on the generated production plan information.

2. The information processing method according to claim 1, wherein in the step of generating the production plan information, the plurality of objects are grouped into one or more clusters based on the degree of commonality of parts, which indicates the number of common parts among the plurality of objects, and the production plan information for the objects included in each cluster is generated for each cluster.

3. The information processing method according to claim 1, wherein in the step of generating the production plan information, the number of dimensions of the component usage information is compressed to three dimensions or less using component usage information for each object, which is expressed in a dimension equal to the number of components, indicating whether each of the multiple components is a component used in the production of the respective object; the multiple objects are grouped into one or more clusters based on the component commonness, which indicates the number of common components based on the compressed component usage information; a change is accepted to include objects included in a first cluster in a second cluster different from the first cluster; and the production plan information for objects included in each cluster after applying the accepted change is generated for each cluster.

4. An information processing device comprising: a first generation unit that generates production plan information showing the production plans for each of a plurality of objects; and a second generation unit that generates an order plan for ordering common parts used in the production of any of the plurality of objects based on the generated production plan information.

5. A program for causing the computer to execute the information processing method described in any one of claims 1 to 3.

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