Information processing method, information processing device, and program

Abstract

This information processing method is executed by a computer and is for determining production capacity when producing, within a total production capacity, each of a plurality of objects that can be produced, the information processing method including: a step (S110) for acquiring an initial ratio of the production capacity of each of the plurality of objects to the total production capacity; a step (S120) for generating, on the basis of the acquired initial ratio and a demand forecast for each of the plurality of objects, production plan information showing a production plan for each of the plurality of objects; and a step (S150) for evaluating the generated production plan information, and adjusting and outputting the ratio of the production capacity of each of the plurality of objects to the total production capacity according to the evaluation result.

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, when creating a production plan, information on the production capacity required to produce the target item is necessary. However, the production capacity allocated to the production of the target item may not be appropriate.

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

[0006] An information processing method according to one aspect of the present disclosure is an information processing method performed by a computer for determining the production capacity when producing each of a plurality of objects within the total production capacity, comprising the steps of: obtaining an initial ratio of the production capacity of each of the plurality of objects to the total production capacity; generating production plan information indicating the production plan for each of the plurality of objects based on the obtained initial ratio and the demand forecast for each of the plurality of objects; and evaluating the generated production plan information and adjusting and outputting the ratio of the production capacity of each of the plurality of objects to the total production capacity according to the evaluation result.

[0007] An information processing device according to one aspect of the present disclosure is an information processing device for determining the production capacity when producing each of a plurality of objects within the total production capacity that can be produced, comprising: an acquisition unit that acquires the initial ratio of the production capacity of each of the plurality of objects to the total production capacity; a generation unit that generates production plan information indicating the production plan of each of the plurality of objects based on the acquired initial ratio and the demand forecast for each of the plurality of objects; and an adjustment unit that evaluates the generated production plan information and adjusts and outputs the ratio of the production capacity of each of the plurality of objects to the total production capacity according to the evaluation result.

[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 capacity in the production of the object 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 flowchart showing the detailed operation of step S120 shown in Figure 5. Figure 7 is a flowchart showing the detailed operation of step S70 shown in Figure 6. Figure 8 is a diagram showing the result of the transfer of production capacity according to the embodiment.

[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 product. However, if production capacity is fixed, the PSI plan will be formulated accordingly, leaving room for further improvement at the production plant level. This disclosure provides an information processing device that enables the overall profit expansion at the production plant level by allowing the transfer of production capacity. For example, when demand for a certain product is expected to increase, the production capacity of another product produced at the same production plant can be used as the production capacity for that product, or when demand for a certain product is expected to decrease, the production capacity of that product can be used as the production capacity for another product produced at the same production plant.

[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 the present disclosure is an information processing method performed by a computer for determining the production capacity when producing each of a plurality of objects within the total production capacity, and includes the steps of: obtaining an initial ratio of the production capacity of each of the plurality of objects to the total production capacity; generating production plan information indicating the production plan for each of the plurality of objects based on the obtained initial ratio and the demand forecast for each of the plurality of objects; and evaluating the generated production plan information and adjusting and outputting the ratio of the production capacity of each of the plurality of objects to the total production capacity according to the evaluation result.

[0019] This allows for the adjustment of production capacity ratios based on evaluation results, rather than simply using production planning information based on fixed production capacity. For example, it becomes possible to adjust production capacity ratios to improve evaluation results while making appropriate use of total production capacity. Therefore, it becomes possible to make production capacity in the production of the target product 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, and includes, after the output step, a step of regenerating production plan information showing the production plan for each of the multiple objects based on the adjusted ratio and the demand forecast for each of the multiple objects.

[0021] This allows for the regeneration of better production planning information based on adjusted production capacity.

[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 output step, the ratio of the production capacity of the first object among the plurality of objects up to that point is reduced, and the ratio of the production capacity of the second object, which is different from the first object, from that point onward is increased in proportion to the reduction.

[0023] This allows for reallocation of production capacity allocated to the first object to the production capacity of the second object when adjusting production capacity.

[0024] Furthermore, for example, the information processing method according to the fourth embodiment is an information processing method according to the first or second embodiment, wherein in the output step, the ratio of the production capacity of the first object is increased to a level higher than the ratio of the production capacity of the first object during a predetermined period before reducing the ratio of the production capacity of the first object up to that point.

[0025] This allows for the implementation of reserve production during a predetermined period in preparation for a future decrease in production capacity.

[0026] Furthermore, for example, the information processing method according to the fifth embodiment is an information processing method according to the first or second embodiment, wherein in the output step, a cash flow calculation is performed on the generated production plan information, and if the calculation result exceeds a predetermined standard value, the ratio of the production capacity of each of the multiple objects used to generate the production plan information is not adjusted, and if the calculation result does not exceed the predetermined standard value, the ratio of the production capacity of each of the multiple objects used to generate the production plan information is adjusted.

[0027] This allows the production capacity ratio to be adjusted if the cash flow calculation result does not exceed a predetermined threshold, so that the result of the cash flow calculation exceeds a predetermined threshold.

[0028] Furthermore, for example, the information processing method according to the sixth embodiment is an information processing method according to the first or second embodiment, wherein in the output step, an inventory quantity fluctuation calculation is performed in the generated production plan information, and if the calculation result does not exceed a predetermined standard value, the ratio of the production capacity of each of the multiple objects used to generate the production plan information is not adjusted, and if the calculation result exceeds the predetermined standard value, the ratio of the production capacity of each of the multiple objects used to generate the production plan information is adjusted.

[0029] This allows the production capacity ratio to be adjusted if the calculation result of inventory fluctuation calculation exceeds a predetermined standard value, so that the result does not exceed a predetermined standard value.

[0030] Furthermore, for example, the information processing device according to the seventh embodiment is an information processing device for determining the production capacity when producing each of a plurality of objects within the total production capacity that can be produced, and comprises: an acquisition unit that acquires the initial ratio of the production capacity of each of the plurality of objects to the total production capacity; a generation unit that generates production plan information indicating the production plan of each of the plurality of objects based on the acquired initial ratio and the demand forecast for each of the plurality of objects; and an adjustment unit that evaluates the generated production plan information and adjusts and outputs the ratio of the production capacity of each of the plurality of objects to the total production capacity according to the evaluation result.

[0031] This produces the same effect as the information processing method described above.

[0032] Furthermore, for example, the program relating to the eighth aspect is a program that causes a computer to execute the information processing method described in any one of the first to sixth aspects.

[0033] This produces the same effect as the information processing method described above.

[0034] 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 sixth aspects described above, which includes acquiring a first demand record including the actual demand for an object during a first period, inputting the first demand record into a demand forecasting model that has been trained to take the actual demand for the object as input and output a plurality of demand fluctuation scenarios showing time-series information of demand fluctuations for the object, and a plurality of occurrence probabilities for each of the plurality of demand fluctuation scenarios, and outputting a plurality of occurrence probabilities for each of the plurality of demand fluctuation scenarios, and outputting 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] This allows for an effective reduction in processing load using Lagrangian relaxation.

[0040] 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.

[0041] This allows us to reduce processing load by using a mathematical model in which a penalty term is added to the objective function.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] This allows the production quantity to be recalculated using weights corresponding to the difference, potentially leading to a more feasible solution.

[0046] 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.

[0047] This ensures that a solution satisfying the unpredictability constraint can be obtained.

[0048] 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).

[0049] This reduces the processing load required to calculate production quantities while considering integer constraints.

[0050] 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.

[0051] This allows for a reduction in processing load by relaxing the integer constraint on a given coefficient.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] This allows the production plan to be displayed to the user, thereby supporting the user in making decisions about the production plan.

[0058] 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.

[0059] This allows for an intuitive and easy-to-understand tree-like display, effectively supporting users in making production plan decisions.

[0060] 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.

[0061] This makes it possible to automatically build a Markov model as a demand forecasting model.

[0062] 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.

[0063] This produces the same effect as the information processing method described above.

[0064] 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.

[0065] This produces the same effect as the information processing method described above.

[0066] 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.

[0067] The embodiments will be described in detail below with reference to the drawings.

[0068] 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.

[0069] 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%).

[0070] 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.

[0071] (Embodiment) The information processing method and the like according to this embodiment will be described below with reference to Figures 1 to 8.

[0072] [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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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 allocating production capacity. 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 volume and maximum production volume. Parameters for allocating production capacity may include, for example, the initial ratio of production capacity for initial allocation of production capacity, and the total production capacity at the production plants where production takes place (total production capacity).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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. If other objects are being produced in the same production plant that produces the object, separate PSI plans will be calculated for the other objects as well.

[0087] On the other hand, when there are multiple items, the total production capacity, which is the production capacity of the entire production plant, is allocated to each of the multiple items as individual production capacity in a predetermined ratio. In this context, production capacity refers to production lines and personnel such as operators that can be transferred between multiple items. In other words, production capacity is the resources other than raw materials and consumables involved in the production of the items.

[0088] In calculating the PSI plan, an optimal solution that satisfies the constraints is sought by increasing or decreasing the production volume in accordance with the predicted demand fluctuations. However, for example, the amount of production that can be increased may be limited by the allocated production capacity. Therefore, in this embodiment, the ratio of production capacity allocation is adjusted so that at least a portion of the production capacity allocated to other objects whose production volume is planned to decrease in the PSI plan is allocated to the production capacity of objects whose production volume is planned to increase in the PSI plan. To this end, the optimization calculation unit 23 includes an acquisition unit 23a, a generation unit 23b, and an adjustment unit 23c as functional configurations related to ratio adjustment.

[0089] The acquisition unit 23a has the function of acquiring the production capacity ratio, i.e., the initial ratio, which is the stage before ratio adjustment. The acquisition unit 23a may acquire the initial ratio computationally by averaging past demand performance, or it may acquire an arbitrarily set initial ratio that is input as a parameter to the input unit 10.

[0090] The generation unit 23b calculates and generates a PSI plan based on the initial ratio, using a process described later with reference to Figures 5 to 8.

[0091] The adjustment unit 23c evaluates the PSI plan obtained by calculation, such as determining whether there is a surplus or shortage of production capacity, and adjusts the production capacity allocation ratio as necessary according to the evaluation result and outputs it. The adjusted ratio is obtained, for example, by the acquisition unit 23a and used by the generation unit 23b in place of the initial ratio for the calculation of the next PSI plan.

[0092] The output unit 30 outputs production plan information based on multiple demand fluctuation scenarios and multiple occurrence probabilities predicted by the demand forecasting unit 22. 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 volume for each demand fluctuation scenario.

[0093] The output unit 30 may include a communication interface and transmit production plan information via communication, or it may have a display unit such as an LCD panel or sound output device and display the production plan information.

[0094] [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.).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [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.

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] 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.

[0118]

[0119] 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.

[0120]

[0121]

[0122] 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.

[0123] In each demand fluctuation scenario, the following constraints exist.

[0124] 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.

[0125]

[0126] 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.

[0127]

[0128] 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 ｔIt is also described. Here, time t = 1 indicates the most recent time (e.g., next month) for creating the production plan, and time t = T indicates the most future time (e.g., one month out of six months later) for creating the production plan, but is not limited thereto.

[0129] Equation 1 is a mixed integer programming (MIP) problem subject to integer constraints (where u ｔ takes an integer value of 0 or 1) such as such feasibility constraints, so it is NP (Non-deterministic Polynomial) hard.

[0130] Also, at the stage of time t, since it is impossible to make a decision (it is unpredictable) in anticipation of the branching of the first demand fluctuation scenario s １ and the second demand fluctuation scenario s ２ into two different future demand fluctuation scenarios, the demand quantity values at time t for the two demand fluctuation scenarios should be the same value. Also, the variable u ｔ should similarly have the same value at time t for the two demand fluctuation scenarios. 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 fluctuation scenarios, at the same time t, the production quantity x ｔ and the variable u ｔ take the same value, and are constraints imposed on the time series prediction of the production quantity using the demand fluctuation 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.

[0131]

[0132] Note that B(s, t) is a set of demand fluctuation scenarios whose history up to time t is equal to the demand fluctuation scenario s.

[0133] Equation 1 is mainly subject to two constraint conditions, such integer constraints and unpredictability constraints, so the computational complexity is enormous and there is a risk that an ideal solution cannot be obtained.

[0134] In view of the above, the information processing device 100 according to this embodiment reduces the computational load while executing the information processing method shown below, and produces a production volume x that satisfies the two constraints. ｔ It is designed to acquire it.

[0135] [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 8. 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 S150 show the process of calculating a more appropriate optimal solution, including adjustment of production capacity. In particular, step S120 shows the process of calculating an optimal solution that satisfies two constraints (for example, by simulation).

[0136] 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).

[0137] 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)).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Next, the optimization calculation unit 23 obtains the initial ratio of production capacity by averaging past demand data using the acquisition unit 23a, and then performs the initial allocation of production capacity for each model by multiplying the total production capacity by the acquired initial ratio (S110). Then, the optimization calculation unit 23 optimizes the PSI plan for each model using the generation unit 23b to generate the optimal PSI plan (S120). Here, Figure 6 is a flowchart showing the detailed operation (information processing method) of step S120 shown in Figure 5. Hereafter, each step shown in Figure 6 is performed similarly for all of the specified models to be optimized.

[0144] As shown in Figure 6, 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; for example, a poor solution or a genetic algorithm may also be used.

[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 7. Figure 7 is a flowchart showing the detailed operation (information processing method) of step S70 shown in Figure 6. Figure 7 shows the operation of LP mitigation for each demand fluctuation scenario (MIP and NP hard) using the capacity scaling method.

[0160] As shown in Figure 7, 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 uｔ 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 ｓｔ The 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). ＬＰ Replace 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 ｔIf 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 ｔ The 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 6, the optimization calculation unit 23 then calculates the provisional solution (x) calculated in step S74 of Figure 7. 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 7.

[0179] The optimization calculation unit 23 calculates the production volume x corresponding to each demand fluctuation scenario. ｓｔThe 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 the production capacity given to the model 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] Hereinafter, the Lagrange multiplier calculated by Equation 15 is denoted as w. ｓｔ The Lagrange multiplier w ｓｔ has a scaling function that converts the unit of production volume to the unit of amount of money. The Lagrange multiplier w ｓｔ 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 when updating the Lagrange multiplier, and how much it affects (emphasizes) the update of the Lagrange multiplier. 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 good solution appears during 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 Equation 8 in which the Lagrange multiplier w ｓｔ is updated. The optimization calculation unit 23 repeatedly executes Steps S70 and S100 until it is determined as Yes in Step S80 (for example, until the difference becomes zero).

[0191] In this way, the optimization calculation unit 23 calculates the production quantity by ignoring (relaxing) the unpredictability constraint, then calculates the extent to which the unpredictability constraint is violated, adds this to the objective function as a penalty term, and solves the problem again (calculates the production quantity again), repeating this process. The penalty term is calculated each time step S70 is executed.

[0192] This means that the current solution is production quantity x ｓｔ This 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, since a PSI plan based on the optimal solution for the initially allocated production capacity has been obtained for each model, the optimization calculation unit 23 then determines whether the obtained PSI plan satisfies the conditions corresponding to appropriateness in terms of production capacity allocation (S130). For example, the optimization calculation unit 23 calculates the cash flow for each model in the obtained PSI plan and determines that the above conditions are met if the cash flow for the entire production plant based on the cash flow for each model exceeds a predetermined threshold value. Alternatively, the above conditions may be set so that the cash flow for the entire production plant is maximized. For example, the cash flow for the entire production plant calculated in the previous step may be used as the threshold value, and the process of obtaining a new PSI plan and calculating the cash flow may be repeated in step S130, determining "No" as long as the newly obtained cash flow exceeds that threshold value.

[0194] Furthermore, for example, in step S130, instead of cash flow, the inventory fluctuations for each model in the obtained PSI plan (i.e., the value obtained by subtracting the beginning inventory from the ending inventory) may be calculated, and it may be determined that the above conditions are met if the overall inventory fluctuations for the production plant based on the inventory fluctuations for each model do not exceed a predetermined threshold value. Since reducing inventory levels as much as possible can suppress the deterioration of inventory costs due to excess inventory, when targeting models with relatively high inventory costs, the PSI plan generated by inventory fluctuations may be evaluated.

[0195] If the answer is Yes in step S130, the PSI plan is deemed appropriate from the standpoint of allocating production capacity, and therefore the PSI plan is output as the optimal solution (S140).

[0196] On the other hand, if the result is determined to be No in step S130, there is room for improvement in the PSI plan from the perspective of production capacity allocation, so adjustments are made to the transfer of production capacity between models (between products) (S150). Figure 8 is a diagram showing the results of the transfer of production capacity according to the embodiment. In Figure 8, each series is shown for each production month, and the changes in production capacity over 10 months, from left to right, are shown for the 1st month, 2nd month, 3rd month, ..., 10th month. For each month, the ratio of the production capacity of the seven models A, B, C, ..., G is shown from bottom to top. Here, the sum of the ratios of all models equals 100%, indicating that it matches the total production capacity of the production plant. In other words, multiplying the total production capacity by the ratio of each model gives the production capacity of that model. Also, in Figure 8, the upper row shows the ratio of production capacity before adjustment, and the lower row shows the ratio of production capacity after adjustment.

[0197] The transfer of production capacity occurs between a model whose production volume increases (second target) and a model whose production volume decreases (first target) in the PSI plan. During the period of increase or decrease in production volume, at least a portion of the production capacity allocated to the first target is added to the production capacity of the second target. Furthermore, the periods of increase or decrease in production volume for the second target and the first target do not necessarily coincide. For example, if the period of decrease for the first target precedes the period of increase for the second target, the production capacity of the first target is increased during the period of decrease (a predetermined period), and then the production capacity of the first target is transferred to the second target during the period of increase for the second target. As an example, in Figure 8, in the lower section showing the change in the ratio of production capacity, the production capacity of model E is decreased in the third month in order to increase the production capacity of model A. Prior to this, the production capacity of model E was increased in the second month. By doing this, it becomes possible to produce an excess of the first product (the above-mentioned model E) in advance in order to transfer production capacity to the second product (the above-mentioned model A), thereby reducing the likelihood of inventory shortages during the period when the production capacity of the first product is reduced due to the transfer.

[0198] Referring again to Figure 5, after the production capacity transfer adjustment, the process returns to step S120, where the optimization calculation unit 23 optimizes the PSI plan for each model based on the production capacity adjusted by the generation unit 23b and regenerates the optimal PSI plan (S120). In the regeneration of the PSI plan, the process is the same as described above, except that the adjusted ratios are used instead of the initial ratios.

[0199] Then, for each model, a PSI plan based on the optimal solution for production capacity at the adjusted ratio is obtained, so the process proceeds to step S130 to determine whether the obtained PSI plan satisfies the conditions corresponding to appropriateness in terms of production capacity allocation (S130).

[0200] (Other Embodiments) As described above, the information processing method and the like according to one or more aspects have been described based on the embodiments. However, the present disclosure is not limited to these embodiments. Without departing from the spirit of the present disclosure, various modifications conceived by those skilled in the art applied to these embodiments, or forms constructed by combining components in different embodiments may also be included in the present disclosure.

[0201] For example, in step S73 shown in FIG. 6 above, in order to suppress the maximum production quantity Q per time from becoming less than or equal to the optimal solution due to the monotonic decrease of the production quantity x ｔ at least one of the following processes may be executed: (i) changing the relaxation constraint shown in Equation 11 to 0 ≤ u ｔ , (ii) changing Equation 14 to the following Equation 17, and (iii) adding x ｔ ≤ Q ｔ as a constraint condition. （ｉｎｉｔｉａｌ）

[0202]

[0203] Note that Q （ｉｎｉｔｉａｌ） represents the original maximum production quantity

[0204] In the above embodiment, the demand acquisition unit has been described as an example of obtaining a plurality of demand fluctuation scenarios and a plurality of occurrence probabilities for each of the plurality of demand fluctuation scenarios by inputting demand actual results into a demand prediction model. However, the present disclosure is not limited to this. Other devices may calculate a plurality of demand fluctuation scenarios and a plurality of occurrence probabilities calculated by inputting demand actual results into a demand prediction model, and obtain 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 devices.

[0205] Also, 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 device that transfers production capacity may be realized using any existing method for generating a PSI plan.

[0206] Furthermore, in the above embodiment, each component may be implemented by being composed of dedicated hardware or by executing a software program suitable for each component. Each component may also be implemented by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as a hard disk or semiconductor memory.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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 either Figure 5 or Figure 6.

[0213] 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.

[0214] This disclosure is useful for information processing devices, etc., related to the creation of PSI plans.

[0215] 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 Acquisition unit 23b Generation unit 23c Adjustment unit 30 Output unit 100 Information processing device

Claims

1. An information processing method for determining the production capacity of each of a plurality of objects within the total production capacity that can be produced, which is performed by a computer, comprising the steps of: obtaining an initial ratio of the production capacity of each of the plurality of objects to the total production capacity; generating production plan information that shows the production plan of each of the plurality of objects based on the obtained initial ratio and the demand forecast for each of the plurality of objects; and evaluating the generated production plan information and adjusting and outputting the ratio of the production capacity of each of the plurality of objects to the total production capacity according to the evaluation result.

2. The information processing method according to claim 1, further comprising the step of regenerating production plan information showing the production plan for each of the plurality of objects based on the adjusted ratio and the demand forecast for each of the plurality of objects, after the output step.

3. The information processing method according to claim 1, wherein in the output step, the ratio of the production capacity of the first object among the plurality of objects up to that point is reduced, and the ratio of the production capacity of the second object, which is different from the first object, thereafter is increased in proportion to the reduction.

4. The information processing method according to claim 3, wherein in the output step, the ratio of the production capacity of the first object is increased to a level higher than the ratio of the production capacity of the first object during a predetermined period before the ratio of the production capacity of the first object is reduced.

5. The information processing method according to claim 1, wherein in the output step, a cash flow calculation is performed on the generated production plan information, and if the calculation result exceeds a predetermined standard value, the ratio of the production capacity of each of the multiple objects used to generate the production plan information is not adjusted, and if the calculation result does not exceed the predetermined standard value, the ratio of the production capacity of each of the multiple objects used to generate the production plan information is adjusted.

6. The information processing method according to claim 1, wherein in the output step, an inventory quantity fluctuation calculation is performed in the generated production plan information, and if the calculation result does not exceed a predetermined standard value, the ratio of the production capacity of each of the multiple objects used to generate the production plan information is not adjusted, and if the calculation result exceeds the predetermined standard value, the ratio of the production capacity of each of the multiple objects used to generate the production plan information is adjusted.

7. An information processing device for determining the production capacity when producing each of a plurality of objects within the total production capacity that can be produced, comprising: an acquisition unit that acquires the initial ratio of the production capacity of each of the plurality of objects to the total production capacity; a generation unit that generates production plan information indicating the production plan of each of the plurality of objects based on the acquired initial ratio and the demand forecast for each of the plurality of objects; and an adjustment unit that evaluates the generated production plan information and adjusts and outputs the ratio of the production capacity of each of the plurality of objects to the total production capacity according to the evaluation result.

8. A program for causing the computer to execute the information processing method described in any one of claims 1 to 6.

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