WO2023276534A1 - Système informatique - Google Patents

Système informatique Download PDF

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Publication number
WO2023276534A1
WO2023276534A1 PCT/JP2022/022222 JP2022022222W WO2023276534A1 WO 2023276534 A1 WO2023276534 A1 WO 2023276534A1 JP 2022022222 W JP2022022222 W JP 2022022222W WO 2023276534 A1 WO2023276534 A1 WO 2023276534A1
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plan
users
recommended
controller
evaluation
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PCT/JP2022/022222
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English (en)
Japanese (ja)
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雄一 小林
由泰 高橋
慶行 但馬
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株式会社日立製作所
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry

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  • the present invention relates to a computer system and calculation method, and more particularly to a computer system and method for achieving plan optimization in multiple subjects.
  • Plans are often drawn up in cooperation with different industries and departments. Along with this, it is necessary for multiple entities to mutually agree on plans, such as the need to reach agreement on product supply and demand plans between the sales department and the production department, and the need to coordinate operation plans for multiple transportation systems. There are many things that must be done. Between departments and between companies, while sharing the purpose of improving the profits of the entire company, society, or region, multiple entities each want to improve their own profits. In the first place, it is not easy to get multiple entities to mutually agree on their respective plans.
  • Non-Patent Document 1 a method has been proposed to solve the problem that multiple entities with different utility functions must cooperate with each other to optimize plans.
  • This method aims to realize a symbiotic resource operation that simultaneously achieves the overall purpose while equally satisfying the interests of multiple entities.
  • This method is based on the fact that each entity can make its own desired resource request to the collaborative field, and that the collaborative field for the resource request, from the viewpoint of overall optimization of multiple entities, It has the feature of being able to negotiate with the subject multiple times, that is, mediation.
  • Patent Document 1 A related technique is also disclosed in Japanese Unexamined Patent Application Publication No. 2009-30476 (Patent Document 2).
  • each entity in multi-entity planning work is, for example, the order of production and the order of shipment, or the time of arrival of a train and the time of departure of a bus.
  • the types of are not uniform.
  • the present invention finds a plurality of plan candidates that can be agreed upon by the multiple subjects even when the utility functions of the multiple subjects are uncertain. It is an object of the present invention to provide a computer system and a method therefor, which makes it easier for a plurality of subjects to agree on an optimization plan by presenting it to the subjects, thereby facilitating cooperation between the tasks of the plurality of subjects.
  • the present invention connects terminal devices of each of a plurality of users and a server via a network, and the server optimizes a plan transmitted from each of the plurality of terminal devices,
  • a computer system for providing plans to the terminal devices of each of the plurality of users, the server comprising a controller executing a program stored in a memory, the controller being transmitted from each of the plurality of terminals.
  • a server connected to the terminal devices of each of a plurality of users via a network optimizes the plan transmitted from each of the plurality of terminal devices, and provides the optimized plan to each of the plurality of users.
  • a method for assisting the creation of an optimized plan wherein a controller of the server, executing a program stored in memory, evaluates a plan transmitted from each of the plurality of terminals and outputs results of the evaluation.
  • a plurality of plan candidates that can be agreed upon by the multiple subjects are selected.
  • FIG. 4 is a characteristic diagram simply explaining the operation of the computer system when optimizing plans for multiple users;
  • FIG. 10 is a characteristic diagram after the computer system recalculates the recommended plan a predetermined number of times;
  • 1 is an example of a block diagram of a computer system;
  • FIG. It is a system diagram in which a bus route of a bus company and a railway route of a railway company are connected at a station.
  • 3 is a flowchart relating to an example of the operation of a controller of a server that constitutes the computer system of FIG. 2; It is a management table concerning an example of an operation plan of a bus and a railroad. It is an example of a recommended plan (operation plan) provided from the server to the user terminal device.
  • FIG. 4 is a characteristic diagram showing the distribution of assumed plans created by the server; It is the table which recorded the evaluation result by the server with respect to the user's response.
  • FIG. 4 is a characteristic diagram showing the distribution of assumed plans created by the server;
  • a computer system provides multiple users with a service that enables the early establishment of an optimized plan by helping multiple users agree on a plan.
  • a computer system calculates a plan that a plurality of subjects can agree on based on plans requested by a plurality of users, and provides this to a plurality of users as a recommended plan. Multiple users decide to accept or reject the recommended plan and, in the case of rejection, send the revised plan to the computing system.
  • the computer system repeats recalculating the recommended plan until all users have accepted it.
  • the computer system determines the recommended plan agreed upon by all users as the optimum plan.
  • FIG. 1A is a characteristic diagram simply explaining the operation of the computer system when optimizing plans for multiple users.
  • the horizontal axis indicates user A's utility function
  • the vertical axis indicates user B's utility function.
  • the utility functions of both users have a trade-off relationship, that is, a plan with a high utility value for one of users A and B is a plan with a low utility value for the other.
  • the area indicated by reference numeral 100 in FIG. 1A is the solution space in which there is a plan that users A and B agree on, and reference numeral 102 is the boundary line of the solution space.
  • reference numeral 102 is the boundary line of the solution space.
  • Pareto-optimal designs 102A, 102B, 102C, 102D exist in the solution space 100, these are located on the boundary 102 of the solution space.
  • 104A indicates user A's allowable range
  • 104B indicates user B's allowable range.
  • User A can agree on a plan that is within tolerance 104A
  • user B can agree on a candidate plan that is within tolerance 104B.
  • 102A has a high utility value for user B but a low utility value for user A, so the computer system estimates the probability of both parties agreeing at 5%.
  • 102D has a large utility value for user A but a small utility value for user B, so the computer system estimates the agreement probability to be 5%. Since 102B and 102C are intermediate values for users A and B, the computer system calculates that the agreement probability is 10%.
  • Each user has an allowable range in which they can agree on the plan calculated by the computer system, but since the allowable range is narrow at the initial stage, there is no plan existing in the area where the shared ranges of both users intersect. No. That is, there is no plan with a high probability that users A and B can agree.
  • FIG. 1B shows a characteristic diagram after the computer system has recalculated the recommended plan a predetermined number of times.
  • the computer system estimates the extension of the sharing range based on the consent or refusal responses from users A and B, and calculates the consensus probability based on that.
  • the computer system presents the candidate plan 102B among the plurality of candidate plans to the users A and B as a recommended plan together with its agreement probability (80%). As a result, users A and B are more likely to recognize the recommended plan 102B as the optimum plan.
  • Fig. 2 shows an example of a block diagram of a computer system.
  • the computing system comprises terminal devices 10 of a plurality of subjects and servers 200 , both of which are connected via a network 300 .
  • the terminal device 10 includes a controller 110 , an input device 170 , an output device 180 and a communication device 190 .
  • the controller 110 realizes a plan request module 111 and a plan judgment module 112 by executing programs recorded in memory.
  • the user terminal may be of any form, such as a personal computer, smart phone, PDA, or the like.
  • the server 200 includes a controller 210, an input device 171, an output device 181, a communication device 191, and a storage 120.
  • Controller 210 implements utility function estimation module 211, tolerance estimation module 212, and Pareto optimum search module 213 by executing programs in memory.
  • the storage 120 has a user management area 121 and a plan history area 122 .
  • the input devices 170, 171, the output devices 180, 181, and the communication devices 190, 191 are all well-known and commonly used in computers, so they will not be described in the following description.
  • a “module” is a function implemented by a controller executing a program, and may be translated as means, part, unit, function, or the like. Also, the modules may be implemented in hardware separately from the program or in cooperation with the program. The functioning of the modules will be referred to in the discussion of the flow charts below.
  • the user When a user newly creates a plan or corrects a plan, the user sends to the server 200 a request for coordination with other users who are interested parties, together with plan data.
  • the interested party may be specified by the user who accessed the server 200, or may be determined by the server.
  • the server 200 defines the user groups whose plans should be coordinated with each other, the server 200 initiates a dialogue with the user groups for plan optimization.
  • a bus route B of a bus company and a railway route A of a railway company are connected at a station X.
  • Each means of transportation operates its own vehicle and smoothly carries passengers to and from station X.
  • Each transportation system is in charge of a different route, so they have different constraints and utility functions.
  • each means of transportation In order to prevent passengers from staying too long at station X, each means of transportation must coordinate its operation plans with each other while considering its own constraints and utility functions.
  • the controller creating one optimized operation plan will be explained.
  • FIG. 4 is a flowchart relating to an example of the operation of the controller 210.
  • FIG. This flow chart will be described in connection with FIG.
  • the controller 210 starts the flow chart when receiving a new plan setting or a plan correction request from at least one terminal device of the bus company B or the railroad company A.
  • the plan request module 111 of the terminal device 10 creates data for the operation plan.
  • the controller 210 receives data on operation plans from each of the railway company A (user A) and the bus company B (user B).
  • the controller 210 stores the operation plan data in the form of a management table (FIG. 5) in the user management area 121 of the storage 120 (step S002).
  • This operation plan data is composed of passenger transport volumes for each time zone.
  • the operation plans of the railway company A and the bus company B have the same passenger transport volume in the time zone of "06:00-", but the passenger transport volume is different in the time zone after this.
  • the controller 210 repeats the re-creation and proposal of the operation plan until both parties reach an agreement, as will be described later, for the purpose of compensating for this difference and creating a unified operation plan for both parties.
  • the controller 210 moves to step S003 and checks whether or not all users (users A and B) have agreed to the operation plan proposed to users A and B. Since users A and B only requested the plan data and have not yet received consent notices from these users, the controller makes a negative decision in step S003 and proceeds to step S004.
  • step S004 the controller 210 refers to the user management area 121 of the storage, evaluates the plan data transmitted from the users A and B, and based on the evaluation results, selects candidate plans for the optimum plan for the users A and B. multiple calculations.
  • step S005 the controller proceeds to step S005, ranks a plurality of candidate plans, and determines the highest-ranked candidate plan as the first (round 1) recommended plan.
  • the controller stores the recommended plan in the plan history area 122 and transmits it to the terminal devices 10 of users A and B (step S006).
  • An example of a recommended plan is shown in FIG.
  • the controller 210 receives responses to the recommended plans from users A and B (step S001).
  • the plan determination module 112 of the terminal device 10 determines whether to accept or reject the recommended plan transmitted from the controller and responds. In the latter case, plan request module 111 modifies the recommended plan and sends a re-creation of the recommended plan to server 200 .
  • the controller 210 advances to step S002 to update and record the content of the response from the user in the management table of the user management area 121.
  • FIG. 7 shows the user management table.
  • the recommended plan is rejected by user A, and the recommended plan is changed. has been changed to On the other hand, user B has accepted the recommended plan, and the recommended plan itself is recorded in user B's management table.
  • step S003 the controller 210 refers to the user management table, and since the response from user A is refusal, the process proceeds to step S004.
  • a user-recommended plan (second round) is determined (step S005), and this recommended plan is updated and recorded in the plan history area 122 of the storage.
  • FIG. 8 shows a recommended plan management table in the plan history area 122. As shown in FIG. In the recommended plan (2nd round), the transportation volume of the recommended plan (1st round) "18:00-" is changed from "20" to "10".
  • the controller 210 transmits the recommended plan (second round) to users A and B (step S006). Since both users A and B accept the recommended plan (second round), the user management table is updated as shown in FIG. 9 through step S001 (step S002). Next, the controller refers to the user management table (FIG. 9), makes an affirmative decision in step S003, and terminates the flowchart of FIG.
  • FIG. 10 is a flow chart showing the details.
  • the controller 210 estimates a utility function between users even if plans created by a plurality of users are in a trade-off relationship with each other, and through interaction with a plurality of user devices, a plurality of A user-agreable plan can be quickly provided to a plurality of users by estimating an increase in the user's tolerance for agreeing on the plan.
  • Dialogue means that users A and B respond to the recommended plan sent from the controller 210, and the server changes the recommended plan based on this response and provides it to users A and B repeatedly. Responses include whether users A and B agree or disagree with the recommended plan. Send a create request to the controller. The dialogue continues until all users agree on the recommended plan. A plan agreed upon by all users becomes an optimized plan for all users.
  • the controller 210 estimates a utility function and an acceptable range for each of users A and B, and creates a recommended plan.
  • the controller evaluates the planning data returned by the user and sets utility functions and tolerances based on the evaluation results.
  • an evaluation index common to both is set.
  • Evaluation indices (Eval) include, for example, the total number of passengers transported per day (EvalA), the leveling of the number of passengers for each time period (EvalB), and the difference between the immediately preceding planned data (EvalC).
  • the controller 210 evaluates the planning data of users A and B shown in FIG. 5 based on these evaluation indices, the table (evaluation table) in FIG. 11 is obtained (step S401).
  • EvalA the daily passenger traffic volume of bus (B) is 80, while that of train (A) is 40, so EvalA for bus (B) is 1.00 and EvalA for train (A) is 0.50. is.
  • EvalB EvalB is 1.0 because the transport volume for each time slot of bus (B) is the same, and EvaB is 0.30 because transport volume for each time slot for train (A) is not the same.
  • Controller 210 stores the evaluation table in user management area 121 .
  • the controller 210 refers to the user management area 121 of the storage and compares the plan data sent by the user with the most recent plan data.
  • EvalC is 1.00 because both are the same.
  • buses (B) tend to change their operation plans flexibly, and the two are actually different, so EvalC is 0.50.
  • the weight in FIG. 11 is the importance of the evaluation index.
  • WeightA is the weight for EvalA
  • WeightB is the weight for EvalB
  • WeightC is the weight for EvalC.
  • Error is the error of the evaluation index for each round
  • Utility Value is the degree of satisfaction obtained by the utility function. Error is used for weight estimation.
  • the controller 210 compares the Eval value divided by the Error value to estimate the weight. That is, for example, even if the value of EvalA is greater than the value of EvalB, WeightA becomes smaller than WeightB if the value of ErrorA is very large.
  • the utility function estimating module 211 of the controller 210 defines the utility function by multiplying the value of each of a plurality of evaluation indices by the weight corresponding to the evaluation index, and summing the values for all the evaluation indices, as shown in Equation 1 below.
  • User A's utility function U A is expressed using Equation 1, where r is the requested data, n is the number of evaluation indices, and W Ai is the weight for the i-th index value.
  • User A's utility value UtilValue is calculated by Equation (1). Eval(r i ) is user A's i-th index value, and W Ai is the weight for user A's i-th index value.
  • the controller can similarly set user B's utility function and calculate its utility value UtilValue.
  • a simple statistical method may be used for predicting the utility function, or machine learning such as deep learning may be used.
  • the Pareto optimal search module 213 of the controller 210 simulates a combination of the hypothetical plan of user A and the hypothetical plan of user B in the solution space based on the utility function of user A and the utility function of user B. multiple occurrences.
  • the assumed plan is a virtual plan calculated by the Pareto optimum search module 213, and the plan consists of the passenger transport volume for each time period.
  • the Pareto optimal search module 213 uses genetic algorithms (for example, Shinya Watanabe, Tomoyuki Hiroyasu, Mitsunori Miki, "Multi-objective optimization by neighborhood culture genetic algorithm", Information Processing Society of Japan journal “Mathematical modeling and application , Vol. 43 No. SIG 10 (TOM 7), 2002).
  • the Pareto optimum search module 213 selects a set of Pareto optimum plans from many assumed plans as candidate plans with which user A and user B may agree (step S403). In FIG. 12, each of 1200-1206 is a candidate plan as Pareto optimal.
  • the controller 210 calculates the probability that users A and B agree with the recommended plan for each of the multiple Pareto-optimal plan candidates. As shown in FIG. 1B, the wider the tolerance, the higher the probability of agreement, so the tolerance estimation module 212 estimates the expansion or contraction of the tolerance from the user's response, and calculates the probability of agreement based on the result. For this estimation, the allowable range estimation module 212 calculates the amount of change in utility value for each round for each user, and calculates the average value of the amount of change for all rounds (step S404).
  • the fact that there is a large amount of change in the utility value between the parties trying to reach an agreement on the plan with each other proves that the user has the flexibility to change the plan data according to the plan calculated by the server. In other words, it can be assumed that the user's tolerance is expanding.
  • the average value of the amount of change for all rounds can be rephrased as the compromise average speed, and the higher the compromise average speed, the higher the agreement probability.
  • the compromise average speed is represented by the following Equation 2, where k is the number of rounds. User B's compromised average speed is similarly determined.
  • Controller 210 calculates the probability that all users agree to the recommended plan for each Pareto-optimal plan candidate (FIG. 12, 1200-1206) based on each user's utility function and compromised average speed (step S405).
  • U p,j be the utility value of user j for Pareto-optimal plan candidate p
  • agreement probability Cp for plan candidate p is expressed using Equation 3 below.
  • the agreement probability of any plan candidate p is calculated as the sum of the distances dj from each user's utility value. When the sum of the distances dj is zero, the probability of agreement is 1.0. The greater the sum of the distances dj , the smaller the probability of agreement. When the sum of the distances dj is infinite, the probability of agreement becomes zero.
  • the distance d j to the utility value of user j is calculated from the utility value U j , the compromised average speed, and the utility value up ,j of plan candidate p.
  • U j is the most desired utility value of user j, and the utility value that can be compromised is obtained by subtracting the compromised average speed from this utility value. If the utility value u p,j for user j for any plan candidate p is greater than or equal to a compromiseable utility value, the probability that user j will agree is high, so the distance d j will be zero. The smaller the utility value u p,j for user j for any plan candidate p is than the negotiable utility value, the less likely user j will agree, and thus the greater the distance d j .
  • the assumption is that the utility value minus the compromised average speed is always positive.
  • the server 200 selects the recommended plan (Round 1 in FIG. 8). is transmitted to the terminals of users A and B together with the agreement probability (step S006).
  • step S003 steps S401-S405 based on the responses of users A and B to the recommended plan for Round 1 (Round 1 in FIG. 9).
  • FIG. 13 shows the result of evaluation by the server for the responses of users A and B as Round2.
  • FIG. 14 is a distribution diagram of combinations of user A's assumed plan and user B's assumed plan with respect to the utility function estimated based on the evaluation result of Round 2, and is one point plan candidate.
  • ⁇ 1412 is a Pareto optimal candidate design.
  • the server calculates the probability of agreement for each candidate plan and transmits the candidate plan with the highest rank (candidate plan with the highest probability of agreement, Round 2 in FIG. 8) to users A and B as a recommended plan together with the probability of agreement. Since the recommended plan for Round 2 has been accepted by users A and B, the server makes an affirmative decision in step S003 and terminates the flowchart of FIG. As a result, the plan shown in Round 2 in FIG. 8 is determined as the optimum plan.
  • the controller 210 may change the weights depending on the user's response to the recommended plan.
  • User A rejected the recommended plan for Round 1 (FIG. 8) and changed the passenger transport volume at "18:00-" from “20" to “10.”
  • WeightB was increased from “0” to "1", judging that there was a strong tendency to try to eliminate variation in the amount.
  • the utility function evaluation module 212 allows User B to accept variations in passenger traffic volume, and tends to allow the difference from Round 1 to increase. has become stronger, WeightB is changed from “10” to "1", while WeightC is increased from “1” to “10".
  • the computer system can set the utility function and the agreement probability in accordance with the user's current tendency.
  • the agreement probability for each point in the Pareto optimal plans is shown in Table 1 (FIG. 12) and Table 2 (FIG. 14).
  • Table 3 shows the agreement probabilities of the respective plans (Rounds 1 and 2) in FIG.
  • the server estimates the utility function from each subject's response even if the mutual utility function is uncertain for the multiple subjects. , and since the allowable range can be estimated based on the compromised average speed, it is possible to rapidly set plan candidates that can be mutually agreed upon by multiple parties. As a result, it is possible to improve the efficiency of operations among multiple entities and to build smooth business cooperation.
  • the mutual trade-off relationship between multiple users includes trade-off relationships in at least one of the aforementioned multiple evaluation indicators.
  • the above-described embodiment is an example of the embodiment of the present invention, and the present invention is not limited to the above-described embodiment. That is, although the plan optimization between different types of transportation, such as trains and buses, has been described as an example, the present invention is not limited to this, and may be between trains or between buses. Also, although plan optimization between two users has been described, the present invention can also be applied to plan optimization between three or more users.
  • the present invention is not limited to optimizing plans for transportation facilities, but can also be applied to business forms in which production and distribution work together to optimize their plans for the purpose of promoting sales of commodities.
  • the user is not limited to a corporation, but may be a division of a corporation, an institution of a national or local government, an association, a collective, a group of multiple individuals, and the like.

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Abstract

Dans la présente invention, un serveur optimise des plans transmis à partir de chaque dispositif terminal d'une pluralité de dispositifs terminaux, et fournit un plan optimisé aux utilisateurs respectifs de la pluralité de dispositifs terminaux. Un dispositif de commande du serveur : évalue les plans transmis à partir de la pluralité de terminaux ; estime une fonction utilitaire de chaque utilisateur de la pluralité d'utilisateurs sur la base du résultat d'évaluation ; définit un plan de recommandation à la pluralité d'utilisateurs sur la base de la fonction utilitaire ; transmet le plan recommandé aux dispositifs terminaux respectifs de la pluralité d'utilisateurs ; répète l'évaluation et la définition d'un plan recommandé jusqu'à ce que la pluralité d'utilisateurs acceptent le plan recommandé ; et définit, en tant que plan optimisé, le plan recommandé qui a été accepté par la pluralité d'utilisateurs.
PCT/JP2022/022222 2021-06-30 2022-05-31 Système informatique WO2023276534A1 (fr)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021033302A1 (fr) * 2019-08-22 2021-02-25 日本電気株式会社 Dispositif de négociation côté réception d'ordre, procédé de négociation côté réception d'ordre et programme de négociation côté réception d'ordre

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021033302A1 (fr) * 2019-08-22 2021-02-25 日本電気株式会社 Dispositif de négociation côté réception d'ordre, procédé de négociation côté réception d'ordre et programme de négociation côté réception d'ordre

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
KONDO, AI: "Proposal for Mixed Matching System with Auto-Negotiation Platform", OPERESHONZU RISACHI - COMMUNICATIONS OF THE OPERATIONS RESEARCH SOCIETY OF JAPAN [OPERATIONS RESEARCH AS A MANAGEMENT SCIENCE RESEARCH], NIHON OPERESHONZU RISACHI GAKKAI, TOKYO, JP, vol. 66, no. 1, 7 January 2021 (2021-01-07), JP , pages 18 - 24, XP009542260, ISSN: 0030-3674 *
TOIDA, AI; IKADA, SATOSHI: "1C3-OS-6a-04 Proposal of Marge Method for Transportation Plans using Cooperative Game and Multi-Issue Negotiation", THE 34TH ANNUAL CONFERENCE OF THE JAPANESE SOCIETY FOR ARTIFICIAL INTELLIGENCE (JSAI); [ONLINE]; JUNE 9-12, 2020, vol. 34, 1 January 2020 (2020-01-01) - 12 June 2020 (2020-06-12), pages 1 - 4, XP009542292, DOI: 10.11517/pjsai.JSAI2020.0_1C3OS6a04 *

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