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システム/制御/情報 Vol. 26 (2013), No. 4

ISIJ International
belloff
オンライン版ISSN: 2185-811X
冊子版ISSN: 1342-5668
発行機関: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

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  30. Vol. 8 (1995)

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  33. Vol. 5 (1992)

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  34. Vol. 4 (1991)

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  35. Vol. 3 (1990)

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  36. Vol. 2 (1989)

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  37. Vol. 1 (1988)

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キーワードランキング

21 Nov. (Last 30 Days)

システム/制御/情報 Vol. 26 (2013), No. 4

The Hybrid Approach of LCO and SA to Solve Job-shop Scheduling Problem

Yasumasa Tamura, Ikuo Suzuki, Masahito Yamamoto, Masashi Furukawa

pp. 121-128

DOI:

10.5687/iscie.26.121

抄録

A Job-shop Scheduling Problem (JSP) is one of the combinatorial optimization problems. JSP appears as a basic scheduling problem in many situations of a manufacturing system and many methods for JSP have been invented. This study examines two effective methods, SA and LCO, for JSP and propose a hybrid method based on them. As a result of the experiments, the proposed method can find a good solution with short computational time. Summarizing this study, the proposed method is efficient in the early or middle search of the optimization.

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自律分散型リアルタイムスケジューリングへのマルチエージェント強化学習の適用

岩村 幸治, 眞弓 宗久, 谷水 義隆, 杉村 延広

pp. 129-137

DOI:

10.5687/iscie.26.129

抄録

Autonomous Distributed Manufacturing Systems (ADMS) have been proposed to realize flexible control structures of manufacturing systems. In the previous researches, a real-time scheduling method based on utility values has been proposed and appliedto the ADMS. In the proposed method, all the job agents and the resource agents evaluate the utility values for the cases where the agent selects the individual candidate agents for the next machining operations. Multi-agent reinforcement learning is newly proposed and implemented to the job agents and resource agents, in order to improve their coordination processes. In the reinforcement learning method, an agent must be able to sense the status of the environment to some extent and must be able to takeactions that affect the status. The agent also must have a goal or goals relating to the status of the environment. The status, the action and the reward are defined for the individual job agents and the resource agents to evaluate the suitable utility values based on the status of the ADMS.

mendeley

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自律分散型リアルタイムスケジューリングへのマルチエージェント強化学習の適用

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mendeley

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効率的サプライチェーンネットワークのための階層型ゲームに基づく交渉プロトコル

余 芳, 貝原 俊也, 藤井 信忠

pp. 138-146

DOI:

10.5687/iscie.26.138

抄録

This paper focuses on single-attribute negotiation protocol between multiple Manufacture Agents (MA) and multiple Material Supplier Agents (MSA). A hierarchical-game based negotiation protocol is proposed. It is a three-layer game, where the first layer game is based on the results of the second and the third layer games. Two-person game is used to find the optimal trade partnerships to maximize the whole profit of Supply Chain Network (SCN) in the first layer. The second layer games aim to findall the possible coalitions using cooperative game. However, they are not necessary for all the time. They exist only at the situation where the order of MA is out of the ability of MSA. Then, the third layer games are used to determine the final strategies between MAs and MSAs or all the possible coalitions found in the second layer games. Stackelberg equilibrium is introduced to resolve the conflict of the interests of the two sides. What we should pay attention to is the second and the third layer games are nested inside the first layer game. Simulations are provided to verify the effectiveness and the feasibility of the proposed protocol. The optimal setting of the proposed protocol under given condition is obtained by the performance analysis.

mendeley

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効率的サプライチェーンネットワークのための階層型ゲームに基づく交渉プロトコル

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mendeley

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