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Transactions of the Institute of Systems, Control and Information Engineers Vol. 26 (2013), No. 4

ISIJ International
belloff
ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668
Publisher: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

Backnumber

  1. Vol. 37 (2024)

    1. No.1

  2. Vol. 36 (2023)

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  3. Vol. 35 (2022)

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  12. Vol. 26 (2013)

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  26. Vol. 12 (1999)

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  27. Vol. 11 (1998)

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  28. Vol. 10 (1997)

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  29. Vol. 9 (1996)

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  30. Vol. 8 (1995)

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  31. Vol. 7 (1994)

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  32. Vol. 6 (1993)

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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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Transactions of the Institute of Systems, Control and Information Engineers 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

Abstract

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

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Ris

Applying Multi-agent Reinforcement Learning to Autonomous Distributed Real-time Scheduling

Koji Iwamura, Norihisa Mayumi, Yoshitaka Tanimizu, Nobuhiro Sugimura

pp. 129-137

DOI:

10.5687/iscie.26.129

Abstract

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.

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Applying Multi-agent Reinforcement Learning to Autonomous Distributed Real-time Scheduling

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Hierarchical-game Based Negotiation Protocol for Effective Supply Chain Network

Fang Yu, Toshiya Kaihara, Nobutada Fujii

pp. 138-146

DOI:

10.5687/iscie.26.138

Abstract

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.

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Hierarchical-game Based Negotiation Protocol for Effective Supply Chain Network

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