| Project/Area Number |
12680456
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
社会システム工学
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| Research Institution | Kobe University (2001)
University of Marketing and Distribution Sciences (2000) |
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Principal Investigator |
KAIHARA Toshiya Graduate School of Science and Technology, Kobe University, Associate Profressor, 大学院・自然科学研究科, 助教授 (70289114)
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| Project Period (FY) |
2000 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2001: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2000: ¥800,000 (Direct Cost: ¥800,000)
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| Keywords | Supply chain management / Multi-agent / Market-oriented programming / Virtual market / Multi-criteria optimization / 多目的最適化手法 |
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| Research Abstract |
Supply chain management (SCM) is now recognized as one of the best means by which enterprises can make instant improvements to their business strategies and operations. SCM, however, is generally based on the simple TOC (Theory Of Constraints) concept, and is not always concerned with Pareto optimal solutions in product distribution. Since market price systems constitute a well-understood class of mechanisms that under certain conditions provide effective decentralisation of decision making with minimal communication overhead, we propose SCM based on market-oriented programming in this research. In market-oriented programming we take a metaphor of economy computing multi-agent behavior literally, and directly implement the distributed computation as a market price system. We define the agent activities to negotiate the tradeoffs of acquiring different resources, so as to realize the multi-echelon optimization. Several simulation experiments on the supply chain model with multi-echelon structure clarify the market dynamics that emerge through the agent negotiations. It is confirmed that careful constructions of the decision process according to economic principles can lead to Pareto optimal resource allocations in SCM, and the behavior of the system can be analyzed in economic terms.
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