2007 Fiscal Year Final Research Report Summary
Study on the Production Scheduling and Logistic By Advanced Evolutionary Algorithms
| Project/Area Number |
17510138
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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 |
Social systems engineering/Safety system
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| Research Institution | Waseda University |
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Principal Investigator |
GEN Mitsuo Waseda University, Graduate School of Information, Production, and Systems, Professor (20095003)
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| Co-Investigator(Kenkyū-buntansha) |
KATAYAMA Hiroshi Waseda University, Department of Industrial and Management Systems Engineering, Professor (60091849)
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| Project Period (FY) |
2005 – 2007
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| Keywords | e-Manufacturing / e-Logistics / Evolutionary Algorithms / Fuzzy Logic Control / Advanced Planning and Scheduling / Supply Chain Management / Manufacturing & Logistics Systems / Integrated Scheduling |
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| Research Abstract |
In this research, we provided a comprehensive survey of the current state-of-the-art in the use of EA in manufacturing and logistics systems.
Advanced planning and scheduling (APS) refers to a manufacturing management process by which raw materials and production capacity are optimally allocated to meet demand. APS is especially well-suited to environments where simpler planning methods cannot adequately address complex trade-offs between competing priorities. In this research, we analyze the structure of integrated manufacturing system, and extract the mathematical model in advanced planning and scheduling field. According to the flexibility in APS, a multistage operation-based GA is developed as an effective approach for representing the information of flexible resources assignment in combinatorial scheduling problem. In terms of characters of different problem, effective local search techniques are combined to obtain active schedule, i.e. critical path local search, left shift local search.
With the development of economic globalization and extension of worldwide electronic marketing, global enterprise services supported by universal supply chain and world-wide logistics become imperative for business world. How to manage logistics system efficiently thus has become a key issue for almost, all of the enterprises to reduce their various costs in today's keenly competitive environment of business, especially for many multinational companies. In this research, we concern the logistics systems using GM. lb solve the logistics problems, we proposed different GA approaches, Prufer number-based encoding method, priority-based encoding method, extended priority-based GA, and random path-based GA. The effectiveness and efficiency of GA approaches were investigated with various scales of logistics problems by comparing with recent related researches.
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