An enhanced model for the integrated production and transportation problem in a multiple vehicles environment

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• 作者：He-Yau Kang ; W. L. Pearn ; I-Ping Chung ; Amy H. I. Lee
• 关键词：Semiconductor manufacturing ; Turnkey service ; Production and transportation problem ; Mixed integer linear programming ; Genetic algorithm
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：20
• 期：4
• 页码：1415-1435
• 全文大小：1,462 KB
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• 作者单位：He-Yau Kang (1)
  W. L. Pearn (2)
  I-Ping Chung (2)
  Amy H. I. Lee (3)
  
  1. Department of Industrial Engineering and Management, National Chin Yi University of Technology, Taichung, Taiwan
  2. Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, Taiwan
  3. Department of Technology Management, Chung Hua University, Hsinchu, Taiwan
  
• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479

文摘

Solving an integrated production and transportation problem (IPTP) is a very challenging task in semiconductor manufacturing with turnkey service. A wafer fabricator needs to coordinate with outsourcing factories in the processes including circuit probing testing, integrated circuit assembly, and final testing for buyers. The jobs are clustered by their product types, and they must be processed by groups of outsourcing factories in various stages in the manufacturing process. Furthermore, the job production cost depends on various product types and different outsourcing factories. Since the IPTP involves constraints on job clusters, job-cluster dependent production cost, factory setup cost, process capabilities, and transportation cost with multiple vehicles, it is very difficult to solve when the problem size becomes large. Therefore, heuristic tools may be necessary to solve the problem. In this paper, we first formulate the IPTP as a mixed integer linear programming problem to minimize the total production and transportation cost. An efficient genetic algorithm (GA) is proposed next to tackle the problem when it becomes too complicated. The objectives are to minimize total costs, where the costs include production cost and transportation cost, under the environment with backup capacities and multiple vehicles, and to determine an appropriate production and distribution plan. The results demonstrate that the proposed GA model is an effective and accurate tool.