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Limited Re-Sequencing for Mixed-Models with Multiple Objectives, Part II: A Permutation Approach

Limited Re-Sequencing for Mixed-Models with Multiple Objectives, Part II: A Permutation Approach
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摘要 This research presents an approach to solving the limited re-sequencing problem for a JIT system when two objectives are considered for multiple processes. One objective is to minimize the number of setups;the other is to minimize the material usage rate [1]. For this research effort, each unique permutation of the problem’s demand structure is noted, and used as a mechanism for finding subsequent sequences. Two variants of this permutation approach are used: one employs a Monte-Carlo simulation, while the other employs a modification of Ant-Colony Optimization to find sequences satisfying the objectives of interest. Problem sets from the literature are used for assessment, and experimentation shows that the methodology presented here outperforms methodology from an earlier research effort [3]. This research presents an approach to solving the limited re-sequencing problem for a JIT system when two objectives are considered for multiple processes. One objective is to minimize the number of setups;the other is to minimize the material usage rate [1]. For this research effort, each unique permutation of the problem’s demand structure is noted, and used as a mechanism for finding subsequent sequences. Two variants of this permutation approach are used: one employs a Monte-Carlo simulation, while the other employs a modification of Ant-Colony Optimization to find sequences satisfying the objectives of interest. Problem sets from the literature are used for assessment, and experimentation shows that the methodology presented here outperforms methodology from an earlier research effort [3].
机构地区 Schools of Business
出处 《American Journal of Operations Research》 2012年第1期10-21,共12页 美国运筹学期刊(英文)
关键词 PERMUTATION HEURISTIC SEQUENCING Discrete Optimization Permutation Heuristic Sequencing Discrete Optimization

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