An Effective Markov Random Fields based Estimation of Distribution Algorithm and Scheduling of Flexible Job Shop Problem
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- Hao Xinchang
- Graduate School of Information, Production and Systems, Waseda University
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- Tian Jing
- Graduate School of Information, Production and Systems, Waseda University
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- Lin Hao Wen
- Harbin Institute of Technology Shenzhen Graduate School
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- Murata Tomohiro
- Graduate School of Information, Production and Systems, Waseda University
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During the past several years, a large number of studies have been conducted in the area of flexible job shop problems. Intelligent manufacturing planning and scheduling solutions that are based on meta-heuristic methods, such as the simulated annealing and particle swarm optimization, have become common techniques for finding satisfactory solutions within reasonable computational times in real scenarios. However, only a limited number of studies have analyzed the effects of interdependent relationships associated with various decision factors considered for the complex problems. This paper presents a Markov network based estimation of distribution algorithm to address the flexible job shop scheduling problem. The proposal uses a subclass of estimation of distribution algorithms where the effects between decision variables are represented as an undirected graph model. Furthermore, a critical path-based local search method is adopted by the proposed algorithm to achieve better performance. We present an empirical validation for the proposal by applying it to solve various benchmark flexible job shop problems.
収録刊行物
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- 電気学会論文誌Ｃ（電子・情報・システム部門誌）
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電気学会論文誌Ｃ（電子・情報・システム部門誌） 134 (6), 796-805, 2014
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390282679583716992
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- NII論文ID
- 130004870056
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 025597756
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
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