Robust Optimization Method based on Hybridization of GA and MMEDA for Resource Constraint Project Scheduling with Uncertainty

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2017/07/01

タイトル(英語): Robust Optimization Method based on Hybridization of GA and MMEDA for Resource Constraint Project Scheduling with Uncertainty

著者名: Jing Tian (IPS, Waseda University), Xinchang Hao (IPS, Waseda University), Tomohiro Murata (IPS, Waseda University)

著者名(英語): Jing Tian (IPS, Waseda University), Xinchang Hao (IPS, Waseda University), Tomohiro Murata (IPS, Waseda University)

キーワード: Robust Scheduling，Estimation Distribution of Algorithm，Markov Network，Resource Constrained Scheduling Problem，Multi-objective

要約(英語): Inspired by the cooperative co-evolutionary paradigm, this paper presents a two-stage algorithm hybrid genetic algorithm (GA) and multi-objective Markov network based EDA (MMEDA), to solve the robust scheduling problem for resource constrained scheduling problem (RCSP) with uncertainty. Within the two-stage architecture based on sequential co-evolutionary paradigm, GA is used to find feasible solution for sequencing sub-problem in the first stage, and in the second stage, MMEDA is adopted to model the interrelation for resource allocation and calculate the Pareto set with the scenario based approach. Moreover, one problem-specific local search with considering both makespan and robustness is designed to increase the solution quality. Experiment results based on a benchmark (PSPLIB) and comparisons demonstrate that our approach is highly effective and tolerant of uncertainty.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.137 No.7 （2017） 特集：平成28年電子・情報・システム部門大会

本誌掲載ページ: 957-966 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/137/7/137_957/_article/-char/ja/