Optimization of job shop scheduling problems using improved teaching-learning-based optimization algorithm

カテゴリ: 部門大会

論文No: SS2-1

グループ名: 【C】平成29年電気学会電子・情報・システム部門大会講演論文集

発行日: 2017/09/06

タイトル(英語): Optimization of job shop scheduling problems using improved teaching-learning-based optimization algorithm

著者名: LI LINNA(Waseda University),Weng Wei(Waseda University),Fujimura Shigeru(Waseda University)

著者名(英語): Linna Li|Wei Weng|Shigeru Fujimura

キーワード: job shop scheduling|optimization|teaching-learning-based optimization algorithm

要約(日本語): In this paper, an improved teaching?learning-based optimization algorithm (TLBO) is proposed to solve the job shop scheduling problem(JSP). JSP is a strongly NP-hard combinatorial optimization problem. It is difficult to solve the problem to the optimum in a reasonable time. TLBO has a considerable potential when compared to the best-known heuristic algorithms for scheduling problems. To make TLBO become more suitable for solving JSP, a new coding method is used. New learners are introduced and special local search operators are incorporated into TLBO to balance the diversification and intensification. To show effectiveness of the improved TLBO, numerical results based on some benchmark instances and the comparisons with the traditional TLBO and the best known lower bounds are provided.

PDFファイルサイズ: 609 Kバイト