Advanced scheduling problem in MTO manufacturing using multi-objective hybrid genetic algorithm

カテゴリ: 部門大会

論文No: OS5-4

グループ名: 【C】平成15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: 2003/08/29

タイトル(英語): Advanced scheduling problem in MTO manufacturing using multi-objective hybrid genetic algorithm

著者名: 金 官禹(東京都立科学技術大学),玄 光男(早稲田大学),山崎 源治(東京都立科学技術大学)

著者名(英語): KwanWoo Kim(Tokyo Metropolitan Institute of Technology),Mitsuo Gen(Waseda University),Genji Yamazaki(Tokyo Metropolitan Institute of Technology)

キーワード: advanced scheduling|hybrid genetic algorithm|make-to-order|work-in-process|multi-objective fuctions

要約(日本語): Recently, manufacturers with make-to-order (MTO) tend to use a flexible flows strategy, where they manufacture products to customer specification in job shop type production and small batch production. In this environment, scheduling problems have complexities. The space of feasible schedules grows exponentially as there are increasing in the number of different orders that must be processed, number of operations required by each order, batch size of each order, and the size and complexity of the installation of interest.

In this paper, we propose an advanced scheduling problem to generate the schedules considering resource constraints and precedence constraints in MTO manufacturing. Precedence of work-in-process and resources constraints, in advanced scheduling problems, have recently emerged as one of the main constraints. The advanced scheduling problems is formulated as a multi-objective mathematical model for generating operation schedules which are obeyed resources constraints, alternative resources and the precedence constraints in MTO manufacturing. For effectively solving the advanced scheduling problem, the multi-objective hybrid genetic algorithm (m-hGA) is proposed in this paper. The m-hGA is to minimize the makespan, total flow time of order, and maximum tardiness for each order, simultaneously. The m-hGA approach with local search-based mutation through swap mutation is developed to solve the advanced scheduling problem. Experimental result is presented for advanced scheduling problems of various sizes to describe the performance of the proposed m-hGA

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