Optimal production planning and scheduling under big data environment

2017 Fiscal Year Final Research Report

• Project/Area Number: 15K00357
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Soft computing
• Research Institution: Fuzzy Logic Systems Institute
• Principal Investigator: Gen Mitsuo 一般財団法人ファジィシステム研究所, 研究部, 特別研究員 (20095003)
• Co-Investigator(Kenkyū-buntansha): 川上 浩司 京都大学, デザイン学ユニット, 特定教授 (90214600)
• Co-Investigator(Renkei-kenkyūsha): LIN Lin 一般財団法人ファジィシステム研究所, 研究部, 主任研究員 (90434331)
• Research Collaborator: HAO Xinchang ; ZANG Wenqiang ; YUN Yongsu
• Project Period (FY): 2015-04-01 – 2018-03-31
• Keywords: 進化算法(EA) / 遺伝的アルゴリズム(GA) / 分布推定アルゴリズム(EDA) / 生産計画・スケジューリング / 物流配送・ルーチング問題 / ビッグデータ

Outline of Final Research Achievements

Production steps of various parts such as semiconductor elements are manufactured on a real time basis and processing is required within a predetermined time by each element. If the predetermined process can not be executed within this constraint, it becomes a defective product and affects the production efficiency. Generally, the production scheduling problem of semiconductor devices is formulated as a large scale mixed integer planning model (MIP), and in mathematical planning software it is difficult to obtain the optimum schedule in minutes. In particular, with nonlinear MIP models, it is impossible to obtain optimal solutions with conventional software.
In this research, we developed an optimal scheduling method based on hybrid evolutionary calculation method, announced the effectiveness of the proposed method by numerical experiment, and published a paper in international journal

Free Research Field

進化算法による生産スケジューリング