Multi Objective Dynamic Job Shop Scheduling using Composite Dispatching Rule and Reinforcement Learning

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概要

• 論文の詳細を見る

• The applications of composite dispatching rules for multi objective dynamic scheduling have been widely studied in literature. In general, a composite dispatching rule is a combination of several elementary dispatching rules, which is designed to optimize multiple objectives of interest under a certain scheduling environment. The relative importance of elementary dispatching rules is modeled by weight factors. A critical issue for implementation of composite dispatching rule is that the inappropriate weight values may result in poor performance. This paper presents an offline scheduling knowledge acquisition method based on reinforcement learning using simulation technique. The scheduling knowledge is applied to adjust the appropriate weight values of elementary dispatching rules in composite manner with respect to work in process fluctuation of machines during online scheduling. Implementation of the proposed method in a two objectives dynamic job shop scheduling problem is demonstrated and the results are satisfactory.

• 社団法人 電気学会の論文
• 2011-06-01

著者

• Lin Hao

  Graduate School of Information, Production and Systems, Waseda University

• Chen Xili

  Graduate School of Information, Production and Systems, Waseda University

• Chen Xili

  Graduate School Of Information Production And Systems Waseda University

• Hao XinChang

  Graduate School of Information, Production and Systems, Waseda University

• Lin Hao

  Graduate School Of Information Production And Systems Waseda Univ.

• Murata Tomohiro

  Graduate School Of Information Production And Systems Waseda Univ.

• Hao Xinchang

  Graduate School Of Information Production And Systems Waseda University

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