バッファ無しジョブショップスケジューリング問題に対する遺伝アルゴリズムの適用

抄録

A genetic algorithm (GA) is proposed to solve no-buffer jobshop scheduling problems. In the case of applying the standard GA to these problems, no-buffer constraint leads to a deadlock state in the meaning that an operation cannot be processed on the next machine in a pre-defined machine sequence. Because it causes many infeasible solutions, the standard GA cannot perform an efficient search. In this paper, we propose a new semi-active decoding method which avoids the deadlock to generate a feasible solution. Computer experiments on some modified benchmark problems show that the proposed GA performs efficient search and obtains equivalent or better schedules than other algorithms proposed previously.