作業者を考慮したジョブショップスケジューリング

（ニューラルネットワークを用いた優先規則の学習）

抄録

This paper deals with a job shop scheduling problem that considers the skill difference of operators. A priority rule for this problem is constructed using a neural network. A neural network learns from input-output pairs extracted from a schedule optimized using an IP solver for small-scale problem instances. Through numerical experiments, it was found that trained neural network by the proposed method showed better performance than the existing priority rules even in larger scale problem instances.