Job shop scheduling with a genetic algorithm and machine learning

Abstract

Dynamic job shop scheduling has been proven to be an intractable problem for analytical procedures. Recent advances in computing technology, especially in artificial intelligence, have alleviated this problem by intelligently restricting the search space considered, thus opening the possibility of obtaining better results. Researchers have used various techniques that were developed under the general rubric of artificial intelligence to solve job shop scheduling problems. The most common of these have been expert systems, genetic algorithms and machine learning. Of these, we identify machine learning and genetic algorithms to be promising for scheduling applications in a job shop. In this paper, we propose to combine complementarily the strengths of genetic algorithms and induced decision trees, a machine learning technique, to develop a job shop scheduling system. Empirical results, using machine learning for releasing jobs into the shop floor and a genetic algorithm to dispatch jobs at each machine, are promising.