An improved genetic algorithm to the job shop scheduling problem

Yu Yan-Fang and Ying Yue

Abstract

For a dynamic, changeable agile manufacturing system, a dynamic job shop scheduling approach is one of effective measures for production management. In this paper, an improved genetic algorithm is proposed to the job shop scheduling problem. The experimental results suggest that this improved genetic algorithm is correct, feasible and available. The data-driven optimization method is a new approach to study the agile manufacturing system.