Production job-shop scheduling using genetic algorithms

This paper explains the application of genetic algorithms (GAs) to job-shop scheduling problems, minimizing a makespan of the jobs. Given combinatorial problems which are subject to precedence and resource constraints, it is important to develop an efficient representational scheme and effective genetic operators of the GAs for better performance. The GA features a number of advantages. They are robust in the sense that they provide good solution on a wide range of the problem, in addition they can easily be modified with respect to the objective function and constraints. For better performance, we use conjointly the assignment and scheduling problems in order to create new representational scheme (parallel form) for a crossover and mutation operators. These operators can exchange meaningful ordering information of parents effectively without producing illegal solutions. Simulation results show that our parallel genetic operators are very powerful and very suitable to job-shop scheduling problems