A coevolutiouary genetic algorithm for job shop scheduling problems

Scheduling problems in general consist of a set of concurrent conflicting goals that are to be satisfied using a limited number of resources. They arise in diverse areas such as transportation and communication systems, logistics and manufacturing environments. The job shop scheduling problem (JSSP) is considered to be one of the hardest combinatorial optimisation problems. The coevolutionary genetic algorithm (CEGA) is proposed for solving the job shop scheduling problem. The JSSP is divided into a number of subproblems, and the CEGA encourages the parallel evolution of partial solutions. This approach combines and extends previous evolutionary computation techniques used to solve similar optimisation problems. Initial computational results indicate that the CEGA performs favourably when compared to other approaches and that a number of high quality solutions to the problem are produced on each run.