Genetic neuro-scheduler: A new approach for job shop scheduling

In this paper, a hybrid approach between two new techniques, genetic algorithms and artificial neural network is described for generating job shop schedules in a discrete manufacturing environment based on nonlinear multiobjective function. Genetic algorithm (GA) is used as an effective search technique for finding an optimal schedule via population of gene strings which represent alternative feasible schedulers. GA propagates new population of genes through number of cycles called generations by implementing natural genetic mechanisms. Specifically gene strings should have a structure that imposes the most common restrictive constraint: a precedence constraint. The other technique is an artificial neural network that perfroms multiobjective schedule evaluation. The intention is to establish an effective model that maps a complex set of scheduling criteria (i.e. flowtime, lateness) to appropriate values provided by experienced schedulers. The proposed approach is prototyped and tested on four different job shop scheduling problems based on problem size, namely; small, medium, large, and a sample problem provided by a company. The comparative results indicate that the proposed approach is consistently better than those of heuristic algorithms used extensively in industry.