AN IMPROVED GENETIC ALGORITHM FOR RESOURCE-CONSTRAINED FLEXIBLE JOB-SHOP SCHEDULING

Flexible job-shop scheduling could effectively lower the costs of manpower and materials. However, there is little report on the scheduling algorithm or optimization model for the optimization of production resources. This paper proposes a resource-constrained flexible job-shop scheduling algorithm based on an improved genetic algorithm. Firstly, an optimization model was established for resource-constrained FJSP, together with the objective functions about resources and time, as well as constraints. Next, the multi-objective genetic algorithm (MOGA) was combined with the whale optimization algorithm (WOA) into a combinatory method to solve the proposed model for resource-constrained FJSP. Experimental results show that the combination enhances the adaptivity of crossover and mutation probabilities, and improves the local search ability, presenting an effective solution to the FJSP.