Flexible Job Shop Scheduling Multi-objective Optimization Based on Improved Strength Pareto Evolutionary Algorithm

Scheduling for the flexible job shop is very important in fields of production management. To solve the multi objective optimization in flexible job shop scheduling problem (FJSP), the FJSP multi-objective optimization model is constructed. The cost, quality and time are taken as the optimization objectives. An improved strength Pareto evolutionary algorithm (SPEA2+) is put forward to optimize the multi-objective optimization model parallelly. The algorithm uses a new model of a Multi-objective genetic algorithm that includes more effective crossover and could obtain diverse solutions in the objective and variable spaces to archive the Pareto optimal sets for FJSP multi-objective optimization. Then an approach based on fuzzy set theory was developed to extract one of the Pareto-optimal solutions as the best compromise one. The optimization results were compared with those obtained by NSGA-II and POS. At last, an instance of flexible job shop scheduling problem in automotive industry is given to illustrate that the proposed method can solve the multi-objective FJSP effectively.