An Improved Differential Evolution Algorithm for Solving a Distributed Flexible Job Shop Scheduling Problem

Abstract

The single-factory manufacturing is gradually transiting to the multi-factory collaborative production under the globalization. How to schedule the collaborative production among multiple factories effectively is extremely urgent. The distributed flexible job shop scheduling problem (DFJSP) is studied. To begin with, a DFJSP mixed integer linear programming model is formulated to minimize the makespan. Then, an improved differential evolution simulated annealing algorithm (IDESAA) is proposed. Two crossover and mutation operators are designed. Simulated annealing is integrated to local search the best solution(s). The tournament selection is employed to select the offspring. Finally, the numerical experiments are conducted and the results show that the proposed algorithm can solve the DFJSP effectively and efficiently.