Multi-objective flexible job shop scheduling problem using differential evolution algorithm

Flexible job shop scheduling problem (FJSP) is very complex to be controlled, and it is a problem which inherits job shop scheduling problem (JSP) characteristics. FJSP has two sub-problems: routing sub-problem and scheduling sub-problem. In this paper, improved differential evolution (DE) algorithm is presented for multi-objective FJSP. Minimization of three objective functions includes maximum completion time, workload of the most loaded machine and total workload of all machines. The improved algorithm has a well-designed mutation and crossover operator, and uses a Pareto non-dominated sorting method. Computational simulations and comparisons demonstrate the effectiveness of the proposed improved DE algorithm