An efficient genetic algorithm for flexible job-shop scheduling problem

Abstract

In this paper a genetic algorithm (GA) is developed to create a feasible and active schedule for the flexible job-shop scheduling problems with the aims of minimizing completion time of all jobs, i.e. makespan. In the proposed algorithm, an enhanced solution coding is used. To generate high quality initial populations, we designed an Operation order-based Global Selection (OGS), which is taken into account both the operation processing times and workload of machines while is assigning a machine to the operation which already is ordered randomly in chromosome `operation sequence part. The precedence preserving order-based crossover (POX) and uniform crossover are used appropriately and furthermore an intelligent mutation operator is carried out. The proposed algorithm is applied on the benchmark data set taken from literature. The results demonstrated efficiency and effectiveness of the algorithm for solving the flexible job shop scheduling problems.