A novel initialization method for solving Flexible Job-shop Scheduling Problem

A novel initialization method was proposed for genetic algorithm (GA) to generate high-quality initialization population so as to solve flexible job-shop scheduling problem (FJSP). The novel initialization method consists of two sub-methods: global selection (GS) and local selection (LS). GS is used to find different initial assignments in different runs of the algorithm, and to enhance the capability of exploring search space considering the workload of all machines, while LS can find the shortest occupation time machine in alternative machine set of each job. To prove the efficiency of this initialization method, various benchmark data taken from the literature are tested, and the computation results show that this method can shorten the computational time and generate better results.