A modified Variable Neighborhood Search for minimizing the makespan on identical parallel machines

摘要

Variable Neighborhood Search (VNS) is one of the most recent metaheuristics used for problem solving in which a systematic change of neighborhood within a local search is carried out. The idea is to build the best local search and shake operations based on neighbourhood structure available. In this paper, a modified version of VNS algorithm proposed for identical parallel machines scheduling problems with the objective function of minimizing makespan. The proposed VNS algorithm was tested 150 randomly generated problems with different jobs and machines. The results gained by modified VNS (MVNS) algorithm are presented and compared with the both Genetic Algorithm (GA) and Longest Processing Time (LPT) solutions. It is concluded that the MVNS algorithms outperform the both GA and LPT.