An Improved Nondominated Sorting Genetic Algorithm-II for Multi-objective Flexible Job-shop Scheduling Problem

This study focuses on the multi-objective flexible job-shop scheduling problem (MOFJSP) considering three objectives, namely maximal completion time, total workload of machines and maximal machine workload. An improved nondominated sorting genetic algorithm-II (INSGA-II) is developed to solve the MOFJSP. Specifically, a probability-based uniform crossover operator called “PEPPX” is designed to reach a tradeoff between exploration and exploitation. A novel”pruning-regenerating” mechanism (PRM), which can remove the redundant solutions in an elitist population and generate a new population via variable neighborhood search (VNS), is proposed to maintain the population diversity. Three objective-oriented local search operators for the VNS are elaborately designed based on critical path. Furthermore, a left-shift decoding method is applied to attain active schedules. Numerical experiments including two case studies confirm the effectiveness of the proposed INSGA-II.