A Hybrid Multiobjective Evolutionary Approach for Flexible Job-Shop Scheduling Problems

Abstract EN

This paper addresses multiobjective flexible job-shop scheduling problem (FJSP) with three simultaneously considered objectives: minimizing makespan, minimizing total workload, and minimizing maximal workload.

A hybrid multiobjective evolutionary approach (H-MOEA) is developed to solve the problem.

According to the characteristic of FJSP, a modified crowding distance measure is introduced to maintain the diversity of individuals.

In the proposed H-MOEA, well-designed chromosome representation and genetic operators are developed for FJSP.

Moreover, a local search procedure based on critical path theory is incorporated in H-MOEA to improve the convergence ability of the algorithm.

Experiment results on several well-known benchmark instances demonstrate the efficiency and stability of the proposed algorithm.

The comparison with other recently published approaches validates that H-MOEA can obtain Pareto-optimal solutions with better quality and/or diversity.