A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling

TL;DR

This paper introduces a tailored NSGA-III-based multi-objective evolutionary algorithm for flexible job shop scheduling, incorporating smart initialization, diverse crossover operators, automatic parameter tuning, and local search strategies to improve solution quality and efficiency.

Contribution

It presents a novel integration of various enhancement techniques into NSGA-III specifically for FJSP, demonstrating superior performance over standard implementations.

Findings

Achieves better scheduling solutions with less computational effort.

Outperforms standard NSGA-III on benchmark FJSP problems.

Effective use of parameter tuning and local search strategies.

Abstract

A customized multi-objective evolutionary algorithm (MOEA) is proposed for the multi-objective flexible job shop scheduling problem (FJSP). It uses smart initialization approaches to enrich the first generated population, and proposes various crossover operators to create a better diversity of offspring. Especially, the MIP-EGO configurator, which can tune algorithm parameters, is adopted to automatically tune operator probabilities. Furthermore, different local search strategies are employed to explore the neighborhood for better solutions. In general, the algorithm enhancement strategy can be integrated with any standard EMO algorithm. In this paper, it has been combined with NSGA-III to solve benchmark multi-objective FJSPs, whereas an off-the-shelf implementation of NSGA-III is not capable of solving the FJSP. The experimental results show excellent performance with less computing budget.