Improving NSGA-III for flexible job shop scheduling using automatic configuration, smart initialization and local search

Genetic and Evolutionary Computation Conference Companion (GECCO Companion), pp. 181–182

Abstract

This paper provides a short summary of a novel algorithm tailored towards multi-objective flexible job shop scheduling problems (FJSP). The result shows that for challenging real-world problems in combinatorial optimization, off-the-shelf implementations of multi-objective optimization evolutionary algorithms (MOEAs) might not work, but by using various adaptations, these methods can be tailored to provide excellent results. This is demonstrated for a state of the art MOEA, that is NSGA-III, and the following adaptations: (1) initialization approaches to enrich the first-generation population, (2) various crossover operators to create a better diversity of offspring, (3) parameter tuning, to determine the optimal mutation probabilities, using the MIP-EGO configurator, (4) local search strategies to explore the neighborhood for better solutions. Using these measures, NSGA-III has been enabled to solve benchmark multi-objective FJSPs and experimental results show excellent performance.

Authors 4

1. Yali Wang Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, The Netherlands

2. Bas van Stein Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, The Netherlands

3. Thomas Bäck Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, The Netherlands

4. Michael Emmerich Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, The Netherlands