A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling
This work addresses scheduling optimization for manufacturing or logistics, but it is incremental as it enhances an existing algorithm for a specific domain.
The authors tackled the multi-objective flexible job shop scheduling problem by proposing a customized multi-objective evolutionary algorithm based on NSGA-III, which achieved excellent performance with reduced computing budget.
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.