A customized multi-objective evolutionary algorithm (MOEA) is proposed for the flexible job shop scheduling problem (FJSP) with three objectives: makespan, total workload, critical workload. In... Show moreA customized multi-objective evolutionary algorithm (MOEA) is proposed for the flexible job shop scheduling problem (FJSP) with three objectives: makespan, total workload, critical workload. In general, the algorithm can be integrated with any standard MOEA. In this paper, it has been combined with NSGA-III to solve the state-of-the-art benchmark FJSPs, whereas an off-the-shelf implementation of NSGA-III is not capable of solving them. Most importantly, we use the various algorithm adaptations to enhance the performance of our algorithm. To be specific, it uses smart initialization approaches to enrich the first-generation population, and proposes new crossover operator to create a better diversity on the Pareto front approximation. The MIP-EGO configurator is adopted to automatically tune the mutation probabilities, which are important hyper-parameters of the algorithm. Furthermore, different local search strategies are employed to explore the neighborhood for better solutions. The experimental results from the combination of these techniques show the good performance as compared to classical evolutionary scheduling algorithms and it requires less computing budget. Even some previously unknown non-dominated solutions for the BRdata benchmark problems could be discovered. Show less
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... Show moreThis 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. Show less
