An improved MOEA/D for multi-objective flexible job shop scheduling with release time uncertainties

Abstract

To capture the multi-objective and uncertain nature of flexible job shop scheduling, a mathematical model for the multi-objective flexible job shop scheduling problem with release time uncertainties (mOFJSSP-RTU) is constructed, where three objectives of make-span, tardiness, and robustness are taken into account simultaneously under various constraints. To solve MOFJSSP-RTU appropriately, an improved multi-objective evolutionary algorithm based on decomposition (IMOEA/D) is proposed for robust scheduling. The novelty of our algorithm is that it employs a new subproblem update strategy which utilizes the global information, allows the elitists recorded in an archive to take part in the child generation, and incorporates a repair-based crossover operator and an adaptive differential evolution (DE)-based mutation operator for variation, which helps better balance the exploration and exploitation of the algorithm. Experimental results on 4 problem instances indicate that our IMOEA/D-based robust scheduling method has a much better convergence performance than the state-of-the-art multi-objective optimization evolutionary algorithms (MOEAs), and it is also good at maintaining a uniform distribution of solutions. Different trade-offs among the three objectives are also analyzed.