The flexible job shop scheduling problem (FJSP) has been extensively researched over the past decade. However, the integration of preventive maintenance (PM), transportation times, and energy efficiency remains under-explored. This paper addresses the energy-efficient flexible job shop scheduling problem with preventive maintenance and transportation times (EFJSP-PMT). To minimize both makespan and total energy consumption, a multi-population optimization algorithm with adaptive strategy and competition (MPOA-ASC) is proposed, featuring distinct early and later evolution phases. In the early evolution phase, we develop a hybrid heuristic initialization method, a new population division strategy, an adaptive mechanism for crossover and mutation probabilities, and three specialized local search operators. In the later phase, a novel inter-population competition mechanism is introduced to optimize the allocation of computational resources and enhance convergence performance. Extensive experimental results demonstrate that the proposed MPOA-ASC significantly outperforms state-of-the-art algorithms in solving the EFJSP-PMT.
Pan et al. (Mon,) studied this question.