A self-learning classification-based multi-objective evolutionary algorithm for machine multi-state energy-efficient flexible job shop scheduling under time-of-use pricing

Driven by the “dual carbon” strategic goals, the coordinated optimization of energy consumption and production efficiency has become a core issue for manufacturing industries. As an important means to promote energy structure transformation, electric substitution has made significant progress in industrial manufacturing, transportation, household electrification, and other fields. Among them, industrial production accounts for over 60% of the total electric energy substitution, becoming the largest electricity consumer. Note that the electricity price is based on time-of-use pricing (TOU), meanwhile, electric consumption is related to the machine multi-state (MM). Regarding these matters, this study focuses on determining sensible machine states and formulating reasonable production scheduling plan, to minimize both production time and power consumption. First, a novel energy-efficient flexible job shop scheduling problem is developed, which considers both the TOU strategy and the MM conditions (EFJSP-MM-TOU). Second, a self-learning classification-based multi-objective evolutionary algorithm (SCMOEA) is proposed to solve the EFJSP-MM-TOU. In specific, the SCMOEA enhances population diversity through a hybrid initialization strategy, adopts a dynamic selection of cross individuals based on the self-learning classification mechanism to improve the search efficiency, and designs four local search operators to increase the potential for approaching better positions. Third, by employing the MK standard dataset in EFJSP-MM-TOU, the proposed SCMOEA is compared with its three variants and five state-of-the-art algorithms to verify its optimization performance. The experimental results suggest that SCMOEA has advantages in terms of Pareto optimal solutions’ diversity and convergence. Finally, by testing in an actual enterprise case, the results further support the effectiveness of the EFJSP-MM-TOU and the significance of SCMOEA.