研究成果：A multi-objective memetic algorithm for energy-efficient flow shop group scheduling problem with a dynamic speed-scaling mechanism

作者：袁帅鹏；王柏琳[T]；Wang Sihan；Pei Yihan；李铁克

发表期刊：Annals of Operations Research

期刊级别：ABS3

发表时间：20260413

摘要：This study investigates an energy-efficient flow shop group scheduling problem that incorpo-rates a dynamic speed-scaling mechanism, where both the sequence-dependent setup timesand round-trip transportation times are considered. This problem involves three deeply cou-pled sub-problems: the sequence of groups, the sequence of jobs within each group, andthe processing speed of each job on each machine. To the best of our knowledge, this prob-lem has not been previously explored. A mixed integer linear programming model is firstformulated to simultaneously minimize makespan and total energy consumption. To solvelarge-scale instances, we propose a knowledge-driven multi-objective memetic algorithm(MMA) tailored to the problem’s characteristics. The MMA incorporates a knowledge-basedinitialization heuristic and a speed-down strategy that reduces energy consumption withoutincreasing the makespan. Adaptive global optimization operators are designed for hierarchi-cal exploration. Moreover, a two-stage local search strategy, combining a co-evolutionarymechanism with a speed-up heuristic for critical operations, isintroduced to enhance solutionquality. A diversity mechanism is also employed to prevent premature convergence. Exten-sive experiments confirm the effectiveness of the proposed MMA in generating a diverse andhigh-quality set of Pareto-optimal solutions.