Abstract

This paper studies a bi-objective distributed flexible job shop scheduling problem considering machine on–off decisions. A mathematical model is formulated to minimize the makespan and total energy consumption while distinguishing processing energy, idle energy, and on–off energy. To address the coupled effects among job-to-factory assignment, machine selection, operation sequencing, and machine on–off states, a domain-knowledge-driven memetic algorithm (DKMA) is proposed. The algorithm represents each schedule with a three-layer encoding scheme and integrates hybrid initialization, knowledge-driven neighborhood search, and energy-saving reconstruction to improve solution-set quality and the use of on–off-eligible idle intervals. The proposed model and algorithm are evaluated through Taguchi parameter tuning, small-scale mixed-integer linear programming (MILP) validation, component ablation experiments, and multi-algorithm comparisons. The results show that DKMA improves solution-set coverage, Pareto-front approximation, and energy control on the tested instances, which supports its applicability to distributed green scheduling with machine on–off decisions.

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