Hybrid Multi-Objective Variable-Scale Iterated Greedy Algorithm for Energy-Efficient Distributed Blocked Flowshop Scheduling with Heterogeneous Factories

Energy conservation and carbon reduction are essential for achieving carbon peaking and neutrality, as well as advancing the green transformation of the economy and society. This research examines the energy-efficient distributed blocked flowshop scheduling problem with heterogeneous factories (EEDBFSP-HF), which aims to simultaneously minimize makespan and total energy consumption (TEC). To tackle the issue, a hybrid multi-objective variable-scale iterated greedy algorithm (HMOVIG) is developed. The algorithm incorporates several customized strategies to enhance solution quality and optimization efficiency. An elite selection strategy is employed to reserve high-quality individuals from the initial population. To promote population diversity and prevent premature convergence, a factory-aware crossover operator and a dynamic destruction intensity strategy are designed. Additionally, a problem-specific energy-saving heuristic is applied to lower TEC. A speedup-based deep local search is further integrated to refine solutions. Numerical experiments validate that HMOVIG consistently surpasses state-of-the-art algorithms in terms of scheduling effectiveness and solution quality.