A two-stage cooperative evolutionary algorithm for energy-efficient distributed group blocking flow shop with setup carryover in precast systems

As the main link of the rapidly developing prefabricated construction industry, the production of precast components (PCs) has become a research hotspot. Therefore, the distributed group flow shop scheduling problem with blocking and carryover sequence-dependent setup time constraints (DPGFSP-BCT) in precast systems is considered. To address this problem, first, a mixed integer linear model is presented. Second, a two-stage cooperative coevolutionary algorithm (TS-CCEA) is proposed to minimize both the makespan and total energy consumption (TEC). In TS-CCEA, two acceleration rules are designed to reduce computational efforts. Third, to diversify the population, different initialization methods are established for different populations. Based on the problem-specific knowledge of solution classification, the individuals of the group population and job population execute two neighborhood search algorithms. Subsequently, considering the TEC, a critical path based speed mutation strategy is proposed to further improve the exploitation ability. Furthermore, a reinitialization heuristic is developed to avoid premature convergence. Last, the performance of the TS-CCEA is verified after calibrating the parameters. The experimental results demonstrate the stability and effectiveness of the algorithm.