An iterated greedy algorithm based on population evolution for distributed blocking flowshop scheduling with balanced energy costs criterion

Based on the classical distributed flowshop scheduling problem, this paper constructs the mixed linear integer programming mode (MILP) of distributed blocking flowshop scheduling problem with sequence-dependent setup time (DBFSP SDST), and the optimization objective is to balance the energy consumption cost of each factory. To tackle this problem, an iterated greedy algorithm based on the population evolution (PEIG) is proposed. In PEIG, firstly, a problem-specific heuristic is well designed based on the blocking constraint and multiple factories model. Secondly, for the advantages and disadvantages of the traditional IG algorithm, the local search strategies based on the population operation, the multiple neighborhood search structures, and the cross-factory destruction-reconstruction strategy are proposed to further balance the global exploration and exploitation abilities of the proposed algorithm. The 270 test instances numerical simulations and statistical comparison with four representative algorithms show that the proposed algorithm has superior performance and can provide a better scheduling scheme for medium and large-scale DBFSP SDST than the compared algorithms. © 2024 South China University of Technology. All rights reserved.