A Pareto-based hybrid iterated greedy algorithm for energy-efficient scheduling of distributed hybrid flowshop - Lu, Chao; Liu, Qiao; Zhang, Biao; Yin, Lvjiang (Expert systems with applications, 2022)
Due to its practicality, hybrid flowshop scheduling problem (HFSP) with productivity objective has been extensively explored. However, studies on HFSP considering green objective in distributed production environment are quite limited. Moreover, the current manufacturing mode is gradually evolving toward distributed co-production mode. Thus, this paper investigated a distributed hybrid flowshop scheduling problem (DHFSP) with objectives of minimization the makespan and total energy consumption (TEC). To address this problem, this paper designed a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG) by integrating the merits of genetic operator and iterated greedy heuristic. In this MOHIG, firstly, one cooperative initialization strategy is proposed to boost initial solutions’ quality based on the previous experience and rules. Secondly, one knowledge-based multi-objective local search method is invented to enhance the exploitation capability according to characteristics of problem. Thirdly, an energy-saving technique is developed to decrease the idle energy consumption of machine tools. Furthermore, the effectiveness of each improvement component of MOHIG is assessed by three common indicators. Finally, the proposed MOHIG algorithm is compared with other multi-objective optimization algorithms, including SPEA2, MOEA/D, and NSGAII. Experimental results indicate that the proposed MOHIG outperforms its compared algorithms in solving this problem. In addition, this research can better guide practical production in some certain environments. •Considering green scheduling in distributed hybrid flowshop environment.•Designing a new energy-saving strategy into this problem.•Proposing a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG).•Evaluating performance of the proposed MOHIG by conducting comparison experiments.