Adaptive reference-points learning and cooperation driven multi-objective algorithm for hybrid group flow shop with outsourcing option

With the development of economic globalization, group scheduling with outsourcing option has attracted much attention. This study considers a hybrid flow shop with group and outsourcing constraints, named HFGSP_OO. To solve this problem, adaptive reference-points learning and cooperation driven multi-objective algorithm (ARPCMOA) is proposed to optimize makespan, total energy consumption (TEC) and outsourcing cost, simultaneously. First, according to the characteristics of the problem, a strategy for determining the group to be outsourced is considered to generate the promising initial solutions. Second, a two-stage co-evolutionary method is used to explore the solution space in depth. In the first stage, a hybrid local search (HLS) is proposed to obtain more extreme solutions. In the second stage, the reference points adaptation mechanism is employed to enhance the global search capability of the algorithm, which can select high-quality solutions. These two stages are working cooperatively during the iterative process so that the population evolves towards the true Pareto front. In addition, an energy saving strategy based on idle time is proposed to better optimize TEC. Finally, a large number of statistical analysis experiments (KW) show that ARPCMOA outperforms existing multi-objective algorithms.