Multi-objective complex product assembly scheduling problem considering parallel team and worker skills

The flow shop scheduling problem (FSP) is a classic shop scheduling problem with a strong engineering background. As an extension of FSP, the hybrid flow shop scheduling problem (HFSP) involves parallel machine scheduling, and the worker assignment problem on assembly lines (WAPAL) involves worker allocation. Parallel machine scheduling and multi-skilled and multilevel worker allocation are both involved in the actual assembly lines of complex products, but few studies have investigated them simultaneously. This work studies a complex product assembly line scheduling problem considering multi-skilled worker assignment and parallel team scheduling and takes the maximum completion time and the imbalance degree of team workload as the optimization objective. An integer programming model is proposed, and a hybrid coding method considers the worker assignment and task order. Three improved strategies based on a multiple objective evolutionary algorithm (MOEA) are proposed in the local search. Finally, 20 test instances are generated based on actual enterprise labor data, and the results based on the three strategies are compared with six MOEAs. The results show that the three strategies are superior in terms of the quality and distribution of solutions. The inverted generational distance (IGD) index value is increased by 49.97%, 47.89%, 47.08% respectively and the hypervolume (HV) index value is increased by 39.76%, 38.19%, 38.15% respectively.