Multi-objective Evolutionary Algorithm with Strong Convergence of Multi-area for Assembly Line Balancing Problem with Worker Capability

Multiobjective assembly line balancing with worker capability (moALB-wc) is a realistic and important issue from classical assembly line balancing (ALB) problem involving conflicting criteria such as the cycle time, the total worker cost, and/or the variation of workload. This paper proposes a multiobjective evolutionary algorithm (MOEA) with strong convergence of multi- area (MOEA-SCM) to deal with moALB-wc problem considering minimization of the cycle time and total worker cost, given a fixed number of station limit. It adopts special fitness function strategy considering dominating and dominated relationship among individuals and hybrid selection mechanism so as to the individuals could converging toward the multiple areas of Pareto front. Such ability to strong convergence of multi-area could preserve both the convergence and even distribution performance of proposed algorithm. Numerical comparisons with various problem instances show that MOEA-SCM could get the better convergence distribution performance than existing MOEAs