Parallel U-shaped assembly line balancing with adaptive MOEA/D hybridized with BBO

The mixed-model line balancing problem of a parallel U-shaped assembly line (PUAL) is addressed in this paper. Four objectives (so-called many objectives) conflicting in nature were concurrently optimized to minimize the (i) task unrelatedness, (ii) different workloads between workstations, (iii) number of stations and (iv) number of workstations. The solution technique adopts the recently emerged and innovative concept of multi-objective evolutionary optimization based on decomposition (MOEA/D), which partitions the fundamental problem into several scalar optimization subproblems and then optimizes them simultaneously. To further strengthen the algorithm, the platform of the MOEA/D was hybridized with the biogeography-based optimization (BBO) algorithm and the adaptive mechanism was also embedded, namely AMOEA/D-BBO. The performances in Pareto sense of AMOEA/D-BBO were evaluated on various problem sizes in comparison with the MOEA/D-BBO, MOEA/D and BBO algorithms. The experimental results demonstrated that AMOEA/D-BBO outperforms the other algorithms in terms of the convergence rate and spread.