Optimization of two-sided assembly line balancing with resource constraints using modified particle swarm optimisation

Abstract. Two-Sided Assembly Line Balancing (2S-ALB) is essential to the production of large-sized high-volume products, including automotive production, at assembly plants. The 2S-ALB problem involves different assembly resources such as worker skills, tools, and machines required for the assembly. This research modeled and optimized the 2S-ALB with resource constraints. In the end, besides favorable workload balance, the number of resources can be optimized. For optimization purpose, particle swarm optimization was modied to reduce dependence on a single best solution. This was conducted by replacing the best solution with the top three solutions in the reproduction process. Computational experiment results using 12 benchmark test problems indicated that the 2S-ALB with a resource-constrained model was able to reduce the number of resources for use in an assembly line. Furthermore, the proposed Modied Particle Swarm Optimization (MPSO) was capable of searching for minimum solutions to 11 out of 12 test problems. The good performance of MPSO was attributed to its ability to maintain particle diversity over iterations. The proposed 2S-ALB model and MPSO algorithm were validated later using an industrial case study. This research makes a two-fold contribution: (a) Proposition of a novel 2S-ALB with resource-constrained model and (b) a modied PSO algorithm with enhanced performance.