Optimizing assembly sequence time using particle swarm optimization (PSO)

Abstract

Assembly sequence planning (ASP) plays an important role in the production planning and should be optimized to minimize production time and cost when large numbers of parts and sub-assemblies are involved in the assembly process. Although the ASP problem has been tackled via a variety of optimization techniques, these techniques are often inefficient when applied to larger-scale problems. In this study, an approach using particle swarm optimization (PSO) is proposed to tackle one of the ASP problems which are optimizing the assembly sequence time. PSO uses a number of agents (particles) that constitute a swarm moving around in the search space looking for the best solution. Each bird, called particle, learns from its own best position and the globally best position. Experimental results show that PSO algorithm can produce good results in optimizing the assembly time, has a powerful global searching ability and fast rate of convergence.