Multi-objective sequencing problems of mixed-model assembly systems using memetic algorithms

This paper investigates the performance of local searches embedded in memetic algorithms for solving multi-objective mixed-model assembly line sequencing problems that are common in a just-in-time production system. Two inversely related objectives, namely, setup times and production rate variation, arc simultaneously considered. We use memetic algorithms which are a type of evolutionary algorithm using a local search algorithm to exercise exploitation. Simulation results demonstrate that memetic algorithms employed in conjunction with an appropriate local search outperform highly meta-heuristic algorithms such as Strength Pareto Evolutionary Algorithm 2 and Non-dominated Sorting Genetic Algorithm II in terms of ability to find Pareto-optimal Solutions.