No abstract

In this paper, a novel bi-objectivemathematical model is proposed to designa fourdimensional (i.e., part, machine, operator, and tool) cellular manufacturing system (CMS) in a dynamic environment. The main objectives of this model are to 1) minimize total costs including tools processing cost, costs of transporting cells between various cells, machine setup cost, and operators' educational costs, and 2) maximizing skill level of operators. The developed model is strictly NP-hard and exact algorithms cannot find globally optimal solutions in reasonably computational time. So, a multi-objective vibration damping optimization algorithm (MOVDO) with a new solution structure that satisfies all the constraints and generates feasible solutions is proposed to find near-optimal solutions in reasonablycomputational time. Since there is no benchmark available in the literature, three other meta-heuristic algorithms (i.e., non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO) and multi-objective invasive weeds optimization (MOIWO)) with the similar solution structure are developed to validate the performance of the proposedMOVDO algorithm for solving various instances of the developed model. The result of comparing theirperformances based on statistical tests and different measuring metrics reveals that the proposed MOVDO algorithm outperforms remarkably better than other meta-heuristics used in this paper.