Designing a manufacturing cell system by assigning workforce

Abstract

Purpose: In this paper, we have proposed a new model for designing a Cellular Manufacturing System (CMS) for minimizing the costs regarding a limited number of cells to be formed by assigning workforce.

Design/methodology/approach: Pursuing mathematical approach and because the problem is NP-Hard, two meta-heuristic methods of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms have been used. A small randomly generated test problem with real-world dimensions has been solved using simulated annealing and particle swarm algorithms.

Findings: The quality of the two algorithms has been compared. The results showed that PSO algorithm provides more satisfactory solutions than SA algorithm in designing a CMS under uncertainty demands regarding the workforce allocation.

Originality/value: In the most of the previous research, cell production has been considered under certainty production or demand conditions, while in practice production and demand are in a dynamic situations and in the real settings, cell production problems require variables and active constraints for each different time periods to achieve better design, so modeling such a problem in dynamic structure leads to more complexity while getting more applicability. The contribution of this paper is providing a new model by considering dynamic production times and uncertainty demands in designing cells.