Hybrid particle swarm algorithm for products' scheduling problem in cellular manufacturing system

Abstract

Industries have to produce high quality products with low cost because of the competitive environment and customer demand. To cope with the increasing demand of customized products at low cost, the concept of cellular manufacturing systems (CMS) has been introduced under the umbrella of lean manufacturing. Industries are facing three major problems in CMS; selection of product families, cell formation and products scheduling. This paper deals with the products scheduling problem in CMS. As it is a non-deterministic polynomial-time (NP) hard problem, a hybrid particle swarm optimization algorithm with Nawaz, Enscore, Ham-NEH (NEPSO) embedded with local search is proposed to find optimized sequence results for two conflicting performance measures (work in process and machine cell utilization). Here, particle swarm optimization (PSO) is integrated with an NEH algorithm to quickly achieve better optimal sequence. For this purpose, the solution obtained from the NEH algorithm is used as a seed for PSO optimization. A mathematical model is presented for conflicting performance measures; minimization of work in process (WIP) and maximization of average machine cell utilization. A case study of automotive manufacturing cells was conducted. Results of the NEPSO were compared with the existing method, standard PSO, genetic algorithm (GA), and NEH algorithm, showing that the NEPSO performed better in term of problem optimization of the cellular layouts.