Abstract

In closed job shop, in which a fixed number of products are produced on a repetitive basis, when there are significant sequence dependent setup times and costs involved, cell formation (CF) problem should consider minimizing the sequence-dependent setup times in order to minimize the production cost. Setup time reduction in CMS has gained little to modest attention in the literature. This could be attributed to the fact that the fundamental problem in cell formation in CMS has been mainly about material handling and machine utilization while setup time was presumed to normally decrease as a result of grouping similar parts in a manufacturing cell. Despite more than three decades of history of CMS’s it has been relatively recent that setup time has been included in cell formation problems and found a place in the existing models. Sequence-dependent setup time in the literature has been dealt with mostly for scheduling part-families in a single manufacturing cell or in allocation of parts to cells in a pure flow shop. In this thesis, the issue of setup time has been extended to the members of a part family and to its lowest level which is operation-level and incorporated in general cell formation problem in a dynamic CMS. In this thesis we have developed a multi-period integer programming CF model to address the reduction of the sequence-dependent setup time as well as considering the dynamic nature of today’s manufacturing environment in CMS, where the product mix demanded would change in different time periods. Due to time complexity of the problem, a two stage solution approach has been adopted. First a GA-based heuristic was developed that provides near optimal solutions for single-period problems of the global model. The performance of the GA-based heuristic was successfully evaluated versus optimization software. Second, a dynamic programming (DP)-based heuristic was developed that reintegrates the single-period solutions into a multi-period solution. The performance of the DP-based heuristic was also evaluated against optimization software