No abstract

Many algorithms for cell formation have been developed for past three decades in cellular manufacturing. Some use binary data for cell formation and others use production data such as operation sequence, processing times, production volumes, etc. for cell formation. All these algorithms assume that the conversion of job shop to cellular manufacturing is performed comprehensively. (In other words, they assume that all the cells are formed at a time.) However, this is far from reality. In practice, cell formation is done incrementally, one after the other, rather than comprehensively. None of the algorithms developed so far addresses the issue of incremental cell formation. In this paper, the incremental cell formation problem is de®ned and various categories of problems are mentioned. One type of those categories is selected for solving. Two methods, namely the branch and bound technique and a heuristic based on a multistage programming approach, have been applied to solve the chosen problem. Data sets have been generated to compare these two methods in terms of quality of solution and demand on computational time. It has been found that the branch and bound technique gives a superior quality solution, but is computationally more demanding, where as heuristic based on a multistage programming approach is computationally far superior.

Over the past three decades considerable amount of research work has been reported in the literature of Group Technology (GT). Most of the research work is concerned with formation of machine cells and part families. This is because cell formation is considered to be the most complex and the most important aspect of Cellular Manufacturing System (CMS). Due to NP completeness of cell formation problem, many heuristics have been developed. These heuristics are developed for both single as well as multiple objectives for the comprehensive cell formation. Here all part types and machine types are considered at a time for cell conversion and that all cells are designed at a single point in time. But planning and implementation of most cell conversions in industry are incremental ones, and not comprehensive. This issue has not been addressed in GT literature adequately. In this paper we consider multiple objectives for incremental cell formation and develop, a lexicographic based simulated annealing algorithm. The performance of the algorithm is tested over several data sets by taking different initial feasible solutions generated using different heuristics.