Development of cell formation algorithm and model for cellular manufacturing system

The Cellular Manufacturing System (CMS) is considered as an efficient production strategy for batch type production. The CMS relies on the principle of grouping machines into machine cells and grouping machine parts into part families that is named cell formation. Cells are formed based on presuming fixed single route and parts demand (traditional cell formation) or fluctuation of parts demand (dynamic cell formation). The majority of existing models are defined in traditional cell formation and a few of them have considered machine assignment, inter-cell travel and subcontracting excluding worker assignment and workload balancing cells based on operation sequences in dynamic environment. Therefore, This research work attempts to solve a developed comprehensive model which integrated cell formation and proce planning problem meanwhile taking into consideration important cell design issues. These issues consist of workload balancing among cells and operation issues such as machines assignment, inter/intra-cells material handling, workers assignment,subcontracting based on operational time, operation sequence of the parts and assessing effects of these parameters on cell design in dynamic environment. In addition, one of the main challenges has been development of efficient algorithm for solving aforementioned model to find exact feasible optimal solution. Previous methods have produced infeasible solution. It is consequence of the designers could not handle constraints satisfaction. Therefore, for this proposes good benchmarked algorithm, bacteria foraging algorithm is selected and developed to solve multiobjective cell formation model and traced constraints satisfaction handling to produce feasible optimal solution. The basic bacteria foraging has been successful in solving single objective non-matrix space NP-hard optimization problems. The performance of the proposed algorithm is compared with a number of key algorithms that reported in the corresponding scientific literature. For this purpose, performance measures such as number of exceptional elements courier of inter-cell material handling, number of voids courier of intra-cell material handling and machine utilization, total cells load variation, operation costs, and maintaining solution diversity in Pareto frontier courier of improvement in domain exploring performance and drift-avoiding are used. The results show proposed algorithm approximately solved problems averagely 22% better in terms of find feasible optimal solutions depends of various performance measures in 72.2% of computational time than other previous considered key algorithms.

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