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Abstract
Instead of using expensive multiprocessor supercomputers, parallel computing can be implemented on a cluster of inexpensive personal computers. Commercial accesses to high performance parallel computing are also available on the pay-per-use basis. However, literature on the use of parallel computing in production research is limited. In this paper, we present a dynamic cell formation problem in manufacturing systems solved by a parallel genetic algorithm approach. This method improves our previous work on the use of sequential genetic algorithm (GA). Six parallel GAs for the dynamic cell formation problem were developed and tested. The parallel GAs are all based on the island model using migration of individuals but are different in their connection topologies. The performance of the parallel GA approach was evaluated against a sequential GA as well as the off-shelf optimization software. The results are very encouraging. The considered dynamic manufacturing cell formation problem incorporates several design factors. They include dynamic cell configuration, alternative routings, sequence of operations, multiple units of identical machines, machine capacity, workload balancing, production cost and other practical constraints.
Acknowledgements
This research is supported by Discovery Grant from NSERC of Canada and by Faculty Research Support Fund from the Faculty of Engineering and Computer Science, Concordia University, Montreal, Quebec, Canada. We also thank RQCHP (Reseau quebecois en calcul de haute performance—http://www.rqchp.qc.ca/) for its assistance in providing access to parallel computing facilities. We very much appreciate the valuable comments and suggestions from the anonymous referees on an early version of this paper.