Abstract
This paper presents a two-stage approach consisting of a real-coded genetic algorithm and goal programming to obtain improved cell formation. In the first stage, the minimum value of each objective is determined using a single-objective genetic algorithm. In the second stage, goal programming is incorporated and the final objective is constructed as the minimization of sum of deviational variables of corresponding objectives. The proposed technique is implemented as a software toolkit using C Sharp.net programming language. Modified grouping efficiency is used as the performance measure to test the efficiency of the proposed technique. Five problems with different sizes have been considered from the literature to show the potentials of the proposed technique.
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Chaudhuri, B., Jana, R.K., Dan, P.K. (2017). A Hybrid Genetic Algorithm for Cell Formation Problems Using Operational Time. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_13
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DOI: https://doi.org/10.1007/978-981-10-3153-3_13
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