Abstract
Solving multi-objective scientific and engineering problems is, generally, a very difficult goal. In these optimization problems, the objectives often conflict across a high-dimensional problem space and require extensive computational resources. In this paper, a migration model of parallelization is developed for a genetic algorithm (GA) based multi-objective evolutionary algorithm (MOEA). The MOEA generates a near-optimal schedule by simultaneously achieving two contradicting objectives of a flexible manufacturing system (FMS). The parallel implementation of the migration model showed a speedup in computation time and needed less objective function evaluations when compared to a single-population algorithm. So, even for a single-processor computer, implementing the parallel algorithm in a serial manner (pseudo-parallel) delivers better results. Two versions of the migration model are constructed and the performance of two parallel GAs is compared for their effectiveness in bringing genetic diversity and minimizing the total number of functional evaluations.
Similar content being viewed by others
References
Fonseca CM, Fleming PJ (1995) An overview of evolutionary algorithms in multiobjective optimization. Evol Comput 3(1):1–16
Bensonson HP, Sayin S (1997) Towards finding global representations of the efficient set in multiple objective mathematical programming. Nav Res Log 44:47–67
Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2:221–248
Fonseca CM, Fleming PJ (1993) Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Forrest S (ed) Proceedings of the 5th International Conference on Genetic Algorithms (ICGA’93), Urbana, Illinois, July 1993. Morgan Kaufmann, San Francisco, California, pp 416–423
Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the 1st IEEE International Conference on Evolutionary Computation, Orlando, Florida, June 1994. IEEE Press, Piscataway, New Jersey, pp 82–87
Van Veldhuzien DA (1999) Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. PhD thesis, Air Force Institute of Technology, Wright Patterson AFB, Ohio, AFIT/DS/ENG/99-01
Abramson D, Mills G, Perkins S (1993) Parallelization of a genetic algorithm for the computation of efficient train schedules. In: Proceedings of the 1993 Parallel Computing and Transputers Conference, Brisbane, Australia, November 1993, pp 139–149
Cantu-Paz E (1998) A survey of parallel genetic algorithms. Calculateurs Paralleles. Reseaux et Systems Repartis 10(2):141–171
Cantu-Paz E, Goldberg DE (1997) Predicting speedups of idealized bounding cases of parallel genetic algorithms. In: Back T (ed) Proceedings of the 7th International Conference on Genetic Algorithms (ICGA’97), East Lansing, Michigan, July 1997. Morgan Kaufmann, San Francisco, California, pp 113–121
Fogarty TC, Huang R (1991) Implementing the genetic algorithm on transputer based parallel processing systems. In: Proceedings of the 1st Workshop on Parallel Problem Solving from Nature, Dortmund, Germany, October 1991, pp 145–149
Shanker K, Agrawal AK (1991) Loading problems and resource considerations in FMS: a review. Int J Prod Econ 25:111–119
Chan TS, Pak HA (1986) Heuristical job allocations in a flexible manufacturing system. Int J Adv Manuf Tech 1(2):69–90
Bagchi Tapan P (2001) Pareto-optimal solutions for multi-objective production scheduling problems. In: Proceedings of the 1st International Conference on Evolutionary Multi-Criterion Optimization (EMO 2001), Zurich, Switzerland, March 2001. Lecture Notes in Computer Science, vol 1993, Springer, Berlin Heidelberg New York, pp 458–471
Nagar A, Haddok J, Heragu S (1995) Multiple and bicriteria scheduling: a literature survey. Eur J Oper Res 81:88–104
Murthy Ch VR, Srinivasan G (1995) Fractional cell formation in group technology. Int J Prod Res 33(5):1323–1337
Baker JE (1987) Reducing bias and inefficiency in the selection algorithm. In: Proceedings of the 2nd International Conference on Genetic Algorithms (ICGA’87), Cambridge, Massachusetts, July 1987. Lawrence Erlbaum, Hillsdale, New Jersey, pp 14–21
Munetomo M, Takai Y, Sato Y (1993) An efficient migration scheme for subpopulation-based asynchronously parallel genetic algorithms. In: Forrest S (ed) Proceedings of the 5th International Conference on Genetic Algorithms (ICGA’93), Urbana, Illinois, July 1993. Morgan Kaufmann, San Francisco, California, p 649
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Saravana Sankar, S., Ponnambalam, S.G. & Gurumarimuthu, M. Scheduling flexible manufacturing systems using parallelization of multi-objective evolutionary algorithms. Int J Adv Manuf Technol 30, 279–285 (2006). https://doi.org/10.1007/s00170-005-0045-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-005-0045-6