{"title":"Discrete Breeding Swarm for Cost Minimization of Parallel Job Shop Scheduling Problem","authors":"Tarek Aboueldah, Hanan Farag","volume":176,"journal":"International Journal of Mathematical and Computational Sciences","pagesStart":98,"pagesEnd":103,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10012188","abstract":"<p>Parallel Job Shop Scheduling Problem (JSSP) is a multi-objective and multi constrains NP-optimization problem. Traditional Artificial Intelligence techniques have been widely used; however, they could be trapped into the local minimum without reaching the optimum solution. Thus, we propose a hybrid Artificial Intelligence (AI) model with Discrete Breeding Swarm (DBS) added to traditional AI to avoid this trapping. This model is applied in the cost minimization of the Car Sequencing and Operator Allocation (CSOA) problem. The practical experiment shows that our model outperforms other techniques in cost minimization.<\/p>","references":"[1]\tKhaled Mesghouni, Slim Hammadi \"Evolutionary algorithms for job-shop scheduling\" International Journal of Applied Mathematics and Computer Science, Vol. 14, no.1. pp. 91-103 January 2004.\r\n[2]\tEtiler, O., Toklu, B., Atak, M., Wilson, J. \u201cA genetic algorithm for flow shop scheduling problems.\u201d J.of the Ope. Res. Soc., vol. 55, pp. 830\u2013835, 2004\r\n[3]\tR. Kolisch and S. Hartmann, \u201cExperimental investigation of heuristics for resource-constrained project scheduling: an update\u201d, European Journal of Operational Research, vol. 174, no. 1, pp. 23\u201337, 2006.\r\n[4]\tJ. H. Holland, \"Adaptation in Natural and Artificial Systems, \" The University of Michigan Press.Ann Arbor. 1975.\r\n[5]\tA. Lihu, and S. Holban,\" Top five most promising algorithms in scheduling\" 5th International Symposium on Applied Computational Intelligence. 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