Abstract
This chapter discusses some optimization issues from a business perspective in the context of the supply chain operations. We note that the term “global optimization” may have different meanings in different communities and we look at it from the business and classical optimization points of view. We present two real-world optimization problems which differ in scope and use them for our discussion on global optimization issues. The differences between these two problems, experimental results, the main challenges they present and the algorithms used are discussed. Here, we claim neither uniqueness nor superiority of the algorithms used, rather the main goal of this chapter is to emphasize the importance of the global optimization concept.
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References
Altiparmak, F., Gen, M., Lin, L., Paksoy, T.: A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers & Industrial Engineering 51(1), 196–215 (2006); Special Issue on Computational Intelligence and Information Technology: Applications to Industrial Engineering
Cheng, R., Gen, M., Tsujimura, Y.: A tutorial survey of job-shop scheduling problems using genetic algorithms—i: representation. Comput. Ind. Eng. 30(4), 983–997 (1996)
Coit, D.W., Smith, A.E.: Solving the redundancy allocation problem using a combined neural network/genetic algorithm approach. Computers & Operations Research 23(6), 515–526 (1996)
Davis, L.: Job shop scheduling with genetic algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 136–140. Lawrence Erlbaum, Hillsdale (1985)
Lee, C.Y., Choi, J.Y.: A genetic algorithm for job sequencing problems with distinct due dates and general early-tardy penalty weights. Computers & Operations Research 22(8), 857–869 (1995)
Lee, H., Pinto, J.M., Grossmann, I.E., Park, S.: Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management. Industrial & Engineering Chemistry Research 35(5), 1630–1641 (1996)
Levine, J., Ducatelle, F.: Ant colony optimization and local search for bin packing and cutting stock problems. The Journal of the Operational Research Society 55(7), 705–716 (2004)
Liang, K.-H., Yao, X., Newton, C., Hoffman, D.: A new evolutionary approach to cutting stock problems with and without contiguity. Computers & Operations Research 29(12), 1641–1659 (2002)
Supply & Demand Chain Executive Magazine. Embracing complexity. Toward a 21st century supply chain solution (2008), http://sdcexec.com/online/printer.jsp?id=9012
Martin, C.H., Dent, D.C., Eckhart, J.C.: Integrated production, distribution, and inventory planning at libbey-owens-ford. Interfaces 23(3), 68–78 (1993)
Michalewicz, Z.: Genetic algorithms + data structures = evolution programs, 3rd edn. Springer-Verlag New York, Inc, New York (1996)
Michalewicz, Z., Schoenauer, M.: Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation 4, 1–32 (1996)
Naso, D., Surico, M., Turchiano, B., Kaymak, U.: Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete. European Journal of Operational Research 177(3), 2069–2099 (2007)
Van Laarhoven, P.J.M.: Job shop scheduling by simulated annealing. Operations research 40, 113 (1992)
Vergara, F.E., Khouja, M., Michalewicz, Z.: An evolutionary algorithm for optimizing material flow in supply chains. Comput. Ind. Eng. 43(3), 407–421 (2002)
Zhou, G., Min, H., Gen, M.: A genetic algorithm approach to the bi-criteria allocation of customers to warehouses. International Journal of Production Economics 86(1), 35–45 (2003)
Zielinski, K., Weitkemper, P., Laur, R., Kammeyer, K.-D.: Parameter study for differential evolution using a power allocation problem including interference cancellation. In: IEEE Congress on Evolutionary Computation, 2006. CEC 2006, pp. 1857–1864 (2006)
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Ibrahimov, M., Mohais, A., Michalewicz, Z. (2009). Global Optimization in Supply Chain Operations. In: Chiong, R., Dhakal, S. (eds) Natural Intelligence for Scheduling, Planning and Packing Problems. Studies in Computational Intelligence, vol 250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04039-9_1
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DOI: https://doi.org/10.1007/978-3-642-04039-9_1
Publisher Name: Springer, Berlin, Heidelberg
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