Abstract
We address the optimal design of a Distribution Network (DN), presenting a procedure employing Multi-Objective Genetic Algorithms (MOGA) to select the (sub) optimal DN configuration. Using multi-objective genetic optimization allows solving a nonlinear design problem with piecewise constant contributions in addition to linear ones. The MOGA application allows finding a Pareto frontier of (sub) optimal solutions, which is compared with the frontier obtained solving the same problem with linear programming, where piecewise constant contributions are linearly approximated. The two curves represent, respectively, the upper and the lower limit of the region including the real Pareto curve. Both the genetic optimization model and the linear programming are applied under structural constraints to a case study describing the DN of an Italian enterprise.
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References
Bevilacqua, V., Mastronardi, G., Menolascina, F., Pedone, A., Pannarale, P.: A Novel Multi-Objective Genetic Algorithm Approach to Artificial Neural Network Topology Optimisation: The Breast Cancer Classification Problem. In: 2006 International Joint Conference on Neural Networks, Vancouver, BC (Canada), July 16-21 (2006)
Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W.: A Model for the Optimal Design of the Hospital Drug Distribution Chain. In: Proc. IEEE Workshop on Health Care Management, Venice, Italy, February 18-20 (2010)
Dotoli, M., Fanti, M.P., Meloni, C., Zhou, M.C.: Design and Optimization of Integrated E-supply Chain for Agile and Environmentally Conscious Manufacturing. IEEE Trans. on Systems Man and Cybernetics, Part A 36(1), 62–75 (2006)
Menolascina, F., Bevilacqua, V., Ciminelli, C., Armenise, M.N., Mastronardi, G.: A Multi-objective Genetic Algorithm Based Approach to the Optimization of Oligonucleotide Microarray Production Process. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS (LNAI), vol. 5227, pp. 1039–1046. Springer, Heidelberg (2008)
Michalewicz, Z., Dasgupta, D., Le Riche, R.G., Schoenauer, M.: Evolutionary Algorithms for Constrained Engineering Problems. Int. J. of Computers and Industrial Engineering (1996)
Zanjirani Farahani, R., Elahipanaha, M.: A Genetic Algorithm to Optimize the Total Cost and Service Level for Just-in-time Distribution in a Supply Chain. Int. J. Prod. Econ. 111, 229–243 (2008)
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Bevilacqua, V. et al. (2012). A Multi-objective Genetic Optimization Technique for the Strategic Design of Distribution Networks. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_32
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DOI: https://doi.org/10.1007/978-3-642-25944-9_32
Publisher Name: Springer, Berlin, Heidelberg
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