Abstract
This paper presents a multicriterion algorithm for dealing with joint facility location and network design problems, formulated as bi-objective problems. The algorithm is composed of two modules: a multiobjective quasi-Newton algorithm, that is used to find the location of the facilities; and a multiobjective genetic algorithm, which is responsible for finding the efficient topologies. These modules are executed in an iterative way, to make the estimation of whole Pareto set possible. The algorithm has been applied to the expansion of a real energy distribution system. The minimization of financial cost and the maximization of reliability have been considered as the design objectives in this case.
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References
ReVelle, C.S., Eiselt, H.A.: Location analysis: A synthesis and survey. European Journal of Operational Research 165(1), 1–19 (2005)
Carrano, E.G., Soares, L.A.E., Takahashi, R.H.C., Saldanha, R.R., Neto, O.M.: Electric distribution multiobjective network design usign a problem-specific genetic algorithm. IEEE Transactions on Power Delivery 21(2), 995–1005 (2006)
Carrano, E.G., Takahashi, R.H.C., Cardoso, E.P., Neto, O.M., Saldanha, R.R.: Optimal substation location and energy distribution network design using an hybrid GA-BFGS algorithm. IEE Proc. on Generation, Transmission and Distribution 152(6), 919–926 (2005)
Chankong, V., Haimes, Y.Y.: Multiobjective decision making: Theory and methodology. North-Holland, Amsterdam (1983)
Bazaraa, M.S., Sherali, H.D., Shetty, C.M.: Nonlinear programming: Theory and algorithms, 2nd edn. John Wiley & Sons, Chichester (1993)
Ehrgott, M.: Multicriteria optimization. Springer, Heidelberg (2000)
Bernal-Agustín, J.L.: Aplicación de algoritmos genéticos al diseño óptimo de sistemas de distribución de energía eléctrica. Ph.D. dissertation, Zaragoza University, Zaragoza, Spain (1998)
Ramírez-Rosado, I., Bernal-Agustín, J.: Genetic algorithms applied to the design of large power distribution systems. IEEE Transactions on Power Systems 13(3), 696–702 (1998)
Carvalho, P., Ferreira, L., Lobo, F., Barruncho, L.: Optimal distribution network expansion planning under uncertainly by evolutionary decision convergence. Electrical Power and Energy Systems 20, 125–129 (1998)
Duan, G., Yu, Y.: Problem-specific genetic algorithm for power transmission system planning. Electric Power Systems Research 61, 41–50 (2002)
Miranda, V., Ranito, J.V., Proença, L.M.: Genetic algorithms in optimal multistage distribution network planning. IEEE Transactions on Power Systems 9(4), 1927–1933 (1994)
Tang, Y.: Power distribution system planning with reliability modelling and optimization. IEEE Transactions on Power Systems 11(1), 181–189 (1996)
Deb, K., Pratap, A., Agrawal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
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Carrano, E.G., Takahashi, R.H.C., Fonseca, C.M., Neto, O.M. (2007). Bi-objective Combined Facility Location and Network Design. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol 4403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70928-2_38
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DOI: https://doi.org/10.1007/978-3-540-70928-2_38
Publisher Name: Springer, Berlin, Heidelberg
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