A “Wait and See” solution for theopen vehicle routing problem with stochastic demands under a point estimation scheme
DOI:
https://doi.org/10.24054/rcta.v3iEspecial.851Keywords:
Demanda estocástica, estabilidad, método de estimación por 2 puntos, ruteo abierto, ruteo de vehículos, solución wait and seeAbstract
En este artículo se propone una técnica de solución estocástica bajo un esquema “wait and see” para abordar los siguientes problemas de ruteo con demandas estocásticas: CVRPSD y OVRPSD. La técnica de solución ha venido siendo aplicada en los últimos diez años al problema de flujo de carga estocástico en redes de distribución de energía eléctrica. El objetivo de la técnica es encontrar un valor esperado de costos operativos con su respectiva desviación estándar a partir de 2J escenarios probabilísticos desacoplados, siendo J el número de clientes en un escenario estocástico. Para su verificación se usan instancias propuestas en la literatura del VRP, observándose la influencia que ejerce en los costos operativos la estocasticidad de la demanda.
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Arias Hernández, A., Hincapié Isaza, R., & Gallego Rendón, R. (2014). Comparación de flujos de carga probabilísticos empleados en sistemas de distribución levemente enmallados. Scientia et Technica, 19(2), 153–162. https://doi.org/10.22517/23447214.9025
Bertsimas, D. J. (1992). A Vehicle Routing Problem with Stochastic Demand. Operations Research, 40(3), 574–585. https://doi.org/10.1287/opre.40.3.574
Birge, J., & Louveaux, F. (2011). Introduction to stochastic programming (second edi; S. S. in O. R. and F. Engineering, ed.).
Christiansen, C. H., & Lysgaard, J. (2007). A branch-and-price algorithm for the capacitated vehicle routing problem with stochastic demands. Operations Research Letters. https://doi.org/10.1016/j.orl.2006.12.009
Fourer, R., Gay, D. M., & Kernighan, B. W. (1993). AMPL. A modeling language for mathematical programming.
Gauvin, C., Desaulniers, G., & Gendreau, M. (2014). A branch-cut-and-price algorithm for the vehicle routing problem with stochastic demands. Computers and Operations Research, 50, 141–153. https://doi.org/10.1016/j.cor.2014.03.028
Gendreau, M., & Laporte, G. (1996). EUROPEAN JOURNAL OF OPERATIONAL RESEARCH Stochastic vehicle routing. In European Journal of Operational Research (Vol. 88).
Hjorring, C., & Holt, J. (1999). New optimality cuts for a single-vehicle stochastic routing problem. Annals of Operations Research, 86, 569–584. https://doi.org/10.1023/A:1018995927636
Hong, H. (1998). An efficient point estimate method for probabilistic analysis. Reliability Engineering and System Safety, 59(3), 261– 267. https://doi.org/10.1016/S0951-8320(97)00071-9
Laporte, G., Louveaux, F., & Mercure, H. (1989). Models and exact solutions for a class of stochastic location-routing problems. European Journal of Operational Research, 39(1), 71–78. https://doi.org/10.1016/0377-2217(89)90354-8
Laporte, G., & Louveaux, F. V. (1998). Solving Stochastic Routing Problems with the Integer L-Shaped Method. In Fleet Management and Logistics (pp. 159–167). https://doi.org/10.1007/978-1-4615-5755-5_7
Laporte, G., Louveaux, F. V, & van Hamme, L. (2002). An Integer L -Shaped Algorithm for the Capacitated Vehicle Routing Problem with Stochastic Demands. Operations Research, 50(3), 415–423. https://doi.org/10.1287/opre.50.3.415.7751
Lavorato, M., Franco, J. F., Rider, M. J., & Romero, R. (2012). Imposing radiality constraints in distribution system optimization problems. IEEE Transactions on Power Systems, 27(1), 172–180. https://doi.org/10.1109/TPWRS.2011.2161349
Ospina-Toro, D, Toro-Ocampo, E. M., & Gallego- Rendón, R. A. (2018). Solución Del MDVRP Usando El Algoritmo De Búsqueda Local Iterada. Revista Colombiana de Tecnologías de Avanzada, 1(31), 120–127. https://doi.org/https://doi.org/10.24054/16927257.v31.n31.2018.2774
Ospina Castaño, A., Toro-Ocampo, E. M., & Gallego-Rendón, R. A. (2019). Sensibility Analysis for the MULTI-Objective MDVRPPC that Considering Cost and Environmental Impact. Revista Colombiana de Tecnologias de Avanzada, 2(34), 8.
Perboli, G., Rosano, M., Saint-Guillain, M., & Rizzo, P. (2018). Simulation-optimisation framework for City Logistics: An application on multimodal last-mile delivery. IET Intelligent Transport Systems, 12(4), 262– 269. https://doi.org/10.1049/iet-its.2017.0357
Rei, W., Gendreau, M., & Soriano, P. (2010). A Hybrid Monte Carlo Local Branching Algorithm for the Single Vehicle Routing Problem with Stochastic Demands. Transportation Science, 44(1), 136–146. https://doi.org/10.1287/trsc.1090.0295
Rosenblueth, E. (1981). Two-point estimates in probabilities. Applied Mathematical Modelling, 5(5), 329–335. https://doi.org/10.1016/S0307-904X(81)80054-6
Secomandi, N., & Margot, F. (2008). Reoptimization Approaches for the Vehicle- Routing Problem with Stochastic Demands. Operations Research, 57(1), 214–230. https://doi.org/10.1287/opre.1080.0520
Stewart, W. R., & Golden, B. L. (1983). Stochastic vehicle routing: A comprehensive approach. European Journal of Operational Research, 14(4), 371–385. https://doi.org/10.1016/0377-2217(83)90237-0
Su, C. L. (2005). Probabilistic load-flow computation using point estimate method. IEEE Transactions on Power Systems, 20(4), 1843–1851. https://doi.org/10.1109/TPWRS.2005.857921
Toro-ocampo, E. M. (2016). Solución del problema de localización y ruteo usando un modelo matemático flexible y considerando efectos ambientales (Universidad Tecnológica de Pereira).
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