Abstract
Horizontal Cooperation (HC) in transportation activities has the potential to decrease supply chain costs and the environmental impact of delivery vehicles related to greenhouse gas emissions and noise. Especially in urban areas the sharing of information and facilities among members of the same supply chain level promises to be an innovative transportation concept. This paper discusses the potential benefits of HC in supply chains with stochastic demands by applying a simheuristic approach. For this, we integrate Monte Carlo Simulation into a metaheuristic process based on Iterated Local Search and Biased Randomization. A non-cooperative scenario is compared to its cooperative counterpart which is formulated as multi-depot Vehicle Routing Problem with stochastic demands (MDVRPSD).
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References
Bahinipati, B., Kanada, A., Deshmukh, S.: Horizontal collaboration in semiconductor manufacturing industry supply chain: an evaluation of collaboration intensity index. Comput. Ind. Eng. 57, 880–895 (2009)
Ballot, E., Fontane, F.: Reducing transportation CO2 emissions through pooling of supply networks: perspectives from a case study in french retail chains. Prod. Plan. Control 21(6), 640–650 (2010)
Bianchi, L., Dorigo, M., Gambardella, L.M., Gutjahr, W.J.: A survey on metaheuristics for stochastic combinatorial optimization. Nat. Comput. 8(2), 239–287 (2009)
Cordeau, J.F., Gendreau, M., Laporte, G.: A tabu search heuristic for periodic and multi-depot vehicle routing problems. Networks 30, 105–119 (1997)
Ehmke, J.F.: Integration of Information and Optimization Models for Routing in City Logistics. Springer Science+Business, New York (2012)
European Commission: Cities of tomorrow - challanges, visions, ways forward. Publications Office of the European Union (2011)
European Environment Agency: Eea draws the first map of Europe’s noise exposure (2009). http://www.eea.europa.eu/media/newsreleases/eea-draws-the-first-map-of-europe2019s-noise-exposure
Faure, L., Battaia, G., Marquès, G., Guillaume, R., Vega-Mejía, C.A., Montova-Torres, J.R., Muñoz-Villamizar, A., Quintero-Araújo, C.L.: How to anticipate the level of activity of asustainable collaborative network: the case of urban freight delivery throughlogistics platforms. In: IEEE International Conference on Digital Ecosystems and Technologies, pp. 126–131 (2013)
Fikar, C., Juan, A., Martinez, E., Hirsch, P.: A discrete-event driven metaheuristic for dynamic home service routing with synchronised trip sharing. Eur. J. Ind. Eng. 10(3), 323–340 (2016)
González, S., Riera, D., Juan, A., Elizondo, M., Fonseca, P.: SIM-RandSHARP: a hybrid algorithm for solving the Arc routing problem with stochastic demands. In: Proceedings of the 2012 Winter Simulation Conference, pp. 1–11 (2012)
Gonzalez-Feliu, J., Routhier, J.: Sustainable Urban Logistics: Concepts, Methods and Information Systems. Springer, Heidelberg (2014)
Juan, A.A., Barrios, B.B., Vallada, E., Riera, D., Jorba, J.: A simheuristic algorithm for solving the permutation flow shop problem with stochastic processing times. Simul. Model. Pract. Theor. 46, 101–117 (2014)
Juan, A.A., Faulin, J., Grasman, S., Riera, D., Marull, J., Mendez, C.: Using safety stocks and simulation to solve the vehicle routing problem with stochastic demands. Transp. Res. Part C: Emerg. Technol. 19(5), 751–765 (2011)
Juan, A.A., Faulin, J., Jorba, J., Riera, D., Masip, D., Barrios, B.: On the use of monte carlo simulation, cache and splitting techniques to improve the clarke and wright savings heuristics. J. Oper. Res. Soc. 62(6), 1085–1097 (2011)
Juan, A.A., Grasman, S.E., Caceres-Cruz, J., Bektaş, T.: A simheuristic algorithm for the single-period stochastic inventory-routing problem with stock-outs. Simul. Model. Pract. Theor. 46, 40–52 (2014)
Juan, A.A., Faulin, J., Ferrer, A., Lourenço, H.R., Barrios, B.: MIRHA: multi-start biased randomization of heuristics with adaptive local search for solving non-smooth routing problems. Top 21(1), 109–132 (2013)
Juan, A.A., Faulin, J., Grasman, S.E., Rabe, M., Figueira, G.: A review of simheuristics: extending metaheuristics to deal with stochastic combinatorialoptimization problems. Oper. Res. Perspect. 2, 62–72 (2015)
Juan, A.A., Pascual, I., Guimarans, D., Barrios, B.: Combining biased randomization with iterated local search for solving the multidepot vehicle routing problem. Int. Trans. Oper. Res. 22(4), 647–667 (2015)
Lourenço, H.R., Martin, O.C., Stützle, T.: Iteratedlocal search: framework and applications. In: Gendreau, M., Potvin, J. (eds.) Handbook of Metaheuristics, 2nd edn, pp. 363–397. Springer, New York (2010)
Montoya-Torres, J.R., Franco, J.L., Isaza, S.N., Jiménez, H.F., Herazo-Padilla, N.: A literature review on the vehicle routing problem with multiple depots. Comput. Ind. Eng. 79, 115–129 (2015)
Muñoz-Villamizar, A., Montoya-Torres, J., Vega-Mejía, C.A.: Non-collaborative versus collaborative last-mile delivery in urban systems with stochastic demands. Procedia CIRP 30, 263–268 (2015)
Pérez-Bernabeu, E., Juan, A.A., Faulin, J., Barrios, B.B.: Horizontal cooperation in road transportation: a case illustrating savings in distances and greenhouse gas emissions. Int. Trans. Oper. Res. 22(3), 585–606 (2015)
Pisinger, D., Ropke, S.: A general heuristic for vehicle routing problems. Comput. Oper. Res. 34, 2403–2435 (2007)
Raychaudhuri, S.: Introduction to monte carlo simulation. In: Proceedings of the 2008 Winter Simulation Conference, pp. 91–100 (2008)
Ritzinger, U., Puchinger, J., Hartl, R.F.: A survey on dynamic and stochastic vehicle routing problems. Int. J. Prod. Res. 54(1), 215–231 (2016)
Toth, P., Vigo, D. (eds.): Vehicle Routing - Problems, Methods and Applications, 2nd edn. SIAM - Society for Industrial and Applied Mathematics, Philadelphia (2014)
United States Environmental Protection Agency: Greenhouse Gas Emissions 1990–2013 (2013). http://www3.epa.gov/otaq/climate/documents/420f15032.pdf
Vidal, T., Crainic, T., Gendreau, M., Lahrichi, N., Rei, W.: A hybrid genetic algorithm for multi-depot and periodic vehicle routing problems. Oper. Res. 60(3), 611–624 (2012)
Acknowledgements
This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P and TRA2015-71883-REDT), and FEDER. Likewise, we want to acknowledge the support received by the Department of Universities, Research & Information Society of the Catalan Government (2014-CTP-00001), the Special Patrimonial Fund from Universidad de La Sabana and the doctoral grant of the UOC.
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Quintero-Araujo, C.L., Gruler, A., Juan, A.A. (2016). Quantifying Potential Benefits of Horizontal Cooperation in Urban Transportation Under Uncertainty: A Simheuristic Approach. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_26
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DOI: https://doi.org/10.1007/978-3-319-44636-3_26
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