Using simheuristics to promote horizontal collaboration in stochastic city logistics
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This paper analyzes the role of horizontal collaboration (HC) concepts in urban freight transportation under uncertainty scenarios. The paper employs different stochastic variants of the well-known vehicle routing problem (VRP) in order to contrast a non-collaborative scenario with a collaborative one. This comparison allows us to illustrate the benefits of using HC strategies in realistic urban environments characterized by uncertainty in factors such as customers’ demands or traveling times. In order to deal with these stochastic variants of the VRP, a simheuristic algorithm is proposed. Our approach integrates Monte Carlo simulation inside a metaheuristic framework. Some computational experiments contribute to quantify the potential gains that can be obtained by the use of HC practices in modern city logistics.
KeywordsCity logistics Horizontal collaboration Stochastic optimization Vehicle routing problems Simheuristics
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.
- 13.European Commission: Cities of tomorrow: Challenges, visions, ways forward (2011). http://ec.europa.eu/regional_policy/sources/docgener/studies/pdf/citiesoftomorrow/citiesoftomorrow_final.pdf. Accessed 21 Mar 2017
- 14.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
- 16.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 a sustainable collaborative network: The case of urban freight delivery through logistics platforms. In: IEEE International Conference on Digital Ecosystems and Technologies, pp. 126–131 (2013)Google Scholar
- 18.Gonzalez-Feliu, J., Semet, F., Routhier, J.: Sustainable Urban Logistics: Concepts, Methods and Information Systems. Springer, Berlin, Heidelberg (2014)Google Scholar
- 19.Gonzalez-Martin, S., Juan, A.A., Riera, D., Elizondo, M.G., Ramos, J.J.: A simheuristic algorithm for solving the arc-routing problem with stochastic demands. J. Simul. (2016). doi: 10.1057/jos.2016.11
- 30.Karakatic, S., Podgorelec, V.: A survey of genetic algorithms for solving multi depot vehicle routing problem. Appl. Soft Comput. 27, 519–532 (2015)Google Scholar
- 31.Li, J., Pardalos, P.M., Sun, H., Pei, J., Zhang, Y.: Iterated local search embedded adaptive neighborhood selection approach for the multi-depot vehicle routing problem with simultaneous deliveries and pickups. Expert Syst. Appl. 42(7), 3551–3561 (2015)Google Scholar
- 39.Raychaudhuri, S.: Introduction to monte carlo simulation. In: Proceedings of the 2008 Winter Simulation Conference, pp. 91–100 (2008)Google Scholar
- 43.United States Environmental Protection Agency: Greenhouse gas emissions 1990–2013 (2013). http://www3.epa.gov/otaq/climate/documents/420f15032.pdf
- 45.Zuhori, S.T., Peya, Z.J., Mahmud, F.: A novel three-phase approach for solving multi-depot vehicle routing problem with stochastic demand. Algorithms Res. 1(4), 15–19 (2012)Google Scholar