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Using simheuristics to promote horizontal collaboration in stochastic city logistics

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Abstract

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.

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References

  1. 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)

    Article  Google Scholar 

  2. Balaprakash, P., Birattari, M., Stützle, T., Dorigo, M.: Estimation-based metaheuristics for the single vehicle routing problem with stochastic demands and customers. Comput. Optim. Appl. 61(2), 463–487 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  3. 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)

    Article  Google Scholar 

  4. Bertsimas, D.: A vehicle routing problem with stochastic demand. Oper. Res. 40(3), 574–585 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  5. 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)

    Article  MATH  MathSciNet  Google Scholar 

  6. Buijs, P., Alejandro, J., Alvarez, L., Veenstra, M., Roodbergen, K.J.: Improved collaborative transport planning at Dutch logistics service provider fritom. Interfaces 46(2), 119–132 (2016)

    Article  Google Scholar 

  7. Caceres-Cruz, J., Arias, P., Guimarans, D., Riera, D., Juan, A.A.: Rich vehicle routing problem:survey. ACM Comput. Surv. 47(2), 32:1–32:28 (2014)

    Article  Google Scholar 

  8. Calvet, L., Ferrer, A., Gomes, M.I., Juan, A.A., Masip, D.: Combining statistical learning with metaheuristics for the multi-depot vehicle routing problem with market segmentation. Comput. Ind. Eng. 94, 93–104 (2016)

    Article  Google Scholar 

  9. Cordeau, J.F., Gendreau, M., Laporte, G.: A tabu search heuristic for periodic and multi-depot vehicle routing problems. Networks 30, 105–119 (1997)

    Article  MATH  Google Scholar 

  10. De Oliveira, F.B., Enayatifar, R., Sadaei, H.J., Guimarães, F.G., Potvin, J.Y.: A cooperative coevolutionary algorithm for the multi-depot vehicle routing problem. Expert Syst. Appl. 43, 117–130 (2016)

    Article  Google Scholar 

  11. Defryn, C., Sörensen, K., Cornelissens, T.: The selective vehicle routing problem in a collaborative environment. Eur. J. Oper. Res. 250(2), 400–411 (2016)

    Article  MATH  MathSciNet  Google Scholar 

  12. Ehmke, J.F.: Integration of Information and Optimization Models for Routing in City Logistics. Springer, Berlin (2012)

    Book  Google Scholar 

  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

  15. Faulin, J., Juan, A., Lera, F., Grasman, S.: Solving the capacitated vehicle routing problem with environmental criteria based on real estimations in road transportation: A case study. Proced. Soc. Behav. Sci. 20, 323–334 (2011)

    Article  Google Scholar 

  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)

  17. Fikar, C., Juan, A., Martinez, E., Hirsch, P.: A discrete-event metaheuristic for dynamic home-service routing with synchronized ride-sharing. Eur. J. Ind. Eng. 10(3), 323–340 (2016)

    Article  Google Scholar 

  18. Gonzalez-Feliu, J., Semet, F., Routhier, J.: Sustainable Urban Logistics: Concepts, Methods and Information Systems. Springer, Berlin, Heidelberg (2014)

  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

  20. Grasas, A., Juan, A.A., Lourenço, H.R.: Simils: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization. J. Simul. 10(1), 69–77 (2016)

    Article  Google Scholar 

  21. 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. Theory 46, 101–117 (2014)

    Article  Google Scholar 

  22. 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)

    Article  MATH  MathSciNet  Google Scholar 

  23. 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)

    Article  Google Scholar 

  24. Juan, A.A., Faulin, J., Grasman, S.E., Rabe, M., Figueira, G.: A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems. Oper. Res. Perspect. 2, 62–72 (2015)

    Article  MathSciNet  Google Scholar 

  25. Juan, A.A., Faulin, J., Jorba, J., Caceres, J., Marquès, J.M.: Using parallel & distributed computing for real-time solving of vehicle routing problems with stochastic demands. Ann. Oper. Res. 207(1), 43–65 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  26. 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)

    Article  Google Scholar 

  27. 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. Theory 46, 40–52 (2014)

    Article  Google Scholar 

  28. Juan, A.A., Mendez, C.A., Faulin, J., de Armas, J., Grasman, S.E.: Electric vehicles in logistics and transportation: a survey on emerging environmental, strategic, and operational challenges. Energies 9(2), 86 (2016)

    Article  Google Scholar 

  29. 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)

    Article  MATH  MathSciNet  Google Scholar 

  30. Karakatic, S., Podgorelec, V.: A survey of genetic algorithms for solving multi depot vehicle routing problem. Appl. Soft Comput. 27, 519–532 (2015)

  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)

  32. Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated local search: framework and applications. In: Gendreau, M., Potvin, J. (eds.) Handbook of Metaheuristics, 2nd edn, pp. 363–397. Springer, New York (2010)

    Chapter  Google Scholar 

  33. Mancini, S.: A real-life multi depot multi period vehicle routing problem with a heterogeneous fleet: formulation and adaptive large neighborhood search based matheuristic. Transp. Res. Part C Emerg. Technol. 70, 100–112 (2016)

    Article  Google Scholar 

  34. Montoya-Torres, J.R., López Franco, J., Nieto Isaza, S., Felizzola Jiménez, H., Herazo-Padilla, N.: A literature review on the vehicle routing problem with multiple depots. Comput. Ind. Eng. 79, 115–129 (2015)

    Article  Google Scholar 

  35. Muñoz-Villamizar, A., Montoya-Torres, J., Vega-Mejía, C.A.: Non-collaborative versus collaborative last-mile delivery in urban systems with stochastic demands. Proced. CIRP 30, 263–268 (2015)

    Article  Google Scholar 

  36. 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)

    Article  MATH  MathSciNet  Google Scholar 

  37. Pisinger, D., Ropke, S.: A general heuristic for vehicle routing problems. Comput. Oper. Res. 34, 2403–2435 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  38. Pomponi, F., Fratocchi, L., Tafuri, S.R.: Trust development and horizontal collaboration in logistics: a theory based evolutionary framework. Supply Chain Manag. Int. J. 20(1), 83–97 (2015)

    Article  Google Scholar 

  39. Raychaudhuri, S.: Introduction to monte carlo simulation. In: Proceedings of the 2008 Winter Simulation Conference, pp. 91–100 (2008)

  40. 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)

    Article  MATH  Google Scholar 

  41. Toth, P., Vigo, D. (eds.): Vehicle Routing—Problems, Methods and Applications, 2nd edn. SIAM - Society for Industrial and Applied Mathematics, New Delhi (2014)

    MATH  Google Scholar 

  42. Ubeda, S., Arcelus, F., Faulin, J.: Green logistics at eroski: a case study. Int. J. Prod. Econ. 131(1), 44–51 (2011)

    Article  Google Scholar 

  43. United States Environmental Protection Agency: Greenhouse gas emissions 1990–2013 (2013). http://www3.epa.gov/otaq/climate/documents/420f15032.pdf

  44. 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)

    Article  MATH  MathSciNet  Google Scholar 

  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 

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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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Authors and Affiliations

  1. Open University of Catalonia - I N3, Barcelona, Spain

    Carlos L. Quintero-Araujo, Aljoscha Gruler & Angel A. Juan

  2. Pompeu Fabra University, Barcelona, Spain

    Jesica de Armas & Helena Ramalhinho

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  1. Carlos L. Quintero-Araujo
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  2. Aljoscha Gruler
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Correspondence to Angel A. Juan.

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Quintero-Araujo, C.L., Gruler, A., Juan, A.A. et al. Using simheuristics to promote horizontal collaboration in stochastic city logistics. Prog Artif Intell 6, 275–284 (2017). https://doi.org/10.1007/s13748-017-0122-8

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