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Step cost functions in a fleet size and mix vehicle routing problem with time windows

  • S.I.: CLAIO 2018
  • Published:
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Abstract

The vehicle routing problem is a traditional combinatorial problem with practical relevance for a wide range of industries. In the literature, several specificities have been tackled by dedicated methods in order to better reflect real-world situations. Following this trend, this article addresses the fleet size and mix vehicle routing problem with time windows in which companies hire a third-party logistics company. The shipping charges considered in this work are calculated using step cost functions, in which values are determined according to the type of vehicle and the total distance traveled, with fixed values for predefined distance ranges. A mixed integer linear programming model is introduced and two sequential insertion heuristics are proposed. The introduced methods are examined through a computational comparative analysis in small-sized instances with known optimal solution, 168 benchmark instances from the literature, and 3 instances based on a real-world problem from the civil construction industry. The numerical experiments show that the proposed methods are efficient and show good performance in different scenarios.

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Notes

  1. https://pressroom.ups.com/pressroom/ContentDetailsViewer.page?ConceptType=FactSheets&id=1426321563187-193.

  2. http://images.fedex.com/us/smartpostguide/pdf/FXSP_ByPound_2017.pdf.

References

  • Belfiore, P., & Yoshizaki, H. T. Y. (2009). Scatter search for a real-life heterogeneous fleet vehicle routing problem with time windows and split deliveries in Brazil. European Journal of Operational Research, 199(3), 750–758.

    Article  Google Scholar 

  • Birgin, E. G., Ferreira, J. E., & Ronconi, D. P. (2015). List scheduling and beam search methods for an extended version of the flexible job shop scheduling problem. European Journal of Operational Research, 247(2), 421–440.

    Article  Google Scholar 

  • Bräysy, O., & Gendreau, M. (2005). Vehicle routing problem with time windows, Part I: Route construction and local search algorithms. Transportation Science, 39(1), 104–118.

    Article  Google Scholar 

  • Bräysy, O., Porkka, P. P., Dullaert, W., Repoussis, P. P., & Tarantilis, C. D. (2009). A well-scalable metaheuristic for the fleet size and mix vehicle routing problem with time windows. Expert Systems with Applications, 36(1), 8460–8475.

    Article  Google Scholar 

  • Cordeau, J.-F., Laporte, G., Savelsbergh, M. W. P., & Vigo, D. (2007). Vehicle routing. In C. Barnhart & G. Laporte (Eds.), Handbooks in operations research and management science, chapter 6 (pp. 195–224). Amsterdam: Elsevier.

    Google Scholar 

  • Dell’Amico, M., Monaci, M., Pagani, C., & Vigo, D. (2007). Heuristic approaches for the fleet size and mix vehicle routing problem with time windows. Transportation Science, 41(4), 516–526.

    Article  Google Scholar 

  • Dullaert, W., Janssens, G. K., Sörensen, K., & Vernimmen, B. (2002). New heuristics for the fleet size and mix vehicle routing problem with time windows. Journal of the Operational Research Society, 53(11), 1232–1238.

    Article  Google Scholar 

  • Ehmke, J. F., Campbell, A. M., & Thomas, B. W. (2016). Vehicle routing to minimize time-dependent emissions in urban areas. European Journal of Operational Research, 251(2), 478–494.

    Article  Google Scholar 

  • Escobar, J . W. (2014). Heuristic algorithms for the capacitated location-routing problem and the multi-depot vehicle routing problem. 4OR-A Quarterly Journal of Operations Research, 12(1), 99–100.

    Article  Google Scholar 

  • Ghiani, G., Laporte, G., & Musmanno, R. (2004). Introduction to logistics systems planning and control. Hoboken: Wiley.

    Google Scholar 

  • Golden, B., Assad, A., Levy, L., & Gheysens, F. (1984). The fleet size and mix vehicle routing problem. Computers & Operations Research, 11(1), 49–66.

    Article  Google Scholar 

  • Gudehus, T., & Kotzab, H. (2012). Comprehensive logistics (2nd ed.). Heidelberg: Springer.

    Book  Google Scholar 

  • Hoff, A., Andersson, H., Christiansen, M., Hasle, G., & Løkketangen, A. (2010). Industrial aspects and literature survey: Fleet composition and routing. Computers & Operations Research, 37(12), 2041–2061.

    Article  Google Scholar 

  • Koç, Ç., Bektaş, T., Jabali, O., & Laporte, G. (2015). A hybrid evolutionary algorithm for heterogeneous fleet vehicle routing problems with time windows. Computers & Operations Research, 64, 11–27.

    Article  Google Scholar 

  • Koç, Ç., Bektaş, T., Jabali, O., & Laporte, G. (2016). Thirty years of heterogeneous vehicle routing. European Journal of Operational Research, 249(1), 1–21.

    Article  Google Scholar 

  • Kritikos, M. N., & Ioannou, G. (2013). The heterogeneous fleet vehicle routing problem with overloads and time windows. International Journal of Production Economics, 144(1), 68–75.

    Article  Google Scholar 

  • Laporte, G. (2009). Fifty years of vehicle routing. Transportation Science, 43(4), 408–416.

    Article  Google Scholar 

  • Lieb, R. C., & Lieb, K. J. (2015). The North American third-party logistics industry in 2013: The provider CEO perspective. Transportation Journal, 54(1), 104–121.

    Article  Google Scholar 

  • Liu, F.-H., & Shen, S.-Y. (1999). The fleet size and mix vehicle routing problem with time windows. Journal of the Operational Research society, 50(7), 721–732.

    Article  Google Scholar 

  • Mao, Z., Huang, D., Fang, K., Wang, C., & Lu, D. (2020). Milk-run routing problem with progress-lane in the collection of automobile parts. Annals of Operations Research, 291, 657–684.

    Article  Google Scholar 

  • Marasco, A. (2008). Third-party logistics: A literature review. International Journal of Production Economics, 113(1), 127–147.

    Article  Google Scholar 

  • Paraskevopoulos, D. C., Repoussis, P. P., Tarantilis, C. D., Ioannou, G., & Prastacos, G. P. (2008). A reactive variable neighborhood tabu search for the heterogeneous fleet vehicle routing problem with time windows. Journal of Heuristics, 14(5), 425–455.

    Article  Google Scholar 

  • Pureza, V. (2008). Waiting and buffering strategies for the dynamic pickup and delivery problem with time windows. INFOR: Information Systems and Operational Research, 46(3), 165–175.

    Google Scholar 

  • Sniezek, J., & Bodin, L. (2006). Using mixed integer programming for solving the capacitated arc routing problem with vehicle/site dependencies with an application to the routing of residential sanitation collection vehicles. Annals of Operations Research, 144(5), 33–58.

    Article  Google Scholar 

  • Solomon, M. M. (1987). Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research, 35(2), 254–265.

    Article  Google Scholar 

  • Vidal, T., Crainic, T. G., Gendreau, M., & Prins, C. (2013). Heuristics for multi-attribute vehicle routing problems: A survey and synthesis. European Journal of Operational Research, 231(1), 1–21.

    Article  Google Scholar 

  • Vidal, T., Crainic, T. G., Gendreau, M., & Prins, C. (2014). A unified solution framework for multi-attribute vehicle routing problems. European Journal of Operational Research, 234(3), 658–673.

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank Prof. Laporte for fruitfull discussion that contributed to the development of this work. The authors also would like to thank the careful reading and the comments of the reviewers that helped a lot to improve the quality of this work.

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Correspondence to Débora P. Ronconi.

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Débora P. Ronconi: Supported by FAPESP (Grants 2016/01860-1 and 2013/07375-0) and CNPq (Grant 306083/2016-7).

João L. V. Manguino: Partially developed while J. Manguino was visiting Prof. G. Laporte at HEC Montreal in 2018.

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Manguino, J.L.V., Ronconi, D.P. Step cost functions in a fleet size and mix vehicle routing problem with time windows. Ann Oper Res 316, 1013–1038 (2022). https://doi.org/10.1007/s10479-020-03915-y

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