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Freight distribution performance indicators for service quality planning in large transportation networks

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Abstract

This paper studies the use of performance indicators in routing problems to estimate how transportation cost is affected by the quality of service offered. The quality of service is assumed to be directly dependent on the size of the time windows. Smaller time windows mean better service. Three performance indicators are introduced. These indicators are calculated directly from the data without the need of a solution method. The introduced indicators are based mainly on a “request compatibility”, which describes whether two visits can be scheduled consecutively in a route. Other two indicators are introduced, which get their values from a greedy constructive heuristic. After introducing the indicators, the correlation between indicators and transportation cost is examined. It is concluded that the indicators give a good first estimation on the transportation cost incurred when providing a certain quality of service. These indicators can be calculated easily in one of the first planning steps without the need of a sophisticated solution tool. The contribution of the paper is the introduction of a simple set of performance indicators that can be used to estimate the transportation cost of a routing problem with time windows.

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Notes

  1. Other correlation types can also be detected, but for our purpose of identifying a significant trend for such indicators, a linear correlation analysis is considered as adequate.

References

  • Baldacci R, Toth P, Vigo D (2007) Recent advances in vehicle routing exact algorithms. 4OR: Q J Oper Res 5–4:269–298

    Article  MathSciNet  Google Scholar 

  • Braysy O, Gendreau M (2005a) Vehicle routing problem with time windows, part I: construction and local search algorithms. Transp Sci 39–1:104–118

    Article  Google Scholar 

  • Braysy O, Gendreau M (2005b) Vehicle routing problem with time windows, part II: Metaheuristics. Transp Sci 39–1:119–139

    Article  Google Scholar 

  • Cordeau JF, Desaulniers G, Desrosiers J, Solomon MM, Soumis F (2002) VRP with time windows. In: Toth P, Vigo D (eds) The vehicle routing problem, vol 9. SIAM Monographs on Discrete Mathematics and Applications, Philadelphia, pp 157–193

    Chapter  Google Scholar 

  • Cordeau JF, Laporte G, Savelsberg M, Vigo D (2007) Vehicle routing. In: Barnhart C, Laporte G (eds) Transportation. Elsevier, North Holland, pp 367–428

    Chapter  Google Scholar 

  • Fischetti M, Lodi A, Martello S, Toth P (2001) A polyhedral approach to simplified crew scheduling and vehicle scheduling problems. Manage Sci 47(6):833–839

    Article  Google Scholar 

  • Gayialis SP, Tatsiopoulos IP (2004) Design of an IT-driven decision support system for vehicle routing and scheduling. Eur J Oper Res 152:382–398

    Article  MATH  Google Scholar 

  • Homberger J, Gehring H (2005) A two-phase hybrid metaheuristic for the vehicle routing problem with time windows. Eur J Oper Res 162:220–238

    Article  MATH  Google Scholar 

  • Laporte G (1992) The vehicle routing problem: an overview of exact and approximate algorithms. Eur J Oper Res 59:345–358

    Article  MATH  Google Scholar 

  • Marcucci E, Danielis R (2008) The potential demand for a urban freight consolidation centre. Transportation 35:269–284

    Article  Google Scholar 

  • Perboli G, Pezzella F, Tadei R (2008) Eve-opt: an hybrid algorithm for the capability vehicle routing problem. Math Methods Oper Res 68:361–382

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Puckett SM, Hensher DA (2008) The role of attribute processing strategies in estimating the preferences of road freight stakeholders. Transp Res Part E 44:379–395

    Article  Google Scholar 

  • Solomon MM (1987) Algorithms for the vehicle routing and scheduling problem with time windows constraints. Oper Res 35:254–265

    Article  MathSciNet  MATH  Google Scholar 

  • Toth P, Vigo D (eds) (2002a) The vehicle routing problem, vol 9. SIAM Monographs on Discrete Mathematics and Applications, Philadelphia

    MATH  Google Scholar 

  • Toth P, Vigo D (2002b) An overview of vehicle routing problems. In: Toth P, Vigo D (eds) The vehicle routing problem, vol 9. SIAM Monographs on Discrete Mathematics and Applications, Philadelphia, pp 1–26

    Chapter  Google Scholar 

  • Wardman M (1998) The value of travel time: a review of British evidence. J Trans Econ Policy 32(3):285–316

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank Simone Amico for his fundamental contribution to this work, which has been partially supported by the Ministero dell’Istruzione, Università e Ricerca (MIUR) (Italian Ministry of University and Research), under the Progetto di Ricerca di Interesse Nazionale (PRIN) 2007 “Optimization of Distribution Logistics”. They would also thank Bruno Dalla Chiara and Jesús Gonzalez Feliu for their valuable suggestions on the paper. The authors finally thank the two anonymous reviewers for their useful comments that have significantly improved the quality of the original manuscript.

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Authors and Affiliations

  1. DITIC (Department of Hydraulics, Transport and Civil Infrastructures), Transport Engineering Working Group, Politecnico Di Torino, corso Duca degli Abruzzi, 24, 10129, Turin, Italy

    Francesco Paolo Deflorio

  2. DAUIN (Department of Control and Computer Engineering), Optimization and Operations Research (ORO) Working Group, Politecnico Di Torino, corso Duca degli Abruzzi, 24, 10129, Turin, Italy

    Guido Perboli & Roberto Tadei

  3. Centre Interuniversitaire de Recherche sur les Reseaux d’Entreprise, la Logistique et le Transport, Montréal, QC, Canada

    Guido Perboli

Authors
  1. Francesco Paolo Deflorio
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  2. Guido Perboli
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  3. Roberto Tadei
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Corresponding author

Correspondence to Francesco Paolo Deflorio.

Appendices

Annex A

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Linear regression for total travel time with PPC, AMTBCR and T I for type 1 (200 customers)

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Fig. 9
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Linear regression for total travel time with PPC, AMTBCR and T I for type 2 (200 customers)

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Annex B

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Linear regression for total travel time with PPC, AMTBCR and T I for type 1 (1,000 customers)

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Fig. 11
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Linear regression for total travel time with PPC, AMTBCR and T I for type 2 (1,000 customers)

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Deflorio, F.P., Perboli, G. & Tadei, R. Freight distribution performance indicators for service quality planning in large transportation networks. Flex Serv Manuf J 22, 36–60 (2010). https://doi.org/10.1007/s10696-010-9072-1

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