Smart City pp 183-192 | Cite as

Environmental Sustainable Fleet Planning in B2C e-Commerce Urban Distribution Networks

  • Francesco Carrabs
  • Raffaele Cerulli
  • Anna SciomachenEmail author
Part of the Progress in IS book series (PROIS)


Sustainable distribution is one of the topics concerning the smart city concept. In this chapter we face the problem of delivering a given amount of goods in urban areas arising from e-channel department stores, with the aim of minimizing the overall distribution costs; costs take into account traveling components, loading and other operative aspects, and environmental issues. More precisely, in the present business to consumer distribution problem, we have to determine the fleet of not homogeneous vehicles (trucks, wagons, vans and picks-up) to be used for satisfying the demands of clients coming from e-channels, and their related itineraries, given the traveling limits imposed by the urban government; in particular, we have to respect the maximum route length constraints and use the appropriate vehicles for each kind of street. We propose a mathematical programming model to solve this computationally difficult problem, which is strategic for being able to implement sustainable distribution plans in a smart city context. Preliminary results of test bed cases related to different sized urban distribution networks are reported and analyzed.


City logistics Sustainable distribution e-Channel Network models Vehicle routing problem 


  1. 1.
    Anderson, S., Allen, J., & Browne, M. (2005). Urban logistics—how can it meet policy makers—sustainability objectives? Journal of Transport Geography, 13, 71–81.CrossRefGoogle Scholar
  2. 2.
    Anttiroiko, A. V., Valkama, P., & Bailey, S. J. (2013). Smart cities in the new service economy: Building platforms for smart services. AI & Society,. doi: 10.1007/s00146-013-0464-0.Google Scholar
  3. 3.
    Bräysy, O., & Gendreau, M. (2005). Vehicle routing problem with time windows, part ii: Metaheuristics. Transportation Science, 39(1), 119–139.CrossRefGoogle Scholar
  4. 4.
    Cordeau, J-F., Desaulniers, G., Desrosiers, J., Solomon, M. M., & Soumis, F. (2002). The vehicle routing problem. In P. Toth & D. Vigo (Eds.), volume 9 of SIAM monographs on discrete mathematics and applications, Chap. 7, 157193. SIAM, Philidelphia.Google Scholar
  5. 5.
    Crainic, T. G., Ricciardi, N., & Storchi, G. (2004). Advanced freight transportation systems for congested urban areas. Transportation Research Part C, 12, 119–137.CrossRefGoogle Scholar
  6. 6.
    Crainic, T. G., Ricciardi, N., & Storchi, G. (2009). Models for evaluating and planning city logistics systems. Transportation Science, 43(4), 432–454.CrossRefGoogle Scholar
  7. 7.
    Figliozzi, M. A. (2010). The impacts of congestion of commercial vehicle tour characteristics and costs. Transportation Research Part E, 46, 496–506.CrossRefGoogle Scholar
  8. 8.
    Lenstra, J. K., & Rinnooy Kan, A. H. G. (1981). Complexity of vehicle routing and scheduling problems. Networks, 11, 221–227.CrossRefGoogle Scholar
  9. 9.
    Li, J. Q., Borenstein, D., & Mirchandani, P. B. (2007). A decision support system for the single-depot vehicle rescheduling problem. Computers & Operations Research, 34, 1008–1032.CrossRefGoogle Scholar
  10. 10.
    Qureshi, A. G., Taniguchi, E., & Yamada, T. (2009). An exact solution approach for vehicle routing and scheduling problems with soft time windows. Transportation Research E, 45(6), 960–977.CrossRefGoogle Scholar
  11. 11.
    Taniguchi, E., Thompson, R. G., Yamada, T., & Van Duin, R. (2001). City logistics—network modeling and Intelligent transport systems. Oxford: Elsevier.Google Scholar
  12. 12.
    Taniguchi, E., Thompson, R. G., & Yamada, T. (2012). Emerging techniques for enhancing the practical application of city logistics models. Procedia—Social and Behavioral Sciences, 39, 3–18.CrossRefGoogle Scholar
  13. 13.
    Teo, J. S. E., Taniguchi, E., & Qureshi, A. G. (2012). Evaluating city logistics measure in e-commerce with multiagent systems. Procedia—Social and Behavioral Sciences, 39, 349–359.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Francesco Carrabs
    • 1
  • Raffaele Cerulli
    • 1
  • Anna Sciomachen
    • 2
    Email author
  1. 1.Department of MathematicsUniversity of SalernoFiscianoItaly
  2. 2.Department of Economics and Business StudiesUniversity of GenoaGenoaItaly

Personalised recommendations