A Sustainable Reverse Logistics System: A Retrofit Case

  • Ana Pires
  • Graça Martinho
  • Susana Rodrigues
  • Maria Isabel Gomes


This chapter presents a real case study of a recyclable waste collection system aiming at redesigning service areas and associated vehicle collection routes to support a sustainable operation. Not only economic objectives are to be considered, but also one should account for environmental and social aspects. The economic dimension is modeled through traveling distance that directly influences the global cost. The environmental one is modeled throughout the calculations of the CO2 emissions. Finally, the social aspect is considered by aiming to define a balanced solution regarding working hours among drivers. A multi-objective solution approach based on mixed-integer linear programming models is developed and applied to real data.


Carbon dioxide emissions Global cost Multi-objective programming Routing problem Working hours 

Supplementary material


  1. Azi N, Gendreau M, Potvin JY (2010) An exact algorithm for a vehicle routing problem with time windows and multiple use of vehicles. Eur J Oper Res 202:756–763CrossRefGoogle Scholar
  2. Balas E, Padberg M (1976) Set partitioning: a survey. SIAM Rev 18:710–760CrossRefGoogle Scholar
  3. Baldacci R, Hadjiconstantinou E, Mingozzi A (2004) An exact algorithm for the capacitated vehicle routing problem based on a two-commodity network flow formulation. Oper Res 52:723–738CrossRefGoogle Scholar
  4. Barth M, Scora G, Younglove T (2004) Modal emissions model for heavy-duty diesel vehicles. Trans Res Rec 1880:10–20CrossRefGoogle Scholar
  5. Coello CAC, Romero CEM. Evolutionary algorithms and multiple objective optimization. In: M. Ehrgott, X. Gandibleux, editors, Multiple criteria optimization: state of the art annotated bibliographic surveys; 2003. p. 277–331Google Scholar
  6. Department for Environment, Food and Rural Affairs (Defra). Guidelines to Defra/DECC’s GHG conversion factors for company reporting. Accessed 17 Mar 2018
  7. Laporte G. What you should know about the vehicle routing problem. Naval Research Logistics 2007;54:811–819CrossRefGoogle Scholar
  8. Mavrotas G (2009) Effective implementation of the epsilon-constraint method in multi-objective mathematical programming problems. Appl Math Comput 213:455–465Google Scholar
  9. Oliveira A, Vieira O (2007) Adaptive memory programming for the vehicle routing problem with multiple trips. Comput Oper Res 34:28–47Google Scholar
  10. Petch RJ, Salhi S (2003) A multi-phase constructive heuristic for the vehicle routing problem with multiple trips. Discret Appl Math 133:69–92CrossRefGoogle Scholar
  11. Ramos TRP (2012) Tactical and operational planning in reverse logistics systems with multiple depots. Dissertation, Universidade Técnica de LisboaGoogle Scholar
  12. Ramos TRP, Gomes MI, Barbosa-Povoa AP (2013) Planning waste cooking oil collection systems. Waste Manag 33:1691–1703CrossRefGoogle Scholar
  13. Rieck J, Zimmermann J (2010) A new mixed integer linear model for a rich vehicle routing problem with docking constraints. Ann Oper Res 181:337–358CrossRefGoogle Scholar
  14. Yu PL (1985) Multiple criteria decision making: concepts, techniques and extensions. Plenum Press, New YorkCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Ana Pires
    • 1
  • Graça Martinho
    • 1
  • Susana Rodrigues
    • 1
  • Maria Isabel Gomes
    • 1
  1. 1.Faculty of Sciences and TechnologyUniversidade NOVA de Lisboa (FCT NOVA)CaparicaPortugal

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