Finding the trade-off between emissions and disturbance in an urban context

  • Jasmin Grabenschweiger
  • Fabien Tricoire
  • Karl F. Doerner


We introduce the bi-objective emissions disturbance traveling salesman problem (BEDTSP), which aims at minimizing carbon dioxide emissions (\(\hbox {CO}_2\)) as well as disturbance to urban neighborhoods, when planning the tour of a single vehicle delivering goods to customers. Although there exist recent studies on minimizing emissions, we are not aware of any work on minimizing disturbance. We develop four different mathematical models for the BEDTSP. We also develop several data generation strategies for minimizing disturbance. These strategies consider optional nodes, thus allowing detours that yield less disturbance but also possibly more emissions. All models and strategies are compared in an extensive computational study. Experimental results allow us to derive clear guidelines for which model and data generation strategy to use in which context. Following these guidelines, we conduct a case study for the city of Vienna.


City logistics \(\hbox {CO}_2\) emissions Disturbance Bi-objective traveling salesman problem Bi-objective shortest path problem 



The present research has been conducted in the context of the Green City Hubs Project, #FA379051 funded by Austrian Research Promotion Agency (FFG). We want to thank Christoph Six from the Institute for Powertrains and Automotive Technology of the Technical University of Vienna and Andreas Krawinkler from our department for providing us with real-world data.


  1. Applegate DL, Bixby RE, Chvatal V, Cook WJ (2011) The traveling salesman problem: a computational study. Princeton University Press, PrincetonMATHGoogle Scholar
  2. Barth M, Boriboonsomsin K (2009) Energy and emissions impacts of a freeway-based dynamic eco-driving system. Transp Res Part D Transp Environ 14(6):400–410CrossRefGoogle Scholar
  3. Bektaş T, Laporte G (2011) The pollution-routing problem. Transp Res Part B Methodol 45(8):1232–1250CrossRefGoogle Scholar
  4. Boland N, Charkhgard H, Savelsbergh M (2015) A criterion space search algorithm for biobjective integer programming—the balanced box method. INFORMS J Comput 27(4):735–754MathSciNetCrossRefMATHGoogle Scholar
  5. Chankong V, Haimes YY (1983) Multiobjective decision making: theory and methodology. Elsevier Science, New YorkMATHGoogle Scholar
  6. Demir E, Bektaş T, Laporte G (2014) The bi-objective pollution-routing problem. Eur J Oper Res 232(3):464–478MathSciNetCrossRefMATHGoogle Scholar
  7. Demir E, Huang Y, Scholts S, Van Woensel T (2015) A selected review on the negative externalities of the freight transportation: modeling and pricing. Transp Res Part E Log Transp Rev 77:95–114CrossRefGoogle Scholar
  8. Dolan ED, Moré JJ (2002) Benchmarking optimization software with performance profiles. Math Progr 91(2):201–213MathSciNetCrossRefMATHGoogle Scholar
  9. Doppstadt C, Koberstein A, Vigo D (2016) The hybrid electric vehicle-traveling salesman problem. Eur J Oper Res 253(3):825–842MathSciNetCrossRefMATHGoogle Scholar
  10. Ehrgott M, Gandibleux X (2000) A survey and annotated bibliography of multiobjective combinatorial optimization. OR Spektrum 22(4):425–460MathSciNetCrossRefMATHGoogle Scholar
  11. European Commission (2011) White paper Roadmap to a single european transport area: towards a competitive and resource efficient transport systemGoogle Scholar
  12. Franceschetti A, Honhon D, Van Woensel T, Bektaş T, Laporte G (2013) The time-dependent pollution-routing problem. Transp Res Part B Methodol 56:265–293CrossRefGoogle Scholar
  13. Gandibleux X, Beugnies F, Randriamasy S (2006) Martins’ algorithm revisited for multi-objective shortest path problems with a maxmin cost function. 4OR 4(1):47–59MathSciNetCrossRefMATHGoogle Scholar
  14. Garaix T, Artigues C, Feillet D, Josselin D (2010) Vehicle routing problems with alternative paths: an application to on-demand transportation. Eur J Oper Res 204(1):62–75MathSciNetCrossRefMATHGoogle Scholar
  15. Gendreau M, Laporte G, Semet F (1998) A tabu search heuristic for the undirected selective travelling salesman problem. Eur J Oper Res 106(2):539–545CrossRefMATHGoogle Scholar
  16. Golden BL, Levy L, Vohra R (1987) The orienteering problem. Naval Res Logist 34(3):307–318CrossRefMATHGoogle Scholar
  17. Gutin G, Punnen AP (2006) The traveling salesman problem and its variations, vol 12. Springer, BerlinMATHGoogle Scholar
  18. Hamacher HW, Pedersen CR, Ruzika S (2007) Finding representative systems for discrete bicriterion optimization problems. Oper Res Lett 35(3):336–344MathSciNetCrossRefMATHGoogle Scholar
  19. Hansen MP, Jaszkiewicz A (1998) Evaluating the quality of approximations to the non-dominated set. IMM, Department of Mathematical Modelling, Technical Universityof DenmarkGoogle Scholar
  20. Hansen P (1980) Bicriterion path problems. In: Fandel G, Gal T (eds) Multiple criteria decision making theory and application. Lecture notes in economics and mathematical systems, vol 177. Springer, Berlin, Heidelberg, pp 109–127CrossRefGoogle Scholar
  21. Hiermann G, Puchinger J, Ropke S, Hartl RF (2016) The electric fleet size and mix vehicle routing problem with time windows and recharging stations. Eur J Oper Res 252(3):995–1018MathSciNetCrossRefMATHGoogle Scholar
  22. Kara I, Kara BY, Yetis MK (2007) Energy minimizing vehicle routing problem. In: Dress A, Xu Y, Zhu B (eds) Proceedings of the combinatorial optimization and applications: first international conference, COCOA 2007, Xi’an, China, August 14–16, 2007. Springer, Berlin, pp 62–71Google Scholar
  23. London Department of Transport (2014) Quiet deliveries good practice guidance: key principles and processes for retailers. Accessed 20 Mar 2017
  24. Martins EQV (1984) On a multicriteria shortest path problem. Eur J Oper Res 16(2):236–245MathSciNetCrossRefMATHGoogle Scholar
  25. Miller C, Zemlin R, Tucker A (1960) Integer programming formulation of traveling salesman problems. J ACM (JACM) 7(4):326–329MathSciNetCrossRefMATHGoogle Scholar
  26. Palmer A (2007) The development of an integrated routing and carbon dioxide emissions model for goods vehicles. Ph.D. thesis, Cranfield University, School of ManagementGoogle Scholar
  27. Roberti R, Wen M (2016) The electric traveling salesman problem with time windows. Transp Res Part E Logist Transp Rev 89:32–52CrossRefGoogle Scholar
  28. Serafini P (1987) Some considerations about computational complexity for multi objective combinatorial problems. In: Jahn J, Krabs W (eds) Recent advances and historical development of vector optimization. Lecture notes in economics and mathematical systems, vol 294. Springer, Berlin, Heidelberg, pp 222–232Google Scholar
  29. Suzuki Y (2011) A new truck-routing approach for reducing fuel consumption and pollutants emission. Transp Res Part D Transp Environ 16(1):73–77CrossRefGoogle Scholar
  30. Suzuki Y (2016) A dual-objective metaheuristic approach to solve practical pollution routing problem. Int J Prod Econ 176:143–153CrossRefGoogle Scholar
  31. Tadei R, Perboli G, Perfetti F (2017) The multi-path traveling salesman problem with stochastic travel costs. EURO J Transp Logist 6:1–21CrossRefGoogle Scholar
  32. Tricoire F (2012) Proute. GitHub
  33. Tricoire F, Romauch M, Doerner KF, Hartl RF (2010) Heuristics for the multi-period orienteering problem with multiple time windows. Comput Oper Res 37(2):351–367MathSciNetCrossRefMATHGoogle Scholar
  34. Tricoire F, Parragh SN (2017) Investing in logistics facilities today to reduce routing emissions tomorrow. Transp Res Part B Methodol 103:56–67CrossRefGoogle Scholar
  35. United Nations Department of Economic and Social Affairs (2014) World urbanization prospects: the 2014 revision. ST/ESA/SERA/352Google Scholar
  36. United Parcel Service of America Inc (2016) Pulse of the online shopper-A UPS white paperGoogle Scholar
  37. VIENNAat (2012) City-Maut für Wien wieder im Gespräch. Accessed 20 Mar 2017
  38. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evolut Comput 3(4):257–271CrossRefGoogle Scholar
  39. Zitzler E, Thiele L, Laumanns M, Fonseca CM, Da Fonseca VG (2003) Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans Evolut Comput 7(2):117–132CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Business AdministrationUniversity of ViennaViennaAustria
  2. 2.Institute for Production and Logistics ManagementJohannes Kepler UniversityLinzAustria
  3. 3.Christian Doppler Laboratory for Efficient Intermodal Transport OperationsViennaAustria

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