Behavioral Factors in City Logistics from an Operations Research Perspective

  • Aljoscha GrulerEmail author
  • Jesica de Armas
  • Angel A. Juan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9704)


In the face of sharp urbanization around the world, metropolitan areas have started different initiatives and projects to make cities more efficient and sustainable. Hereby logistics and transportation activities have a major impact in the development of so called ‘Smart Cities’. By addressing complex decision making problems through simulation and optimization, the Operations Research community has contributed to the development of sustainable city logistic systems. While technical and structural problems have been extensively discussed in the literature, many models neglect the importance of behavioral issues arising from risk aversion, stakeholder interaction and human factors that play an important role in the consolidation and optimization of logistical activities. This paper reviews existing work considering behavioral factors from an OR perspective. Simulation and optimization models to major problem settings in City Logistics are discussed and methodologies to conquer real-life urban L&T challenges are presented.


Smart Cities City Logistics Operations Research Behavioral research Simulation-optimization 



This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P and TRA2015-71883-REDT), and FEDER. Likewise, we want to acknowledge the support received by the Department of Universities, Research & Information Society of the Catalan Government (2014-CTP-00001) and the doctoral grant of the UOC.


  1. 1.
    Albert, G., Toledo, T., Ben-Zion, U.: The role of personality factors in repeated route choice behavior: Behavioral economics perspective. Europ. Transp. 48(48), 47–59 (2011)Google Scholar
  2. 2.
    Anand, N., Quak, H., van Duin, R., Tavasszy, L.: City logistics modeling efforts: Trends and gaps - a review. Procedia Soc. Behav. Sci. 39, 101–115 (2012)CrossRefGoogle Scholar
  3. 3.
    Awasthi, A., Chauhan, S.S., Goyal, S.K.: A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty. Math. Comput. Model. 53(1–2), 98–109 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Badin, F., Le Berr, F., Briki, H., Dabadie, J.-C., Petit, M., Magand, S., Condemine, E.: Evaluation of evs energy consumption influencing factors, driving conditions, auxiliaries use, driver’s aggressiveness. In: World Electric Vehicle Symposium and Exhibition (EVS27), pp. 1–12 (2013)Google Scholar
  5. 5.
    Bektas, T., Crainic, T.G., Woensel, T.V.: From managing urban freight to smart city logistics networks, August 2015Google Scholar
  6. 6.
    Ben Letaifa, S.: Letaifa: How to strategize smart cities: Revealing the smart model. J. Bus. Res. 68(7), 1414–1419 (2015)CrossRefGoogle Scholar
  7. 7.
    Bendoly, E., Donohue, K., Schultz, K.: Behavior in operations management: assessing recent findings and revisiting old assumptions. J. Oper. Manage. 24(6), 737–752 (2006)CrossRefGoogle Scholar
  8. 8.
    Benjelloun, A., Crainic, T.: Trends, challenges, and perspectives in city logistics. In: Proceedings of the Transportation and Land Use Interaction Conference, no. 4, pp. 269–284 (2009)Google Scholar
  9. 9.
    Bianchi, L., Dorigo, M., Gambardella, L., Gutjahr, W.: A survey on metaheuristics for stochastic combinatorial optimization. Nat. Comput. 8, 239–287 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Bourdreau, J.W., Hopp, W., McClain, J., Thomas, L.J.: On the interface between operations and human resources management. Manuf. Serv. Oper. Manage. 5(2), 179–202 (2003)Google Scholar
  11. 11.
    Boussier, J., Cucu, T., Ion, L., Estrailler, P., Breuil, D.: Goods distribution with electric vans in cities: towards and agent-based simulation. World Electric Veh. J. 3, 1–9 (2009)Google Scholar
  12. 12.
    Caceres-Cruz, J., Arias, P., Guimarans, D., Riera, D., Juan, A.A.: Rich vehicle routing problem. ACM Comput. Surv. 47(2), 1–28 (2014)CrossRefGoogle Scholar
  13. 13.
    Caragliu, A., Del Bo, C., Nijkamp, P.: Smart cities in Europe. J. Urban Technol. 18(2), 65–82 (2011)CrossRefGoogle Scholar
  14. 14.
    Cattaruzza, D., Absi, N., Feillet, D., González-Feliu, J.: Vehicle routing problems for city logistics. EURO J. Transp. Logistics 1, 1–29 (2015)Google Scholar
  15. 15.
    Cocchia, A.: Smart and digital city: A systematic literature review.In: Dameri, R.P., Rosenthal-Sabroux, C. (eds.) Smart City - How to Create Public and Economic Value with High Technology in Urban Space, pp. 13–43. Springer International Publishing, Switzerland (2014)Google Scholar
  16. 16.
    Contardo, C., Crainic, T., Hemmelmayr, V.: Lower and upper bounds for the two-echelon capacitated location routing problem. Comput. Oper. Res. 39, 3215–3228 (2012)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Crainic, T., Perboli, G., Mancini, S., Tadei, R.: Two-echelon vehicle routing problem: a satellite location analysis. Procedia Soc. Behav. Sci. 2(3), 5944–5955 (2010)CrossRefGoogle Scholar
  18. 18.
    Crainic, T., Ricciardi, N., Storchi, G.: Models for evaluating and planning city logistics systems. Transp. Sci. 43(4), 432–454 (2009)CrossRefGoogle Scholar
  19. 19.
    Croson, R., Schultz, K., Siemsen, E., Yeo, M.L.: Behavioral operations: The state of the field. J. Oper. Manage. 31(1–2), 1–5 (2013)CrossRefGoogle Scholar
  20. 20.
    Crossette, B., Kollodge, R., Puchalik, R., Chalijub, M.: The state of world population 2011, United Nations Population Fund, pp. 1–132 (2011)Google Scholar
  21. 21.
    Danielis, R., Rotataris, L., Marcucci, E.: Urban freight policies and distribution channels: a discussion based on evidence from italian cities. European Transport/Trasporti Europei 46, 114–146 (2010)Google Scholar
  22. 22.
    Drexl, M., Schneider, M.: A survey of variants and extensions of the location-routing problem. Eur. J. Oper. Res. 241(2), 283–308 (2015)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Duin, R., van Kolck, A., Anand, N., Tavasszy, L., Taniguchi, E.: Towards an agent-based modelling approach for the evaluation of dynamic usage of urban distribution centres. In: Proceedings of the Seventh International Conference on City Logistics (2011)Google Scholar
  24. 24.
    Ehmke, J., Meisel, S., Mattfeld, D.: Floating car based travel times for city logistics. Transp. Res. Part C Emerg. Technol. 21(1), 338–352 (2012)CrossRefGoogle Scholar
  25. 25.
    European Commission, Cities of tomorrow - Challanges, visions, ways forward. Publications Office of the European Union (2011)Google Scholar
  26. 26.
    Agency, E.E.: Eea draws the first map of europe’s noise exposure (2009).
  27. 27.
    Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pilcher-Milanovic, N., Meijers, E.: Smart cities - ranking of european medium sized cities (2007).
  28. 28.
    Hämläinen, R.P., Luoma, J., Saarinen, E.: On the importance of behavioral operational research: The case of understanding and communicating about dynamic systems. Eur. J. Oper. Res. 228(3), 623–634 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    He, H., Cheng, H.: Analyzing key influence factors of city logistics development using the fuzzy decision making trial and evaluation laboratory (dematel) method. Afr. J. Bus. Manage. 6(45), 281–293 (2012)Google Scholar
  30. 30.
    Herazo-Padilla, N., Montoya-Torres, J., Isaza, S., Alvarado, J.: Simulation-optimization approach for the stochastic location-routing problem. J. Simul. 9(4), 296–311 (2015)CrossRefGoogle Scholar
  31. 31.
    Juan, A.A., Barrios, B., Vallada, E., Riera, D., Jorba, J.: Sim-esp: A simheuristic algorithm for solving the permutation flow-shop problem with stochastic processing times. Simul. Model. Pract. Theory 46, 101–117 (2014)CrossRefGoogle Scholar
  32. 32.
    Juan, A.A., Faulin, J., Grasman, S., Riera, D., Marull, J., Mendez, C.: Using safety stocks and simulation to solve the vehicle routing problem with stochastic demands. Transp. Res. Part C Emerg. Technol. 19(5), 751–765 (2011)CrossRefGoogle Scholar
  33. 33.
    Juan, A.A., Goentzel, J., Bektaş, T.: Routing fleets with multiple driving ranges: Is it possible to use greener fleet configurations? Appl. Soft Comput. 21, 84–94 (2014)CrossRefGoogle Scholar
  34. 34.
    Juan, A.A., Mendez, C., Faulin, J., Armas, J., Grasman, S.: Electric vehicles in logistics and transportation: a survey on emerging environmental, strategic, and operational challenges. Energies 9, 86 (2016)CrossRefGoogle Scholar
  35. 35.
    Juan, A.A., Faulin, J., Grasman, S.E., Rabe, M., Figueira, G.: A review ofsimheuristics: Extending metaheuristics to deal with stochastic combinatorialoptimization problems. Oper. Res. Perspect. 2, 62–72 (2015)CrossRefGoogle Scholar
  36. 36.
    Kumar, S.N.: A survey on the vehicle routing problem and its variants. Intell. Inf. Manage. 04(03), 66–74 (2012)Google Scholar
  37. 37.
    Lebeau, P., Macharis, C., Van Mierlo, J., Maes, G.: Implementing electric vehicles in urban distribution: A discrete event simulation. In: Electric Vehicle Symposium and Exhibition (EVS27), 2013 World (2013)Google Scholar
  38. 38.
    Macal, C.M., North, M.: Tutorial on agent-based modelling and simulation. J. Simul. 4, 151–162 (2010)CrossRefGoogle Scholar
  39. 39.
    Mancini, S.: Multi-echelon distribution systems in city logistics. European Transport - Trasporti Europei 54, 1–24 (2013)Google Scholar
  40. 40.
    Muñuzuri, J., Grosso, R., Cortés, P., Guadix, J.: Estimating the extra costs imposed on delivery vehicles using access time windows in a city. Comput. Environ. Urban Syst. 41, 262–275 (2013)CrossRefGoogle Scholar
  41. 41.
    Nguyen, V.P., Prins, C., Prodhon, C.: Solving the two-echelon location routing problem by a grasp reinforced by a learning process and path relinking. Eur. J. Oper. Res. 216, 113–126 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Nowicka, K.: Smart city logistics on cloud computing model. Procedia Soc. Behav. Sci. 151, 266–281 (2014)CrossRefGoogle Scholar
  43. 43.
    Othman, M., Gouw, G.J., Bhuiyan, N.: Workforce scheduling : A new model incorporating human factors 5(2), 259–284 (2013)Google Scholar
  44. 44.
    Quak, H., de Koster, M.: Delivering goods in urban areas: how to deal with urban policy restrictions and the environment. Transp. Sci. 43(2), 211–227 (2009)CrossRefGoogle Scholar
  45. 45.
    Qureshi, A., Taniguchi, E., Yamada, T.: A microsimulation based analysis of exact solution of dynamic vehicle routing with soft time windows. Procedia Soc. Behav. Sci. 39, 205–216 (2011)CrossRefGoogle Scholar
  46. 46.
    Schwengerer, M., Pirkwieser, S., Raidl, G.R.: A variable neighborhood search approach for the two-echelon location-routing problem. In: Hao, J.-K., Middendorf, M. (eds.) EvoCOP 2012. LNCS, vol. 7245, pp. 13–24. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  47. 47.
    Sood, A., Sharma, V.: A study of behavioural perspective of operations. Procedia Soc. Behav. Sci. 189, 229–233 (2015)CrossRefGoogle Scholar
  48. 48.
    Storey, J., Emberson, C., Godsell, J., Harrison, A.: Supply chain management: theory, practice and future challenges. Inte. J. Oper. Prod. Manage. 26(7), 754–774 (2006)CrossRefGoogle Scholar
  49. 49.
    Sweeny, E.: The people dimension in logistics and supply chain management research and practice: its role and importance. In: Passaro, R., Thomas, A. (eds.) Supply Chain Management: Perspectives, Issues and Cases, pp. 73–82. McGraw-Hill, Milan (2013)Google Scholar
  50. 50.
    Tamagawa, D., Taniguchi, E., Yamada, T.: Evaluating city logistics measures using a multi-agent model. Procedia Soc. Behav. Sci. 2(3), 6002–6012 (2010)CrossRefGoogle Scholar
  51. 51.
    Taniguchi, E., Thompson, E., Yamada, T., van Duin, J., Logistics, C.: Network Modelling and Intelligent Transport Systems. Pergamon, Oxford (2001)CrossRefGoogle Scholar
  52. 52.
    Taniguchi, E., Yamada, T., Okamoto, M.: Multi-agent modelling for evaluating dynamic vehicle routing and scheduling systems. J. Eastern Asia Soc. Transp. Stud. 7, 933–948 (2007)Google Scholar
  53. 53.
    Taniguchi, E., Thompson, R.G., Yamada, T.: Emerging techniques for enhancing the practical application of city logistics models. Procedia Soc. Behav. Sci. 39, 3–18 (2012)CrossRefGoogle Scholar
  54. 54.
    Teo, J.S., Taniguchi, E., Qureshi, A.G.: Evaluating city logistics measure in e-commerce with multiagent systems. Procedia Soc. Behav. Sci. 39, 349–359 (2012)CrossRefGoogle Scholar
  55. 55.
    Tokar, T.: Behavioral research in logistics and supply chain management. Int. J. Bus. Manage. 21(1), 89–103 (2010)Google Scholar
  56. 56.
    United States Environmental Protection Agency. Greenhouse gas emissions 1990–2013 (2013).
  57. 57.
    Wangapisit, O., Taniguchi, E., Teo, J.S., Qureshi, A.G.: Multi-agent systems modelling for evaluating joint delivery systems. Procedia Soc. Behav. Sci. 125, 472–483 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Aljoscha Gruler
    • 1
    Email author
  • Jesica de Armas
    • 1
  • Angel A. Juan
    • 1
  1. 1.Department of Computer Science - IN3Open University of CataloniaCastelldefelsSpain

Personalised recommendations