Abstract
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.
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References
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)
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)
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)
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)
Bektas, T., Crainic, T.G., Woensel, T.V.: From managing urban freight to smart city logistics networks, August 2015
Ben Letaifa, S.: Letaifa: How to strategize smart cities: Revealing the smart model. J. Bus. Res. 68(7), 1414–1419 (2015)
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)
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)
Bianchi, L., Dorigo, M., Gambardella, L., Gutjahr, W.: A survey on metaheuristics for stochastic combinatorial optimization. Nat. Comput. 8, 239–287 (2009)
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)
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)
Caceres-Cruz, J., Arias, P., Guimarans, D., Riera, D., Juan, A.A.: Rich vehicle routing problem. ACM Comput. Surv. 47(2), 1–28 (2014)
Caragliu, A., Del Bo, C., Nijkamp, P.: Smart cities in Europe. J. Urban Technol. 18(2), 65–82 (2011)
Cattaruzza, D., Absi, N., Feillet, D., González-Feliu, J.: Vehicle routing problems for city logistics. EURO J. Transp. Logistics 1, 1–29 (2015)
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)
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)
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)
Crainic, T., Ricciardi, N., Storchi, G.: Models for evaluating and planning city logistics systems. Transp. Sci. 43(4), 432–454 (2009)
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)
Crossette, B., Kollodge, R., Puchalik, R., Chalijub, M.: The state of world population 2011, United Nations Population Fund, pp. 1–132 (2011)
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)
Drexl, M., Schneider, M.: A survey of variants and extensions of the location-routing problem. Eur. J. Oper. Res. 241(2), 283–308 (2015)
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)
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)
European Commission, Cities of tomorrow - Challanges, visions, ways forward. Publications Office of the European Union (2011)
Agency, E.E.: Eea draws the first map of europe’s noise exposure (2009). http://www.eea.europa.eu/media/newsreleases/eea-draws-the-first-map-of-europe2019s-noise-exposure
Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pilcher-Milanovic, N., Meijers, E.: Smart cities - ranking of european medium sized cities (2007). http://www.smart-cities.eu/download/smart_cities_final_report.pdf
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)
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)
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)
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)
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)
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)
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)
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)
Kumar, S.N.: A survey on the vehicle routing problem and its variants. Intell. Inf. Manage. 04(03), 66–74 (2012)
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)
Macal, C.M., North, M.: Tutorial on agent-based modelling and simulation. J. Simul. 4, 151–162 (2010)
Mancini, S.: Multi-echelon distribution systems in city logistics. European Transport - Trasporti Europei 54, 1–24 (2013)
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)
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)
Nowicka, K.: Smart city logistics on cloud computing model. Procedia Soc. Behav. Sci. 151, 266–281 (2014)
Othman, M., Gouw, G.J., Bhuiyan, N.: Workforce scheduling : A new model incorporating human factors 5(2), 259–284 (2013)
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)
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)
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)
Sood, A., Sharma, V.: A study of behavioural perspective of operations. Procedia Soc. Behav. Sci. 189, 229–233 (2015)
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)
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)
Tamagawa, D., Taniguchi, E., Yamada, T.: Evaluating city logistics measures using a multi-agent model. Procedia Soc. Behav. Sci. 2(3), 6002–6012 (2010)
Taniguchi, E., Thompson, E., Yamada, T., van Duin, J., Logistics, C.: Network Modelling and Intelligent Transport Systems. Pergamon, Oxford (2001)
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)
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)
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)
Tokar, T.: Behavioral research in logistics and supply chain management. Int. J. Bus. Manage. 21(1), 89–103 (2010)
United States Environmental Protection Agency. Greenhouse gas emissions 1990–2013 (2013). http://www3.epa.gov/otaq/climate/documents/420f15032.pdf
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)
Acknowledgments
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.
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Gruler, A., de Armas, J., Juan, A.A. (2016). Behavioral Factors in City Logistics from an Operations Research Perspective. In: Alba, E., Chicano, F., Luque, G. (eds) Smart Cities. Smart-CT 2016. Lecture Notes in Computer Science(), vol 9704. Springer, Cham. https://doi.org/10.1007/978-3-319-39595-1_4
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