1,546
Views
19
CrossRef citations to date
0
Altmetric
Articles

Vehicle routing problem and driver behaviour: a review and framework for analysis

&
Pages 590-611 | Received 15 Jun 2016, Accepted 10 Dec 2016, Published online: 30 Dec 2016

References

  • Anand, N., van Duin, R., Quak, H., & Tavasszy, L. (2015). Relevance of city logistics modelling efforts: A review. Transport Reviews, 35(6), 701–719. doi: 10.1080/01441647.2015.1052112
  • Bektaş, T., & Laporte, G. (2011). The pollution-routing problem. Transportation Research Part B: Methodological, 45(8), 1232–1250. doi: 10.1016/j.trb.2011.02.004
  • Bella, F. (2014). Effects of combined curves on driver’s speed behavior: Driving simulator study. Transportation Research Procedia, 3, 100–108. doi: 10.1016/j.trpro.2014.10.095
  • Benjamin, A. M., & Beasley, J. E. (2010). Metaheuristics for the waste collection vehicle routing problem with time windows, driver rest period and multiple disposal facilities. Computers & Operations Research, 37(12), 2270–2280. doi: 10.1016/j.cor.2010.03.019
  • Chen, G., Jia, H., Li, Z., Duan, M., & Mi, X. (2013). An analysis of driver’s choice based on information of traffic accidents. Procedia-Social and Behavioral Sciences, 96, 1055–1062. doi: 10.1016/j.sbspro.2013.08.121
  • Contardo, C., & Martinelli, R. (2014). A new exact algorithm for the multi-depot vehicle routing problem under capacity and route length constraints. Discrete Optimization, 12, 129–146. doi: 10.1016/j.disopt.2014.03.001
  • Crainic, T. G., Ricciardi, N., & Storchi, G. (2009). Models for evaluating and planning city logistics systems. Transportation Science, 43(4), 432–454. doi: 10.1287/trsc.1090.0279
  • Crevier, B., Cordeau, J. F., & Laporte, G. (2007). The multi-depot vehicle routing problem with inter-depot routes. European Journal of Operational Research, 176(2), 756–773. doi: 10.1016/j.ejor.2005.08.015
  • Cuda, R., Guastaroba, G., & Speranza, M. G. (2015). A survey on two-echelon routing problems. Computers & Operations Research, 55, 185–199. doi: 10.1016/j.cor.2014.06.008
  • Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management Science, 6(1), 80–91. doi: 10.1287/mnsc.6.1.80
  • Demir, E., Bektaş, T., & Laporte, G. (2012). An adaptive large neighborhood search heuristic for the pollution-routing problem. European Journal of Operational Research, 223(2), 346–359. doi: 10.1016/j.ejor.2012.06.044
  • Demir, E., Bektaş, T., & Laporte, G. (2014). The bi-objective pollution-routing problem. European Journal of Operational Research, 232(3), 464–478. doi: 10.1016/j.ejor.2013.08.002
  • Desrochers, M., Desrosiers, J., & Solomon, M. (1992). A new optimization algorithm for the vehicle routing problem with time windows. Operations Research, 40(2), 342–354. doi: 10.1287/opre.40.2.342
  • Dia, H. (2002). An agent-based approach to modelling driver route choice behaviour under the influence of real-time information. Transportation Research Part C: Emerging Technologies, 10(5), 331–349. doi: 10.1016/S0968-090X(02)00025-6
  • Donati, A. V., Montemanni, R., Casagrande, N., Rizzoli, A. E., & Gambardella, L. M. (2008). Time dependent vehicle routing problem with a multi ant colony system. European Journal of Operational Research, 185(3), 1174–1191. doi: 10.1016/j.ejor.2006.06.047
  • Duhamel, C., Potvin, J. Y., & Rousseau, J. M. (1997). A tabu search heuristic for the vehicle routing problem with backhauls and time windows. Transportation Science, 31(1), 49–59. doi: 10.1287/trsc.31.1.49
  • Ehmke, J. F., Campbell, A. M., & Thomas, B. W. (2016). Vehicle routing to minimize time-dependent emissions in urban areas. European Journal of Operational Research, 251(2), 478–494. doi: 10.1016/j.ejor.2015.11.034
  • Erdoğan, S., & Miller-Hooks, E. (2012). A green vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 48(1), 100–114. doi: 10.1016/j.tre.2011.08.001
  • Erke, A., Sagberg, F., & Hagman, R. (2007). Effects of route guidance variable message signs (VMS) on driver behaviour. Transportation Research Part F: Traffic Psychology and Behaviour, 10(6), 447–457. doi: 10.1016/j.trf.2007.03.003
  • Errico, F., Desaulniers, G., Gendreau, M., Rei, W., & Rousseau, L. M. (2016). A priori optimization with recourse for the vehicle routing problem with hard time windows and stochastic service times. European Journal of Operational Research, 249(1), 55–66. doi: 10.1016/j.ejor.2015.07.027
  • Franceschetti, A., Honhon, D., Van Woensel, T., Bektaş, T., & Laporte, G. (2013). The time-dependent pollution-routing problem. Transportation Research Part B: Methodological, 56, 265–293. doi: 10.1016/j.trb.2013.08.008
  • Gajanand, M. S., & Narendran, T. T. (2013). Green route planning to reduce the environmental impact of distribution. International Journal of Logistics Research and Applications, 16(5), 410–432. doi: 10.1080/13675567.2013.831400
  • Gao, S., Frejinger, E., & Ben-Akiva, M. (2010). Adaptive route choices in risky traffic networks: A prospect theory approach. Transportation Research Part C: Emerging Technologies, 18(5), 727–740. doi: 10.1016/j.trc.2009.08.001
  • Gastaldi, M., Rossi, R., & Gecchele, G. (2014). Effects of driver task-related fatigue on driving performance. Procedia-Social and Behavioral Sciences, 111, 955–964. doi: 10.1016/j.sbspro.2014.01.130
  • Gendreau, M., Ghiani, G., & Guerriero, E. (2015). Time-dependent routing problems: A review. Computers & Operations Research, 64, 189–197. doi: 10.1016/j.cor.2015.06.001
  • Güner, A. R., Murat, A., & Chinnam, R. B. (2012). Dynamic routing under recurrent and non-recurrent congestion using real-time its information. Computers & Operations Research, 39(2), 358–373. doi: 10.1016/j.cor.2011.04.012
  • Haglund, M., & Åberg, L. (2002). Stability in drivers’ speed choice. Transportation Research Part F: Traffic Psychology and Behaviour, 5(3), 177–188. doi: 10.1016/S1369-8478(02)00016-5
  • Hemmelmayr, V. C., Cordeau, J. F., & Crainic, T. G. (2012). An adaptive large neighborhood search heuristic for two-echelon vehicle routing problems arising in city logistics. Computers & Operations Research, 39(12), 3215–3228. doi: 10.1016/j.cor.2012.04.007
  • Hsu, C. I., Hung, S. F., & Li, H. C. (2007). Vehicle routing problem with time-windows for perishable food delivery. Journal of Food Engineering, 80(2), 465–475. doi: 10.1016/j.jfoodeng.2006.05.029
  • Ichoua, S., Gendreau, M., & Potvin, J. Y. (2003). Vehicle dispatching with time-dependent travel times. European Journal of Operational Research, 144(2), 379–396. doi: 10.1016/S0377-2217(02)00147-9
  • Kara, I., Kara, B. Y., & Yetis, M. K. (2007). Energy minimizing vehicle routing problem. In A. Dress, Y. Xu, & B. Zhu (Eds.), Combinatorial optimization and applications (pp. 62–71). Berlin: Springer.
  • Koç, Ç., Bektaş, T., Jabali, O., & Laporte, G. (2014). The fleet size and mix pollution-routing problem. Transportation Research Part B: Methodological, 70, 239–254. doi: 10.1016/j.trb.2014.09.008
  • Kramer, R., Subramanian, A., Vidal, T., & Lucídio dos Anjos, F. C. (2015). A matheuristic approach for the pollution-routing problem. European Journal of Operational Research, 243(2), 523–539. doi: 10.1016/j.ejor.2014.12.009
  • Kuo, Y. (2010). Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem. Computers & Industrial Engineering, 59(1), 157–165. doi: 10.1016/j.cie.2010.03.012
  • Kusakabe, T., Sharyo, T., & Asakura, Y. (2012). Effects of traffic incident information on drivers’ route choice behaviour in urban expressway network. Procedia-Social and Behavioral Sciences, 54, 179–188. doi: 10.1016/j.sbspro.2012.09.737
  • Kwon, Y. J., Choi, Y. J., & Lee, D. H. (2013). Heterogeneous fixed fleet vehicle routing considering carbon emission. Transportation Research Part D: Transport and Environment, 23, 81–89. doi: 10.1016/j.trd.2013.04.001
  • Lau, H. C., Sim, M., & Teo, K. M. (2003). Vehicle routing problem with time windows and a limited number of vehicles. European Journal of Operational Research, 148(3), 559–569. doi: 10.1016/S0377-2217(02)00363-6
  • Lecluyse, C., Van Woensel, T., & Peremans, H. (2009). Vehicle routing with stochastic time-dependent travel times. 4OR, 7(4), 363–377. doi: 10.1007/s10288-009-0097-9
  • Li, N., Jain, J. J., & Busso, C. (2013). Modeling of driver behavior in real world scenarios using multiple noninvasive sensors. IEEE Transactions on Multimedia, 15(5), 1213–1225. doi: 10.1109/TMM.2013.2241416
  • Li, Z., & Hensher, D. (2011). Prospect theoretic contributions in understanding traveller behaviour: A review and some comments. Transport Reviews, 31(1), 97–115. doi: 10.1080/01441647.2010.498589
  • Lin, C., Choy, K. L., Ho, G. T., Chung, S. H., & Lam, H. Y. (2014). Survey of green vehicle routing problem: Past and future trends. Expert Systems with Applications, 41(4), 1118–1138. doi: 10.1016/j.eswa.2013.07.107
  • Ma, Z., Shao, C., Song, Y., & Chen, J. (2014). Driver response to information provided by variable message signs in Beijing. Transportation Research Part F: Traffic Psychology and Behaviour, 26, 199–209. doi: 10.1016/j.trf.2014.07.006
  • Maden, W., Eglese, R., & Black, D. (2010). Vehicle routing and scheduling with time-varying data: A case study. Journal of the Operational Research Society, 61(3), 515–522. doi: 10.1057/jors.2009.116
  • Malandraki, C., & Daskin, M. S. (1992). Time dependent vehicle routing problems: Formulations, properties and heuristic algorithms. Transportation Science, 26(3), 185–200. doi: 10.1287/trsc.26.3.185
  • May, J. F., & Baldwin, C. L. (2009). Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies. Transportation Research Part F: Traffic Psychology and Behaviour, 12(3), 218–224. doi: 10.1016/j.trf.2008.11.005
  • Michon, J. A. (1985). A critical view of driver behavior models: What do we know, what should we do? In L. Evans & R. C. Schwing (Eds.), Human behavior and traffic safety (pp. 485–524). Boston: Springer.
  • Min, H., & Melachrinoudis, E. (2016). A model-based decision support system for solving vehicle routing and driver scheduling problems under hours of service regulations. International Journal of Logistics Research and Applications, 19(4), 256–277. doi: 10.1080/13675567.2015.1075475
  • Montoya-Torres, J. R., Franco, J. L., Isaza, S. N., Jiménez, H. F., & Herazo-Padilla, N. (2015). A literature review on the vehicle routing problem with multiple depots. Computers & Industrial Engineering, 79, 115–129. doi: 10.1016/j.cie.2014.10.029
  • Noland, R. B., & Polak, J. W. (2002). Travel time variability: A review of theoretical and empirical issues. Transport Reviews, 22(1), 39–54. doi: 10.1080/01441640010022456
  • Pahlavani, P., & Delavar, M. R. (2014). Multi-criteria route planning based on a driver’s preferences in multi-criteria route selection. Transportation Research Part C: Emerging Technologies, 40, 14–35. doi: 10.1016/j.trc.2014.01.001
  • Park, J., & Kim, B. I. (2010). The school bus routing problem: A review. European Journal of Operational Research, 202(2), 311–319. doi: 10.1016/j.ejor.2009.05.017
  • Perboli, G., Tadei, R., & Vigo, D. (2011). The two-echelon capacitated vehicle routing problem: Models and math-based heuristics. Transportation Science, 45(3), 364–380. doi: 10.1287/trsc.1110.0368
  • Potvin, J. Y., Xu, Y., & Benyahia, I. (2006). Vehicle routing and scheduling with dynamic travel times. Computers & Operations Research, 33(4), 1129–1137. doi: 10.1016/j.cor.2004.09.015
  • Qian, J., & Eglese, R. (2016). Fuel emissions optimization in vehicle routing problems with time-varying speeds. European Journal of Operational Research, 248(3), 840–848. doi: 10.1016/j.ejor.2015.09.009
  • Salhi, S., Imran, A., & Wassan, N. A. (2014). The multi-depot vehicle routing problem with heterogeneous vehicle fleet: Formulation and a variable neighborhood search implementation. Computers & Operations Research, 52, 315–325. doi: 10.1016/j.cor.2013.05.011
  • Savelsbergh, M. W. (1992). The vehicle routing problem with time windows: Minimizing route duration. ORSA Journal on Computing, 4(2), 146–154. doi: 10.1287/ijoc.4.2.146
  • Soysal, M., Bloemhof-Ruwaard, J. M., & Bektaş, T. (2015). The time-dependent two-echelon capacitated vehicle routing problem with environmental considerations. International Journal of Production Economics, 164, 366–378. doi: 10.1016/j.ijpe.2014.11.016
  • Summala, H., & Mikkola, T. (1994). Fatal accidents among car and truck drivers: Effects of fatigue, age, and alcohol consumption. Human Factors: The Journal of the Human Factors and Ergonomics Society, 36(2), 315–326.
  • Taillard, É., Badeau, P., Gendreau, M., Guertin, F., & Potvin, J. Y. (1997). A tabu search heuristic for the vehicle routing problem with soft time windows. Transportation Science, 31(2), 170–186. doi: 10.1287/trsc.31.2.170
  • Taş, D., Dellaert, N., Van Woensel, T., & De Kok, T. (2013). Vehicle routing problem with stochastic travel times including soft time windows and service costs. Computers & Operations Research, 40(1), 214–224. doi: 10.1016/j.cor.2012.06.008
  • Taş, D., Gendreau, M., Dellaert, N., Van Woensel, T., & de Kok, A. G. (2014). Vehicle routing with soft time windows and stochastic travel times: A column generation and branch-and-price solution approach. European Journal of Operational Research, 236(3), 789–799. doi: 10.1016/j.ejor.2013.05.024
  • Thiffault, P., & Bergeron, J. (2003). Monotony of road environment and driver fatigue: A simulator study. Accident Analysis & Prevention, 35(3), 381–391. doi: 10.1016/S0001-4575(02)00014-3
  • Tran, C., Doshi, A., & Trivedi, M. M. (2012). Modeling and prediction of driver behavior by foot gesture analysis. Computer Vision and Image Understanding, 116(3), 435–445. doi: 10.1016/j.cviu.2011.09.008
  • Van Woensel, T., Kerbache, L., Peremans, H., & Vandaele, N. (2008). Vehicle routing with dynamic travel times: A queueing approach. European Journal of Operational Research, 186(3), 990–1007. doi: 10.1016/j.ejor.2007.03.012
  • Wee, B. V., & Banister, D. (2016). How to write a literature review paper? Transport Reviews, 36(2), 278–288. doi: 10.1080/01441647.2015.1065456
  • Wen, L., & Eglese, R. (2015). Minimum cost VRP with time-dependent speed data and congestion charge. Computers & Operations Research, 56, 41–50. doi: 10.1016/j.cor.2014.10.007
  • Xu, H., Zhou, J., & Xu, W. (2011). A decision-making rule for modeling travelers’ route choice behavior based on cumulative prospect theory. Transportation Research Part C: Emerging Technologies, 19(2), 218–228. doi: 10.1016/j.trc.2010.05.009
  • Zhang, T., Chaovalitwongse, W. A., & Zhang, Y. (2012). Scatter search for the stochastic travel-time vehicle routing problem with simultaneous pick-ups and deliveries. Computers & Operations Research, 39(10), 2277–2290. doi: 10.1016/j.cor.2011.11.021
  • Zhou, L., Zhong, S., Ma, S., & Jia, N. (2014). Prospect theory based estimation of drivers’ risk attitudes in route choice behaviors. Accident Analysis & Prevention, 73, 1–11. doi: 10.1016/j.aap.2014.08.004

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.