System Engineering and Productivity

System Engineering and Productivity

Vehicle Routing Problem with Mobile Customers: Modeling Considering Uncertainty in Customer Location within a Geographical Area

Document Type : Research Paper

Authors
1 Corresponding author: Assistant Professor, Industrial Engineering Group, Golpayegan College of Engineering, Isfahan University of Technology, Golpayegan, Iran
2 Assistant Professor, Industrial Engineering Group, Golpayegan College of Engineering, Isfahan University of Technology, Golpayegan, Iran
3 Professor, Faculty of Industrial Engineering, Sharif University of Technology, Tehran, Iran
4 Associate Professor, Faculty of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Abstract
The customer with a Mobile and Stochastic location is a new approach on which Vehicle Routing Problems can be modeled. This is due to the fact that when a customer places an order and the service provider starts planning to deliver the order, some customers do not actually stay at That place, and many customers tend to move in different places. In classical VRP, the place of delivery is completely known, and the delivery of the order is planned for that specific location. If the customer is allowed to move around in a area after placing his order, the concept of location as it has been dealt with in classical VRP is practically challenged. Although this approach can involve increased customer satisfaction, it will make planning more complicated for the service provider, because it will face ambiguity and uncertainty in the customer location. This article attempts to explain this view of customer location and investigate VRP under conditions of customer location uncertainty.

Highlights

  • In this model, customers are defined by geographical regions rather than static points. 
  • Customers are allowed to traverse to any location within their designated service area. 
  • The service location can be any coordinate within the customer’s corresponding region.
  • The customer’s position at the time of delivery is a stochastic variable.

Keywords
Subjects

Copyright © Abolfazl Shafaei, Hossein Shams Shemirani, Mohammad Reza Akbari Jokar, Majid Rafiee

  

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

Birge, J. R., & Louveaux, F. (1997). Introduction to stochastic programming. New York, NY: Springer New York. https://doi.org/10.1007/0-387-22618-4_3
Dumez, D., Lehuédé, F., & Péton, O. (2021). A large neighborhood search approach to the vehicle routing problem with delivery options. Transportation Research Part B: Methodological144, 103-132. https://doi.org/10.1016/j.trb.2020.11.012
Gambella, C., Naoum-Sawaya, J., & Ghaddar, B. (2018). The vehicle routing problem with floating targets: Formulation and solution approaches. INFORMS Journal on Computing30(3), 554-569. https://doi.org/10.1287/ijoc.2017.0800
Gendreau, M., Laporte, G., & Séguin, R. (1996). Stochastic vehicle routing. European journal of operational research88(1), 3-12. https://doi.org/10.1016/0377-2217(95)00050-X
Grabenschweiger, J., Doerner, K. F., Hartl, R. F., & Savelsbergh, M. W. (2021). The vehicle routing problem with heterogeneous locker boxes. Central European Journal of Operations Research29(1), 113-142. https://doi.org/10.1007/s10100-020-00725-2
He, Y., Qi, M., Zhou, F., & Su, J. (2020). An effective metaheuristic for the last mile delivery with roaming delivery locations and stochastic travel times. Computers & Industrial Engineering145, 106513. https://doi.org/10.1016/j.cie.2020.106513
Hvattum, L. M., Løkketangen, A., & Laporte, G. (2006). Solving a dynamic and stochastic vehicle routing problem with a sample scenario hedging heuristic. Transportation Science40(4), 421-438. https://doi.org/10.1287/trsc.1060.0166
Jaillet, P. (1985). Probabilistic traveling salesman problems (Doctoral dissertation, Massachusetts Institute of Technology).
Laporte, G. (2009). Fifty years of vehicle routing. Transportation science43(4), 408-416. https://doi.org/10.1287/trsc.1090.0301
Laporte, G., Chapleau, S., Landry, P. E., & Mercure, H. (1989). An algorithm for the design of mailbox collection routes in urban areas. Transportation Research Part B: Methodological23(4), 271-280.
Lombard, A., Tamayo-Giraldo, S., & Fontane, F. (2018). Vehicle routing problem with roaming delivery locations and stochastic travel times (VRPRDL-S). Transportation research procedia30, 167-177. https://doi.org/10.1016/j.trpro.2018.09.019
Mardešić, N., Erdelić, T., Carić, T., & Đurasević, M. (2023). Review of stochastic dynamic vehicle routing in the evolving urban logistics environment. Mathematics12(1), 28. https://doi.org/10.3390/math12010028
Mendoza, J. E., Castanier, B., Guéret, C., Medaglia, A. L., & Velasco, N. (2011). Constructive heuristics for the multicompartment vehicle routing problem with stochastic demands. Transportation science45(3), 346-363. https://doi.org/10.1287/trsc.1100.0353
Moccia, L., Cordeau, J. F., & Laporte, G. (2012). An incremental tabu search heuristic for the generalized vehicle routing problem with time windows. Journal of the Operational Research Society63(2), 232-244. https://doi.org/10.1057/jors.2011.25
Murray, C. C., & Chu, A. G. (2015). The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery. Transportation Research Part C: Emerging Technologies54, 86-109. https://doi.org/10.1016/j.trc.2015.03.005
Özarık, S. S., Veelenturf, L. P., Van Woensel, T., & Laporte, G. (2021). Optimizing e-commerce last-mile vehicle routing and scheduling under uncertain customer presence. Transportation Research Part E: Logistics and Transportation Review148, 102263. https://doi.org/10.1016/j.tre.2021.102263
Ozbaygin, G., & Savelsbergh, M. (2019). An iterative re-optimization framework for the dynamic vehicle routing problem with roaming delivery locations. Transportation Research Part B: Methodological128, 207-235. https://doi.org/10.1016/j.trb.2019.08.004
Ozbaygin, G., Karasan, O. E., Savelsbergh, M., & Yaman, H. (2017). A branch-and-price algorithm for the vehicle routing problem with roaming delivery locations. Transportation Research Part B: Methodological100, 115-137. https://doi.org/10.1016/j.trb.2019.08.004
Pham, Q. A., Hà, M. H., Vu, D. M., & Nguyen, H. H. (2022, June). A hybrid genetic algorithm for the vehicle routing problem with roaming delivery locations. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 32, pp. 297-306). https://doi.org/10.1609/icaps.v32i1.19813
Rastegar-Moghadam, H., Shafaei, A., & Jokar, M. R. A. (2019). Modeling the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) considering non-stationary customer locations within different time windows. Proceedings of the 16th National Conference on Industrial Engineering. https://en.civilica.com/doc/1034895/
Reyes, D., Savelsbergh, M., & Toriello, A. (2017). Vehicle routing with roaming delivery locations. Transportation Research Part C: Emerging Technologies80, 71-91. https://doi.org/10.1016/j.trc.2017.04.003
Rezvanian, S., Kashan, A. H., Rezvanian, A., & Sabzevari, A. (2025). Intelligent vehicle routing for stochastic service times: a grouping evolution strategy approach. International Journal of Transportation Science and Technology. https://doi.org/10.1016/j.ijtst.2025.04.009
Shafaei, A., Jokar, M. R. A., Rafiee, M., & Hemmati, A. (2021). Using the stochastic programming for the vehicle routing problems with the probability of customer absentee - a case study in a roadside assistance company. Proceedings of the 16th National Conference on Industrial Engineering. https://en.civilica.com/doc/1034895/
Shafaei, A., Jokar, M. R. A., Rafiee, M., & Hemmati, A. (2025). Using the route planning for supplying spare parts to reduce distribution costs: a case study in a roadside assistance company. International Journal of Shipping and Transport Logistics20(1), 131-158. https://doi.org/10.1504/IJSTL.2025.144995
Tilk, C., Olkis, K., & Irnich, S. (2021). The last-mile vehicle routing problem with delivery options. Or Spectrum43(4), 877-904. https://doi.org/10.1007/s00291-021-00633-0
Wang, X., & Zhao, J. (2025). Distributionally robust optimization of the vehicle routing problem with uncertain customers. Journal of Industrial and Management Optimization21(3), 1983-2006. https://doi.org/10.3934/jimo.2024159
Yuan, Y., Cattaruzza, D., Ogier, M., Semet, F., & Vigo, D. (2021). A column generation based heuristic for the generalized vehicle routing problem with time windows. Transportation Research Part E: Logistics and Transportation Review152, 102391. https://doi.org/10.1016/j.tre.2021.102391

Articles in Press, Accepted Manuscript
Available Online from 22 June 2026

  • Receive Date 30 April 2026
  • Revise Date 11 June 2026
  • Accept Date 22 June 2026
  • First Publish Date 22 June 2026
  • Publish Date 22 June 2026