Sequential learning based re-optimization approaches for less model-based dynamic pick-up routing problem

References

• Bent, R. W., & Van Hentenryck, P. (2004). Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Operations Research, 52(6), 977–987. https://doi.org/10.1287/opre.1040.0124
   Web of Science ®Google Scholar
• Bertazzi, L., & Secomandi, N. (2018). Faster rollout search for the vehicle routing problem with stochastic demands and restocking. European Journal of Operational Research, 270(2), 487–497. https://doi.org/10.1016/j.ejor.2018.03.034https://www.sciencedirect.com/science/article/pii/S0377221718302625.
   Web of Science ®Google Scholar
• Bertsimas, D., & Van Ryzin, G. J. (1993). Stochastic and dynamic vehicle routing in the euclidean plane with multiple capacitated vehicles. Operations Research, 41(1), 60–76. https://doi.org/10.1287/opre.41.1.60
   Web of Science ®Google Scholar
• Bertsimas, D. J., & Van Ryzin, G. (1991). A stochastic and dynamic vehicle routing problem in the euclidean plane. Operations Research, 39(4), 601–615. https://doi.org/10.1287/opre.39.4.601
   Web of Science ®Google Scholar
• Bladt, M., & Nielsen, B. F. (2017). Matrix-exponential distributions in applied probability. (Vol. 81). Springer.
   Google Scholar
• Bopardikar, S. D., Smith, S. L., & Bullo, F. (2014). On dynamic vehicle routing with time constraints. IEEE Transactions on Robotics, 30(6), 1524–1532. https://doi.org/10.1109/TRO.2014.2344451
   Web of Science ®Google Scholar
• Branchini, R. M., Armentano, V. A., & Løkketangen, A. (2009). Adaptive granular local search heuristic for a dynamic vehicle routing problem. Computers & Operations Research, 36(11), 2955–2968. https://doi.org/10.1016/j.cor.2009.01.014
   Web of Science ®Google Scholar
• Branke, J., Middendorf, M., Noeth, G., & Dessouky, M. (2005). Waiting strategies for dynamic vehicle routing. Transportation Science, 39(3), 298–312. https://doi.org/10.1287/trsc.1040.0095
   Web of Science ®Google Scholar
• Breuer, L., & Baum, D. (2005). An introduction to queueing theory: and matrix-analytic methods. Springer Science & Business Media.
   Google Scholar
• Bullo, F., Frazzoli, E., Pavone, M., Savla, K., & Smith, S. L. (2011). Dynamic vehicle routing for robotic systems. Proceedings of the IEEE, 99(9), 1482–1504. https://doi.org/10.1109/JPROC.2011.2158181
   Web of Science ®Google Scholar
• Chen, H.-K., Hsueh, C.-F., & Chang, M.-S. (2006). The real-time time-dependent vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 42(5), 383–408. https://doi.org/10.1016/j.tre.2005.01.003
   Web of Science ®Google Scholar
• da Silva Júnior, O. S., Leal, J. E., & Reimann, M. (2021). A multiple ant colony system with random variable neighborhood descent for the dynamic vehicle routing problem with time windows. Soft Computing, 25(4), 2935–2948. https://doi.org/10.1007/s00500-020-05350-4
   Web of Science ®Google Scholar
• dos Santos Mignon, A., & da Rocha, R. L. D. A. (2017). An adaptive implementation of ε-greedy in reinforcement learning. Procedia Computer Science, 109, 1146–1151. https://doi.org/10.1016/j.procs.2017.05.431
   Google Scholar
• Du, G., Zheng, L., & Ouyang, X. (2019). Real-time scheduling optimization considering the unexpected events in home health care. Journal of Combinatorial Optimization, 37(1), 196–220. https://doi.org/10.1007/s10878-017-0220-3
   Web of Science ®Google Scholar
• Ehmke, J. (2012). Integration of information and optimization models for routing in city logistics. (Vol. 177). Springer Science & Business Media.
   Google Scholar
• Fikar, C., Juan, A. A., Martinez, E., & Hirsch, P. (2016). A discrete-event driven metaheuristic for dynamic home service routing with synchronised trip sharing. European Journal of Industrial Engineering, 10(3), 323–340. https://doi.org/10.1504/EJIE.2016.076382
   Web of Science ®Google Scholar
• Gallager, R. G. (2011). Discrete stochastic processes. Open-CourseWare: Massachusetts Institute of Technology.
   Google Scholar
• Gendreau, M., Guertin, F., Potvin, J.-Y., & Séguin, R. (2006). Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries. Transportation Research Part C: Emerging Technologies, 14(3), 157–174. https://doi.org/10.1016/j.trc.2006.03.002
   Web of Science ®Google Scholar
• Gendreau, M., Guertin, F., Potvin, J.-Y., & Taillard, E. (1999). Parallel tabu search for real-time vehicle routing and dispatching. Transportation Science, 33(4), 381–390. https://doi.org/10.1287/trsc.33.4.381
   Web of Science ®Google Scholar
• Ghiani, G., Guerriero, F., Laporte, G., & Musmanno, R. (2003). Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies. European Journal of Operational Research, 151(1), 1–11. https://doi.org/10.1016/S0377-2217(02)00915-3 https://www.sciencedirect.com/science/article/pii/S0377221702009153.
   Web of Science ®Google Scholar
• Ghiani, G., Manni, E., Quaranta, A., & Triki, C. (2009). Anticipatory algorithms for same-day courier dispatching. Transportation Research Part E: Logistics and Transportation Review, 45(1), 96–106. https://doi.org/10.1016/j.tre.2008.08.003
   Web of Science ®Google Scholar
• Giménez-Palacios, I., Parreño, F., Álvarez-Valdés, R., Paquay, C., Oliveira, B. B., Carravilla, M. A., & Oliveira, J. F. (2022). First-mile logistics parcel pickup: Vehicle routing with packing constraints under disruption. Transportation Research Part E: Logistics and Transportation Review, 164, 102812. https://doi.org/10.1016/j.tre.2022.102812
   Web of Science ®Google Scholar
• Gmira, M., Gendreau, M., Lodi, A., & Potvin, J.-Y. (2021). Managing in real-time a vehicle routing plan with time-dependent travel times on a road network. Transportation Research Part C: Emerging Technologies, 132, 103379. https://doi.org/10.1016/j.trc.2021.103379
   Web of Science ®Google Scholar
• Goodarzi, A. H., Diabat, E., Jabbarzadeh, A., & Paquet, M. (2022). An m/m/c queue model for vehicle routing problem in multi-door cross-docking environments. Computers & Operations Research, 138, 105513. https://doi.org/10.1016/j.cor.2021.105513
   Web of Science ®Google Scholar
• Hougardy, S., Zaiser, F., & Zhong, X. (2020). The approximation ratio of the 2-opt heuristic for the metric traveling salesman problem. Operations Research Letters, 48(4), 401–404. https://doi.org/10.1016/j.orl.2020.05.007
   Web of Science ®Google Scholar
• Huang, N., Li, J., Zhu, W., & Qin, H. (2021). The multi-trip vehicle routing problem with time windows and unloading queue at depot. Transportation Research Part E: Logistics and Transportation Review, 152, 102370. https://doi.org/10.1016/j.tre.2021.102370https://www.sciencedirect.com/science/article/pii/S1366554521001381.
   Web of Science ®Google Scholar
• Huang, Y., Zhao, L., Powell, W. B., Tong, Y., & Ryzhov, I. O. (2019). Optimal learning for urban delivery fleet allocation. Transportation Science, 53(3), 623–641. https://doi.org/10.1287/trsc.2018.0861
   Web of Science ®Google Scholar
• Hvattum, L. M., Løkketangen, A., & Laporte, G. (2006). Solving a dynamic and stochastic vehicle routing problem with a sample scenario hedging heuristic. Transportation Science, 40(4), 421–438. https://doi.org/10.1287/trsc.1060.0166
   Web of Science ®Google Scholar
• Ichoua, S., Gendreau, M., & Potvin, J.-Y. (2000). Diversion issues in real-time vehicle dispatching. Transportation Science, 34(4), 426–438. https://doi.org/10.1287/trsc.34.4.426.12325
   Web of Science ®Google Scholar
• Ichoua, S., Gendreau, M., & Potvin, J.-Y. (2006). Exploiting knowledge about future demands for real-time vehicle dispatching. Transportation Science, 40(2), 211–225. https://doi.org/10.1287/trsc.1050.0114
   Web of Science ®Google Scholar
• Keskin, M., Laporte, G., & Çatay, B. (2019). Electric vehicle routing problem with time-dependent waiting times at recharging stations. Computers & Operations Research, 107, 77–94. https://doi.org/10.1016/j.cor.2019.02.014
   Web of Science ®Google Scholar
• Kovacs, A. A., Golden, B. L., Hartl, R. F., & Parragh, S. N. (2014). Vehicle routing problems in which consistency considerations are important: A survey. Networks, 64(3), 192–213. https://doi.org/10.1002/net.v64.3
   Web of Science ®Google Scholar
• Kullman, N. D., Goodson, J. C., & Mendoza, J. E. (2021). Electric vehicle routing with public charging stations. Transportation Science, 55(3), 637–659. https://doi.org/10.1287/trsc.2020.1018
   Web of Science ®Google Scholar
• Lin, C., Choy, K. L., Ho, G. T., Lam, H., Pang, G. K., & Chin, K.-S. (2014). A decision support system for optimizing dynamic courier routing operations. Expert Systems with Applications, 41(15), 6917–6933. https://doi.org/10.1016/j.eswa.2014.04.036
   Web of Science ®Google Scholar
• Mercier, L., & Van Hentenryck, P. (2011). An anytime multistep anticipatory algorithm for online stochastic combinatorial optimization. Annals of Operations Research, 184(1), 233–271. https://doi.org/10.1007/s10479-010-0798-7
   Web of Science ®Google Scholar
• Pillac, V., Gendreau, M., Guéret, C., & Medaglia, A. L. (2013). A review of dynamic vehicle routing problems. European Journal of Operational Research, 225(1), 1–11. https://doi.org/10.1016/j.ejor.2012.08.015
   Web of Science ®Google Scholar
• Poonthalir, G., & Nadarajan, R. (2019). Green vehicle routing problem with queues. Expert Systems with Applications, 138, 112823. https://doi.org/10.1016/j.eswa.2019.112823
   Web of Science ®Google Scholar
• Powell, W. B., Simao, H. P., & Bouzaiene-Ayari, B. (2012). Approximate dynamic programming in transportation and logistics: A unified framework. EURO Journal on Transportation and Logistics, 1(3), 237–284. https://doi.org/10.1007/s13676-012-0015-8
   Google Scholar
• Psaraftis, H. N. (1980). A dynamic programming solution to the single vehicle many-to-many immediate request dial-a-ride problem. Transportation Science, 14(2), 130–154. https://doi.org/10.1287/trsc.14.2.130
   Web of Science ®Google Scholar
• Ritzinger, U., Puchinger, J., & Hartl, R. F. (2016). A survey on dynamic and stochastic vehicle routing problems. International Journal of Production Research, 54(1), 215–231. https://doi.org/10.1080/00207543.2015.1043403
   Web of Science ®Google Scholar
• Schilde, M., Doerner, K. F., & Hartl, R. F. (2014). Integrating stochastic time-dependent travel speed in solution methods for the dynamic dial-a-ride problem. European Journal of Operational Research, 238(1), 18–30. https://doi.org/10.1016/j.ejor.2014.03.005
   PubMed Web of Science ®Google Scholar
• Secomandi, N., & Margot, F. (2009). Reoptimization approaches for the vehicle-routing problem with stochastic demands. Operations Research, 57(1), 214–230. https://doi.org/10.1287/opre.1080.0520
   Web of Science ®Google Scholar
• Sherzer, E., Senderovich, A., Baron, O., & Krass, D. (2022). Can machines solve general queueing systems?'. arXiv preprint arXiv:2202.01729.
   Google Scholar
• Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
   Google Scholar
• Thomas, B. W. (2007). Waiting strategies for anticipating service requests from known customer locations. Transportation Science, 41(3), 319–331. https://doi.org/10.1287/trsc.1060.0183
   Web of Science ®Google Scholar
• Ulmer, M. W., Brinkmann, J., & Mattfeld, D. C. (2015). Anticipatory planning for courier,express and parcel services. In Logistics Management (pp. 313–324). Springer.
   Google Scholar
• Ulmer, M. W., Goodson, J. C., Mattfeld, D. C., & Hennig, M. (2019). Offline–online approximate dynamic programming for dynamic vehicle routing with stochastic requests. Transportation Science, 53(1), 185–202. https://doi.org/10.1287/trsc.2017.0767
   Web of Science ®Google Scholar
• Ulmer, M. W., Goodson, J. C., Mattfeld, D. C., & Thomas, B. W. (2017). Dynamic vehicle routing: Literature review and modeling framework.
   Google Scholar
• Ulmer, M. W., Mattfeld, D. C., & Köster, F. (2018). Budgeting time for dynamic vehicle routing with stochastic customer requests. Transportation Science, 52(1), 20–37. https://doi.org/10.1287/trsc.2016.0719
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.ejor.2007.03.012
   Web of Science ®Google Scholar
• Wu, Y., & Zeng, B. (2023). Dynamic parcel pick-up routing problem with prioritized customers and constrained capacity via lower-bound-based rollout approach. Computers & Operations Research, 154, 106176. https://doi.org/10.1016/j.cor.2023.106176
   Web of Science ®Google Scholar
• Yang, J., Jaillet, P., & Mahmassani, H. (2004). Real-time multivehicle truckload pickup and delivery problems. Transportation Science, 38(2), 135–148. https://doi.org/10.1287/trsc.1030.0068
   Web of Science ®Google Scholar
• Yu, X., Shen, S., & Wang, H. (2021). Integrated vehicle routing and service scheduling under time and cancellation uncertainties with application in nonemergency medical transportation. Service Science, 13(3), 172–191. https://doi.org/10.1287/serv.2021.0277
   Web of Science ®Google Scholar
• Zheng, J., & Gu, Z. (2017). Research on express delivery vehicle route planning method for stochastic customer demand. In 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China  (pp. 783–787).
   Google Scholar