Gamifying the Vehicle Routing Problem with Stochastic Requests

Published Online:https://doi.org/10.1287/ijoc.2024.0838

References

  • Archetti C, Feillet D, Mor A, Speranza M (2020) Dynamic traveling salesman problem with stochastic release dates. Eur. J. Oper. Res. 280(3):832–844.CrossrefGoogle Scholar
  • Balaji B, Bell-Masterson J, Bilgin E, Damianou A, Moreno Garcia P, Jain A, Luo R, Maggiar A, Narayanaswamy B, Ye C (2019) ORL: Reinforcement learning benchmarks for online stochastic optimization problems. Preprint, submitted November 24, https://arxiv.org/abs/1911.10641.Google Scholar
  • Bellemare M, Dabney W, Munos R (2017) A distributional perspective on reinforcement learning. Precup D, Teh YW, eds. Proc. 34th Internat. Conf. Machine Learn., vol. 70 (PMLR, New York), 449–458.Google Scholar
  • Bent R, Van Hentenryck P (2004) Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper. Res. 52(6):977–987.LinkGoogle Scholar
  • Branchini R, Armentano A, Løkketangen A (2009) Adaptive granular local search heuristic for a dynamic vehicle routing problem. Comput. Oper. Res. 36(11):2955–2968.CrossrefGoogle Scholar
  • Branke J, Middendorf M, Noeth G, Dessouky M (2005) Waiting strategies for dynamic vehicle routing. Transportation Sci. 39(3):298–312.LinkGoogle Scholar
  • Brown D, Smith J, Sun P (2010) Information relaxations and duality in stochastic dynamic programs. Oper. Res. 58(4):785–801.LinkGoogle Scholar
  • Caspar M, Wendt O (2024) Reinforcement learning applied to the dynamic capacitated profitable tour problem with stochastic requests. Gervasi O, Murgante B, Garau C, Taniar D, Rocha AMAC, Faginas Lago MN, eds. Comput. Sci. Appl. (Springer Nature, Cham, Switzerland), 346–363.Google Scholar
  • Chen X, Ulmer M, Thomas B (2022) Deep Q-learning for same-day delivery with vehicles and drones. Eur. J. Oper. Res. 298(3):939–952.CrossrefGoogle Scholar
  • Choi J, Kwon J, Lee KM (2018) Real-time visual tracking by deep reinforced decision making. Comput. Vision Image Understanding 171:10–19.CrossrefGoogle Scholar
  • Ferrucci F, Bock S, Gendreau M (2013) A pro-active real-time control approach for dynamic vehicle routing problems dealing with the delivery of urgent goods. Eur. J. Oper. Res. 225(1):130–141.CrossrefGoogle Scholar
  • Gendreau M, Guertin F, Potvin JY, Séguin R (2006) Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries. Transportation Res. Part C Emerging Tech. 14(3):157–174.CrossrefGoogle Scholar
  • Gendreau M, Guertin F, Potvin JY, Taillard E (1999) Parallel tabu search for real-time vehicle routing and dispatching. Transportation Sci. 33(4):381–390.LinkGoogle Scholar
  • Ghiani G, Manni E, Thomas BW (2012) A comparison of anticipatory algorithms for the dynamic and stochastic traveling salesman problem. Transportation Sci. 46(3):374–387.LinkGoogle Scholar
  • Ghiani G, Manni E, Quaranta A, Triki C (2009) Anticipatory algorithms for same-day courier dispatching. Transportation Res. Part E Logist. Transportation Rev. 45(1):96–106.CrossrefGoogle Scholar
  • Heinrich J, Silver D (2016) Deep reinforcement learning from self-play in imperfect-information games. Preprint, submitted March 3, https://arxiv.org/abs/1603.01121.Google Scholar
  • Hvattum L, Løkketangen A, Laporte G (2006) Solving a dynamic and stochastic vehicle routing problem with a sample scenario hedging heuristic. Transportation Sci. 40(4):421–438.LinkGoogle Scholar
  • Ichoua S, Gendreau M, Potvin J (2000) Diversion issues in real-time vehicle dispatching. Transportation Sci. 34(4):426–438.LinkGoogle Scholar
  • Ichoua S, Gendreau M, Potvin J (2006) Exploiting knowledge about future demands for real-time vehicle dispatching. Transportation Sci. 40(2):211–225.LinkGoogle Scholar
  • Joe W, Lau H (2020) Deep reinforcement learning approach to solve dynamic vehicle routing problem with stochastic customers. Beck J, Buffet O, Hoffmann J, Karpas E, Sohrabj S, eds. Proc. Internat. Conf. Automated Planning Scheduling, vol. 30 (AAAI Press, Palo Alto, CA), 394–402.Google Scholar
  • Kullman N, Cousineau M, Goodson J, Mendoza J (2022) Dynamic ride-hailing with electric vehicles. Transportation Sci. 56(3):775–794.LinkGoogle Scholar
  • Kullman ND, Dudorov N, Cousineau M, Mendoza JE, Goodson JC (2025) Gamifying the vehicle routing problem with stochastic requests. http://dx.doi.org/10.1287/ijoc.2024.0838.cd, https://github.com/INFORMSJoC/2024.0838.Google Scholar
  • Lample G, Chaplot D (2017) Playing FPS games with deep reinforcement learning. Proc. 31st AAAI Conf. Artificial Intelligence (AAAI Press), 2140–2146.Google Scholar
  • Liu Y, Logan B, Liu N, Xu Z, Tang J, Wang Y (2017) Deep reinforcement learning for dynamic treatment regimes on medical registry data. 2017 IEEE Internat. Conf. Healthcare Informatics (IEEE, Piscataway, NJ), 380–385.Google Scholar
  • Meisel S (2011) Anticipatory Optimization for Dynamic Decision Making, Operations Research/Computer Science Interfaces Series, vol. 51 (Springer, New York).CrossrefGoogle Scholar
  • Mitrović-Minić S, Laporte G (2004) Waiting strategies for the dynamic pickup and delivery problem with time windows. Transportation Res. Part B Methodological 38(7):635–655.CrossrefGoogle Scholar
  • Mnih V, Kavukcuoglu K, Silver D, Rusu A, Veness J, Bellemare M, Graves A, et al. (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533.CrossrefGoogle Scholar
  • Palombarini J, Martínez C (2022) End-to-end on-line rescheduling from Gantt chart images using deep reinforcement learning. Internat. J. Production Res. 60(14):4434–4463.CrossrefGoogle Scholar
  • Psaraftis HN (1980) A dynamic programming solution to the single vehicle many-to-many immediate request dial-a-ride problem. Transportation Sci. 14(2):130–154.LinkGoogle Scholar
  • RLlib (2024) Industry-grade reinforcement learning. Accessed June 16, 2024, https://docs.ray.io/en/latest/rllib/rllib-algorithms.html#dqn.Google Scholar
  • Sallab A, Abdou M, Perot E, Yogamani S (2017) Deep reinforcement learning framework for autonomous driving. Electronic Imaging 2017(19):70–76.CrossrefGoogle Scholar
  • Silver D, Hubert T, Schrittwieser J, Antonoglou I, Lai M, Guez A, Lanctot M, et al. (2018) A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 362(6419):1140–1144.CrossrefGoogle Scholar
  • Soeffker N, Ulmer M, Mattfeld D (2019) Adaptive state space partitioning for dynamic decision processes. Bus. Inform. Systems Engrg. 61(3):261–275.CrossrefGoogle Scholar
  • Soeffker N, Ulmer M, Mattfeld D (2022) Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review. Eur. J. Oper. Res. 298(3):801–820.CrossrefGoogle Scholar
  • Thomas BW, White CC III (2007) The dynamic shortest path problem with anticipation. Eur. J. Oper. Res. 176(2):836–854.CrossrefGoogle Scholar
  • Ulmer M, Mattfeld D, Köster F (2018a) Budgeting time for dynamic vehicle routing with stochastic customer requests. Transportation Sci. 52(1):20–37.LinkGoogle Scholar
  • Ulmer M, Soeffker N, Mattfeld D (2018b) Value function approximation for dynamic multi-period vehicle routing. Eur. J. Oper. Res. 269(3):883–899.CrossrefGoogle Scholar
  • Ulmer MW, Goodson JC, Mattfeld DC, Hennig M (2018c) Offline–online approximate dynamic programming for dynamic vehicle routing with stochastic requests. Transportation Sci. 53(1):185–202.LinkGoogle Scholar
  • Ulmer MW, Goodson JC, Mattfeld DC, Thomas BW (2020) On modeling stochastic dynamic vehicle routing problems. EURO J. Transportation Logist. 9(2):100008.CrossrefGoogle Scholar
  • van Hemert JI, La Poutré JA (2004) Dynamic routing problems with fruitful regions: Models and evolutionary computation. Parallel Problem Solving from Nature-PPSN VIII (Springer, Berlin, Heidelberg), 692–701.CrossrefGoogle Scholar
  • Vinyals O, Babuschkin I, Czarnecki W, Mathieu M, Dudzik A, Chung J, Choi D, et al. (2019) Grandmaster level in Starcraft II using multi-agent reinforcement learning. Nature 575(7782):350–354.CrossrefGoogle Scholar
  • Xinquan W, Xuefeng Y (2023) A spatial pyramid pooling-based deep reinforcement learning model for dynamic job-shop scheduling problem. Computers Oper. Res. 160:106401.CrossrefGoogle Scholar
  • Zhang J, Woensel TV (2023) Dynamic vehicle routing with random requests: A literature review. Internat. J. Production Econom. 256:108751.CrossrefGoogle Scholar
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