Gamifying the Vehicle Routing Problem with Stochastic Requests
Published Online:16 Oct 2025https://doi.org/10.1287/ijoc.2024.0838
References
- (2020) Dynamic traveling salesman problem with stochastic release dates. Eur. J. Oper. Res. 280(3):832–844.Crossref, Google Scholar
- (2019) ORL: Reinforcement learning benchmarks for online stochastic optimization problems. Preprint, submitted November 24, https://arxiv.org/abs/1911.10641.Google Scholar
- (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
- (2004) Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper. Res. 52(6):977–987.Link, Google Scholar
- (2009) Adaptive granular local search heuristic for a dynamic vehicle routing problem. Comput. Oper. Res. 36(11):2955–2968.Crossref, Google Scholar
- (2005) Waiting strategies for dynamic vehicle routing. Transportation Sci. 39(3):298–312.Link, Google Scholar
- (2010) Information relaxations and duality in stochastic dynamic programs. Oper. Res. 58(4):785–801.Link, Google Scholar
- (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
- (2022) Deep Q-learning for same-day delivery with vehicles and drones. Eur. J. Oper. Res. 298(3):939–952.Crossref, Google Scholar
- (2018) Real-time visual tracking by deep reinforced decision making. Comput. Vision Image Understanding 171:10–19.Crossref, Google Scholar
- (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.Crossref, Google Scholar
- (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.Crossref, Google Scholar
- (1999) Parallel tabu search for real-time vehicle routing and dispatching. Transportation Sci. 33(4):381–390.Link, Google Scholar
- (2012) A comparison of anticipatory algorithms for the dynamic and stochastic traveling salesman problem. Transportation Sci. 46(3):374–387.Link, Google Scholar
- (2009) Anticipatory algorithms for same-day courier dispatching. Transportation Res. Part E Logist. Transportation Rev. 45(1):96–106.Crossref, Google Scholar
- (2016) Deep reinforcement learning from self-play in imperfect-information games. Preprint, submitted March 3, https://arxiv.org/abs/1603.01121.Google Scholar
- (2006) Solving a dynamic and stochastic vehicle routing problem with a sample scenario hedging heuristic. Transportation Sci. 40(4):421–438.Link, Google Scholar
- (2000) Diversion issues in real-time vehicle dispatching. Transportation Sci. 34(4):426–438.Link, Google Scholar
- (2006) Exploiting knowledge about future demands for real-time vehicle dispatching. Transportation Sci. 40(2):211–225.Link, Google Scholar
- (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
- (2022) Dynamic ride-hailing with electric vehicles. Transportation Sci. 56(3):775–794.Link, Google Scholar
- (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
- (2017) Playing FPS games with deep reinforcement learning. Proc. 31st AAAI Conf. Artificial Intelligence (AAAI Press), 2140–2146.Google Scholar
- (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
- (2011) Anticipatory Optimization for Dynamic Decision Making, Operations Research/Computer Science Interfaces Series, vol. 51 (Springer, New York).Crossref, Google Scholar
- (2004) Waiting strategies for the dynamic pickup and delivery problem with time windows. Transportation Res. Part B Methodological 38(7):635–655.Crossref, Google Scholar
- (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533.Crossref, Google Scholar
- (2022) End-to-end on-line rescheduling from Gantt chart images using deep reinforcement learning. Internat. J. Production Res. 60(14):4434–4463.Crossref, Google Scholar
- (1980) A dynamic programming solution to the single vehicle many-to-many immediate request dial-a-ride problem. Transportation Sci. 14(2):130–154.Link, Google Scholar
- RLlib (2024) Industry-grade reinforcement learning. Accessed June 16, 2024, https://docs.ray.io/en/latest/rllib/rllib-algorithms.html#dqn.Google Scholar
- (2017) Deep reinforcement learning framework for autonomous driving. Electronic Imaging 2017(19):70–76.Crossref, Google Scholar
- (2018) A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 362(6419):1140–1144.Crossref, Google Scholar
- (2019) Adaptive state space partitioning for dynamic decision processes. Bus. Inform. Systems Engrg. 61(3):261–275.Crossref, Google Scholar
- (2022) Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review. Eur. J. Oper. Res. 298(3):801–820.Crossref, Google Scholar
- (2007) The dynamic shortest path problem with anticipation. Eur. J. Oper. Res. 176(2):836–854.Crossref, Google Scholar
- (2018a) Budgeting time for dynamic vehicle routing with stochastic customer requests. Transportation Sci. 52(1):20–37.Link, Google Scholar
- (2018b) Value function approximation for dynamic multi-period vehicle routing. Eur. J. Oper. Res. 269(3):883–899.Crossref, Google Scholar
- (2018c) Offline–online approximate dynamic programming for dynamic vehicle routing with stochastic requests. Transportation Sci. 53(1):185–202.Link, Google Scholar
- (2020) On modeling stochastic dynamic vehicle routing problems. EURO J. Transportation Logist. 9(2):100008.Crossref, Google Scholar
- (2004) Dynamic routing problems with fruitful regions: Models and evolutionary computation. Parallel Problem Solving from Nature-PPSN VIII (Springer, Berlin, Heidelberg), 692–701.Crossref, Google Scholar
- (2019) Grandmaster level in Starcraft II using multi-agent reinforcement learning. Nature 575(7782):350–354.Crossref, Google Scholar
- (2023) A spatial pyramid pooling-based deep reinforcement learning model for dynamic job-shop scheduling problem. Computers Oper. Res. 160:106401.Crossref, Google Scholar
- (2023) Dynamic vehicle routing with random requests: A literature review. Internat. J. Production Econom. 256:108751.Crossref, Google Scholar

