The Stochastic Dynamic Postdisaster Inventory Allocation Problem with Trucks and UAVs

Published Online:https://doi.org/10.1287/trsc.2023.0438

References

  • Alem D, Clark A, Moreno A (2016) Stochastic network models for logistics planning in disaster relief. Eur. J. Oper. Res. 255(1):187–206.CrossrefGoogle Scholar
  • Altay N, Green WG (2006) OR/MS research in disaster operations management. Eur. J. Oper. Res. 175(1):475–493.CrossrefGoogle Scholar
  • Anaya-Arenas AM, Ruiz A, Renaud J (2016) Models for a Fair Humanitarian Relief Distribution (CIRRELT, Montreal).Google Scholar
  • Anderson R, Huchette J, Ma W, Tjandraatmadja C, Vielma JP (2020) Strong mixed-integer programming formulations for trained neural networks. Math. Programming 183(1–2):3–39.CrossrefGoogle Scholar
  • Balcik B, Beamon BM, Smilowitz K (2008) Last mile distribution in humanitarian relief. J. Intelligent Transportation Systems 12(2):51–63.CrossrefGoogle Scholar
  • Bamsey I (2021) Dossier: Wings for Aid MiniFreighter. Accessed July 5, 2022, https://ust-media.com/ust-magazine/UST035/22/.Google Scholar
  • Beirigo BA, Schulte F, Negenborn RR (2022) A learning-based optimization approach for autonomous ridesharing platforms with service-level contracts and on-demand hiring of idle vehicles. Transportation Sci. 56(3):677–703.LinkGoogle Scholar
  • Bellman R (1957) A Markovian decision process. J. Math. Mechanics 6(5):679–684.Google Scholar
  • Ben-Tal A, Do Chung B, Mandala SR, Yao T (2011) Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains. Transportation Res. Part B Methodological 45(8):1177–1189.CrossrefGoogle Scholar
  • Besiou M, Van Wassenhove LN (2020) Humanitarian operations: A world of opportunity for relevant and impactful research. Manufacturing Service Oper. Management 22(1):135–145.LinkGoogle Scholar
  • Bouzaiene-Ayari B, Cheng C, Das S, Fiorillo R, Powell WB (2016) From single commodity to multiattribute models for locomotive optimization: A comparison of optimal integer programming and approximate dynamic programming. Transportation Sci. 50(2):366–389.LinkGoogle Scholar
  • De Vries H, Van Wassenhove LN (2020) Do optimization models for humanitarian operations need a paradigm shift? Production Oper. Management 29(1):55–61.CrossrefGoogle Scholar
  • Delarue A, Anderson R, Tjandraatmadja C (2020) Reinforcement learning with combinatorial actions: An application to vehicle routing. Adv. Neural Inform. Processing Systems 33:609–620.Google Scholar
  • Dulac-Arnold G, Levine N, Mankowitz DJ, Li J, Paduraru C, Gowal S, Hester T (2021) Challenges of real-world reinforcement learning: Definitions, benchmarks and analysis. Machine Learn. 110:2419–2468.CrossrefGoogle Scholar
  • Fan J, Chang X, Mišić J, Mišić VB, Kang H (2022) DHL: Deep reinforcement learning-based approach for emergency supply distribution in humanitarian logistics. Peer-to-Peer Network Appl. 15(5):2376–2389.CrossrefGoogle Scholar
  • Heinold A, Meisel F, Ulmer MW (2023) Primal-dual value function approximation for stochastic dynamic intermodal transportation with eco-labels. Transportation Sci. 57(6):1452–1472.Google Scholar
  • Holguín-Veras J, Jaller M (2012) Immediate resource requirements after hurricane katrina. Natural Hazards Rev. 13(2):117–131.CrossrefGoogle Scholar
  • Holguín-Veras J, Pérez N, Jaller M, Van Wassenhove LN, Aros-Vera F (2013) On the appropriate objective function for post-disaster humanitarian logistics models. J. Oper. Management 31(5):262–280.CrossrefGoogle Scholar
  • Holguín-Veras J, Amaya-Leal J, Cantillo V, Van Wassenhove LN, Aros-Vera F, Jaller M (2016) Econometric estimation of deprivation cost functions: A contingent valuation experiment. J. Oper. Management 45:44–56.CrossrefGoogle Scholar
  • Hoyos MC, Morales RS, Akhavan-Tabatabaei R (2015) OR models with stochastic components in disaster operations management: A literature survey. Computers Industrial Engrg. 82:183–197.CrossrefGoogle Scholar
  • Huang K, Rafiei R (2019) Equitable last mile distribution in emergency response. Comput. Industrial Engrg. 127:887–900.CrossrefGoogle Scholar
  • Huang M, Smilowitz K, Balcik B (2012) Models for relief routing: Equity, efficiency and efficacy. Transportation Res. Part E Logist. Transportation Rev. 48(1):2–18.CrossrefGoogle Scholar
  • Huang K, Jiang Y, Yuan Y, Zhao L (2015) Modeling multiple humanitarian objectives in emergency response to large-scale disasters. Transportation Res. Part E Logist. Transportation Rev. 75:1–17.CrossrefGoogle Scholar
  • Ismail I (2021) A possibilistic mathematical programming model to control the flow of relief commodities in humanitarian supply chains. Comput. Industrial Engrg. 157:107305.CrossrefGoogle Scholar
  • Johnsson I (2016) Tracking Relief Aid: A Spatial Analysis of Aid Distribution in Nepal after the 2015 Gorkha Earthquake (Lund University, Lund, Sweden).Google Scholar
  • Klapp MA, Erera AL, Toriello A (2018) The one-dimensional dynamic dispatch waves problem. Transportation Sci. 52(2):402–415.LinkGoogle Scholar
  • Lei C, Jiang Z, Ouyang Y (2019) Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers. Transportation Res. Proc. 38:77–97.CrossrefGoogle Scholar
  • Lin YH, Batta R, Rogerson PA, Blatt A, Flanigan M (2011) A logistics model for emergency supply of critical items in the aftermath of a disaster. Socio-Econom. Planning Sci. 45(4):132–145.CrossrefGoogle Scholar
  • Liu Y, Lei H, Wu Z, Zhang D (2019) A robust model predictive control approach for post-disaster relief distribution. Comput. Industrial Engrg. 135:1253–1270.CrossrefGoogle Scholar
  • Lu CC, Ying KC, Chen HJ (2016) Real-time relief distribution in the aftermath of disasters: A rolling horizon approach. Transportation Res. Part E Logist. Transportation Rev. 93:1–20.CrossrefGoogle Scholar
  • Moreno A, Alem D, Ferreira D, Clark A (2018) An effective two-stage stochastic multi-trip location-transportation model with social concerns in relief supply chains. Eur. J. Oper. Res. 269(3):1050–1071.CrossrefGoogle Scholar
  • Nadi A, Edrisi A (2016) A reinforcement learning approach for evaluation of real-time disaster relief demand and network condition. Internat. J. Econom. Management Engrg. 11(1):5–10.Google Scholar
  • Nadi A, Edrisi A (2017) Adaptive multi-agent relief assessment and emergency response. Internat. J. Disaster Risk Reduction 24:12–23.CrossrefGoogle Scholar
  • Najafi M, Eshghi K, de Leeuw S (2014) A dynamic dispatching and routing model to plan/re-plan logistics activities in response to an earthquake. Oper. Res. Spectrum 36(2):323–356.CrossrefGoogle Scholar
  • Najafi M, Eshghi K, Dullaert W (2013) A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transportation Res. Part E Logist. Transportation Rev. 49(1):217–249.CrossrefGoogle Scholar
  • Natarajan KV, Swaminathan JM (2017) Multi-treatment inventory allocation in humanitarian health settings under funding constraints. Production Oper. Management 26(6):1015–1034.CrossrefGoogle Scholar
  • Pérez-Rodríguez N, Holguín-Veras J (2016) Inventory-allocation distribution models for postdisaster humanitarian logistics with explicit consideration of deprivation costs. Transportation Sci. 50(4):1261–1285.LinkGoogle Scholar
  • Powell WB (2011) Approximate Dynamic Programming: Solving the Curses of Dimensionality, 2nd ed. (John Wiley & Sons, Hoboken, NJ).CrossrefGoogle Scholar
  • Powell WB (2019) A unified framework for stochastic optimization. Eur. J. Oper. Res. 275(3):795–821.CrossrefGoogle Scholar
  • Powell WB (2022) Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions (Wiley, Hoboken, NJ).CrossrefGoogle Scholar
  • Rejeb A, Rejeb K, Simske S, Treiblmaier H (2021) Humanitarian drones: A review and research agenda. Internet Things 16:100434.CrossrefGoogle Scholar
  • Rivera AEP, Mes MRK (2017) Anticipatory freight selection in intermodal long-haul round-trips. Transportation Res. Part E Logist. Transportation Rev. 105:176–194.CrossrefGoogle Scholar
  • Rivera-Royero D, Galindo G, Yie-Pinedo R (2016) A dynamic model for disaster response considering prioritized demand points. Socio-Econom. Planning Sci. 55:59–75.CrossrefGoogle Scholar
  • Rottkemper B, Fischer K, Blecken A (2012) A transshipment model for distribution and inventory relocation under uncertainty in humanitarian operations. Socio-Econom. Planning Sci. 46(1):98–109.CrossrefGoogle Scholar
  • Sadeghi A, Aros-Vera F, Mosadegh H, YounesSinaki R (2023) Social cost-vehicle routing problem and its application to the delivery of water in post-disaster humanitarian logistics. Transportation Res. Part E Logist. Transportation Rev. 176:103189.CrossrefGoogle Scholar
  • Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017) Proximal policy optimization algorithms. Preprint, submitted July 20, https://arxiv.org/abs/1707.06347.Google Scholar
  • Shao J, Wang X, Liang C, Holguín-Veras J (2020) Research progress on deprivation costs in humanitarian logistics. Internat. J. Disaster Risk Reduction 42:101343.CrossrefGoogle Scholar
  • Sheu JB (2010) Dynamic relief-demand management for emergency logistics operations under large-scale disasters. Transportation Res. Part E Logist. Transportation Rev. 46(1):1–17.CrossrefGoogle Scholar
  • Soghrati Ghasbeh S, Pourmohammadzia N, Rabbani M (2022) Equitable post-disaster relief distribution: A robust multi-objective multi-stage optimization approach. J. Humanitarian Logist. Supply Chain Management 12(4):618–651.CrossrefGoogle Scholar
  • Tzeng GH, Cheng HJ, Huang TD (2007) Multi-objective optimal planning for designing relief delivery systems. Transportation Res. Part E Logist. Transportation Rev. 43(6):673–686.CrossrefGoogle Scholar
  • Van Heeswijk WJA, La Poutré H (2020) Deep reinforcement learning in linear discrete action spaces. Bae KH, Feng B, Kim S, Lazarova-Molnar S, Zheng Z, Roeder T, Thiesing R, eds. Proc. 2020 Winter Simulation Conf. (IEEE, New York City), 1063–1074.Google Scholar
  • Van Heeswijk WJA, Mes MRK, Schutten JMJ (2019) The delivery dispatching problem with time windows for urban consolidation centers. Transportation Sci. 53(1):203–221.LinkGoogle Scholar
  • Van Steenbergen RM, Mes MRK, Van Heeswijk WJA (2023) Reinforcement learning for humanitarian relief distribution with trucks and uavs under travel time uncertainty. Transportation Res. Part C Emerging Tech. 157:104401.CrossrefGoogle Scholar
  • Van Steenbergen RM, Lalla-Ruiz E, Van Heeswijk WJA, Mes MRK (2023) The heterogeneous fleet risk-constrained vehicle routing problem in humanitarian logistics. Daduna JR, Liedtke G, Shi X, Voβ S, eds. Computational Logistics (Springer Nature, Cham, Switzerland), 276–291.CrossrefGoogle Scholar
  • Vanajakumari M, Kumar S, Gupta S (2016) An integrated logistic model for predictable disasters. Production Oper. Management 25(5):791–811.CrossrefGoogle Scholar
  • Vanvuchelen N, Gijsbrechts J, Boute R (2020) Use of proximal policy optimization for the joint replenishment problem. Comput. Industry 119:103239.CrossrefGoogle Scholar
  • Wang S, Sun B (2023) Model of multi-period emergency material allocation for large-scale sudden natural disasters in humanitarian logistics: Efficiency, effectiveness and equity. Internat. J. Disaster Risk Reduction 85:103530Google Scholar
  • Yang Y, Yin Y, Wang D, Ignatius J, Cheng T, Dhamotharan L (2023) Distributionally robust multi-period location-allocation with multiple resources and capacity levels in humanitarian logistics. Eur. J. Oper. Res. 305(3):1042–1062.CrossrefGoogle Scholar
  • Yu L, Yang H, Miao L, Zhang C (2019) Rollout algorithms for resource allocation in humanitarian logistics. IISE Trans. 51(8):887–909.CrossrefGoogle Scholar
  • Yu L, Zhang C, Jiang J, Yang H, Shang H (2021) Reinforcement learning approach for resource allocation in humanitarian logistics. Expert Systems Appl. 173:114663.CrossrefGoogle Scholar
  • Zhan S, Liu S, Ignatius J, Chen D, Chan FT (2021) Disaster relief logistics under demand-supply incongruence environment: A sequential approach. Appl. Math. Modeling 89:592–609.CrossrefGoogle Scholar
  • Zhang X, Zhao C, Liao F, Li X, Du Y (2022) Online parking assignment in an environment of partially connected vehicles: A multi-agent deep reinforcement learning approach. Transportation Res. Part C Emerging Tech. 138:103624.CrossrefGoogle Scholar
  • Zhou Y, Liu J, Zhang Y, Gan X (2017) A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems. Transportation Res. Part E Logist. Transportation Rev. 99:77–95.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.