Publication | Closed Access
Last Mile Distribution in Humanitarian Relief
541
Citations
12
References
2008
Year
Transport Network AnalysisEngineeringHumanitarian Relief ChainTransport LogisticEmergency ManagementOperations ResearchEmergency LogisticsLogisticsSystems EngineeringLogistics ModelTransportation EngineeringDisaster ResponseSupply Chain ManagementLast Mile DistributionInteger ProgrammingHumanitarian AidHumanitarian Relief Supply ChainBusinessVehicle Routing ProblemResource AllocationDisaster Risk ReductionEmergency Medicine
Last‑mile distribution delivers relief supplies from local distribution centers to disaster‑affected beneficiaries. The study proposes a mixed‑integer programming model to schedule vehicles and equitably allocate resources, aiming to minimize transportation costs while maximizing aid benefits, and highlights opportunities for intelligent transportation systems. The model represents a vehicle‑based system where an LDC supplies demand locations, deciding allocation and vehicle routes over the planning horizon, and optimizes these decisions while balancing tradeoffs. The model improves resource allocation and routing, demonstrating tradeoffs on test problems.
Last mile distribution is the final stage of a humanitarian relief chain; it refers to delivery of relief supplies from local distribution centers (LDCs) to beneficiaries affected by disasters. In this study, we consider a vehicle-based last mile distribution system, in which an LDC stores and distributes emergency relief supplies to a number of demand locations. The main decisions are allocating the relief supplies at the LDCs among the demand locations and determining the delivery schedules/routes for each vehicle throughout the planning horizon. We propose a mixed integer programming model that determines delivery schedules for vehicles and equitably allocates resources, based on supply, vehicle capacity, and delivery time restrictions, with the objectives of minimizing transportation costs and maximizing benefits to aid recipients. We show how the proposed model optimizes resource allocation and routing decisions and discuss the tradeoffs between these decisions on a number of test problems. Finally, we identify opportunities for the use of intelligent transportation systems in last mile distribution.
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