Journal of Intelligent Transportation Systems · 2008 · 547 citations · 12 references
ManagementOperations ManagementTransportation PlanningSchedulingTransportTransportationInteger ProgrammingLast-mile DeliveryRoute PlanningTransportation ModelsTransportation ProblemsVehicle SchedulingDistribution Of Emergency SuppliesDistribution Resource AllocationEmergency Relief DistributionFair Resource AllocationHumanitarian Relief SuppliesMixed-integer OptimizationMixed-integer ProgrammingTransportation Resource Allocation
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.
12