Optimizing humanitarian logistics operations : decomposition approaches for solving large-scale deterministic and stochastic vehicle routing problems.

• Online Access: http://hdl.handle.net/2047/D20416568

Immediately after a disaster, humanitarian efforts are hindered by limited logistical resources and infrastructure failures. To address these challenges, we propose several decision frameworks for scheduling and routing of trucks and uncrewed aerial vehicles (UAVs or drones). Through numerical analysis, we provide managerial insights into the impacts of disaster-specific geographical and system parameters on operational performance metrics.In the first framework, we consider the decision problem of replenishing warehouses with emergency supplies following a disaster. We define this problem as a variant of truckload open vehicle routing problems and present two alternative formulations. We designed two column-generation-based solution approaches and several path generation algorithms to solve the resulting models. From numerical experiments, we find that the proposed solution approaches outperform commercial solver in terms of solution time. Due to post-disaster infrastructure failures, obtaining precise demand information and meeting demands using only trucks become all but impossible. To that end, in our second framework, we consider a two-echelon vehicle routing problem, where we assume that trucks transport two sets of UAVs and stop at predefined (satellite) locations to work as UAV launching pads. The first set of dispatched UAVs provide temporary telecommunication signals and obtain demand information, while the second set of UAVs make deliveries to satisfy the demand. We present a two-stage decision approach and design column-generation-based decomposition heuristics to solve the resulting models. From numerical experiments, we find that the proposed approach significantly reduces the solution time with a slight reduction in solution quality. Using a case study with post-disaster scenarios in Puerto Rico, we evaluate the changes in the model outcomes with the variation in several system parameters. In the third framework, we consider a two-echelon vehicle routing problem, where UAVs are deployed only for aid delivery and emergency aid demand is uncertain due to telecommunication infrastructure failure. We develop a two-stage robust optimization approach to handle demand uncertainty. To solve the resulting models, we design a column-and-constraint-generation technique for scenario generation and integrate it with a column-generation-based heuristic designed for the efficient generation of drone routes. We also investigate the effect of various parameters on model outcomes using the same case study.--Author's abstract