| Summary: | 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
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