arXiv subjects

Sayed Hamid Hosseini Dolatabadi

Publications and source records attributed to Sayed Hamid Hosseini Dolatabadi.

2 recordsLinked to original sources

arXiv2026

Designing Hierarchical Hub-and-Spoke Drone-Based Networks for Delivering Time-Sensitive Healthcare Items

Timely distribution of medical items (e.g., whole blood and vaccines) across regional healthcare networks often requires high-volume delivery operations from central facilities (e.g., blood banks) to intermediate regional hospitals, followed by rapid last-mile deliveries to points of injury. Traditional ground-based delivery systems often suffer from limited responsiveness (e.g., traffic congestion) and operational inefficiencies (e.g., blood waste). Leveraging aerial-drone-based delivery systems offers a promising solution for the fast and efficient delivery of time-sensitive medical items across regional healthcare networks. Therefore, we study a hierarchical hub-and-spoke drone-based network for delivering time-sensitive medical items with distinct release and due times to fixed and mobile delivery destinations. We consider a heterogeneous fleet of drones with distinct characteristics (e.g., cost, battery capacity, and speed) and different multi-trip delivery modes. We propose an efficient mixed-integer programming model for location/allocation of mobile delivery destinations, as well as routing and scheduling drones to minimize the total investment and operational costs of the drone delivery network while maintaining the delivery due times. We develop a customized exact solution method integrating problem-specific reformulations and dynamic cutting planes, as well as a fast heuristic algorithm by leveraging a simplified problem variant. Results based on a real-life case study of whole blood delivery data from Pendleton, Oregon, United States, and actual drone flight test data demonstrate that our exact and heuristic solution methods are 31 and 3,375 times faster, respectively, than the Gurobi solver. Results also show that allowing drones to perform multiple trips is 142.8% more cost-efficient than single trips.

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arXiv2025

Leveraging Electric School Buses for Disaster Recovery: Optimizing Routing and Energy Scheduling via Branch-and-Price

Natural disasters threaten the resilience of power systems, causing widespread power outages that disrupt critical loads (e.g., hospitals) and endanger public safety. Compared to the conventional restoration methods that often have long response times, leveraging government-controlled electric school buses (ESBs) with large battery capacity and deployment readiness offers a promising solution for faster power restoration to critical loads during disasters while traditional maintenance is underway. Therefore, we study the problem of routing and scheduling a heterogeneous fleet of ESBs to satisfy the energy demand of critical isolated loads around disasters addressing the following practical aspects: combined transportation and energy scheduling of ESBs, multiple back-and-forth trips of ESBs between isolated loads and charging stations, and spatial-wise coupling among multiple ESB routes. We propose an efficient mixed-integer programming model for routing and scheduling ESBs, accounting for the practical aspects, to minimize the total restoration cost over a planning horizon. We develop an efficient exact branch-and-price (B&P) algorithm and a customized heuristic B&P algorithm integrating dynamic programming and labeling algorithms. Numerical results based on a real case study of San Antonio disaster shelters and critical facilities demonstrate that our proposed exact B&P and heuristic B&P algorithms are computationally 121 and 335 times faster, respectively, than Gurobi. Using network sparsity to incorporate the limitation in shelter-ESB type compatibility in the model demonstrates that the total restoration cost increases, on average, by 207% as the network becomes fully sparse compared to fully connected. The capacity utilization metric reflects that the proposed practical ESB routing and scheduling enables an ESB to meet the energy demand 4.5 times its effective usable capacity.

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