@unpublished{mirjalili2026mothership,title={Coordinated Mothership Scheduling and Drone Routing Optimization: A Branch-and-Price-and-Cut Approach},author={Mirjalili, Reza and Lim, Gino J.},year={2026},note={Manuscript in preparation},}
Traditional project-planning methods such as CPM and PERT rely on simplified and often static assumptions regarding task interdependencies and resource performance. This work models projects as heterogeneous activity–resource graphs and evaluates GraphSAGE and Temporal Graph Networks for predicting project duration and cost. The learned models achieve a 23 to 31 percent reduction in mean absolute error relative to conventional baselines, with R² improving to approximately 0.91 on complex projects, and the learned embeddings provide interpretable information about resource constraints and critical dependencies.
@article{mirjalili2025resource,title={Resource-Based Time and Cost Prediction in Project Networks: From Statistical Modeling to Graph Neural Networks},author={Mirjalili, Reza and Braghi, Behrad and Shadrokh Sikari, Shahram},journal={arXiv preprint arXiv:2511.15003},year={2025},month=nov,doi={10.48550/arXiv.2511.15003},url={https://arxiv.org/abs/2511.15003},}
This study examines how New York City’s transportation network recovers after snow storms, using network science metrics together with public data from the city’s sanitation department and traffic speed detectors across eight snow events. Bhattacharyya distance and Kolmogorov–Smirnov tests are used to determine when traffic conditions return to normal patterns. The results show that less data-intensive graph theory metrics can be used to estimate transportation network performance, enabling recovery-time forecasts that help city officials and businesses anticipate when operations can resume after winter storms.
@article{mirjalili2023resilience,title={Resilience Analysis of New York City Transportation Network After Snow Storms},author={Mirjalili, Reza and Barati, Hojjat and Yazici, Anil},journal={Transportation Research Record: Journal of the Transportation Research Board},volume={2677},number={1},pages={694--707},year={2023},publisher={SAGE Publications},doi={10.1177/03611981221101034},url={https://doi.org/10.1177/03611981221101034},}
A critical component in the public health response to pandemics is the ability to determine the spread of diseases via diagnostic testing kits. This paper presents a delivery scheduling method for diagnostic testing kits using a truck that carries multiple drones. The problem is decomposed into two phases: optimizing the truck route to minimize travel distance, then optimizing the drone delivery schedule to minimize total delivery time, iterating until an optimal solution is reached. Heuristic algorithms reduce computation time to under 50 minutes compared with over 10 hours for exact methods, and against conventional drive-through testing sites the drone-based approach achieves a modified basic reproduction rate of 0.002 versus 0.0153 for face-to-face testing.
@article{park2022scheduling,title={Scheduling Diagnostic Testing Kit Deliveries with the Mothership and Drone Routing Problem},author={Park, Hyung Jin and Mirjalili, Reza and C{\o}t{\'e}, Murray J. and Lim, Gino J.},journal={Journal of Intelligent \& Robotic Systems},volume={105},number={2},pages={38},year={2022},publisher={Springer},doi={10.1007/s10846-022-01632-1},url={https://doi.org/10.1007/s10846-022-01632-1},}