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A Mixed-Integer Programming Framework for Drone Routing and Scheduling with Flexible Multiple Visits in Highway Traffic Monitoring

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Publicado en:Mathematics vol. 13, no. 15 (2025), p. 2427-2456
Autor principal: Mohabbati-Kalejahi Nasrin
Otros Autores: Alavi Sepideh, Toragay Oguz
Publicado:
MDPI AG
Materias:
United States > US
California
Traffic accidents
Software
Linear programming
Integer programming
Police departments
Law enforcement
Crashes
Mathematical models
Optimization techniques
Roads & highways
Injuries
Transportation planning
Traffic flow
Unmanned aerial vehicles
Monitoring
Monitoring systems
Economic impact
Arrest warrants
Nonlinear programming
Optimization models
Vehicles
Scheduling
Fatalities
Cameras
Public safety
Traffic violations
Traffic accidents & safety
Sensors
Drones
Traffic congestion
Algorithms
Mixed integer
Surveillance
Constraints
Helicopters
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Resumen:Traffic crashes and congestion generate high social and economic costs, yet traditional traffic monitoring methods, such as police patrols, fixed cameras, and helicopters, are costly, labor-intensive, and limited in spatial coverage. This paper presents a novel Drone Routing and Scheduling with Flexible Multiple Visits (DRSFMV) framework, an optimization model for planning drone-based highway monitoring under realistic operational constraints, including battery limits, variable monitoring durations, recharging at a depot, and target-specific inter-visit time limits. A mixed-integer nonlinear programming (MINLP) model and a linearized version (MILP) are presented to solve the problem. Due to the NP-hard nature of the underlying problem structure, a heuristic solver, Hexaly, is also used. A case study using real traffic census data from three Southern California counties tests the models across various network sizes and configurations. The MILP solves small and medium instances efficiently, and Hexaly produces high-quality solutions for large-scale networks. Results show clear trade-offs between drone availability and time-slot flexibility, and demonstrate that stricter revisit constraints raise operational cost.
ISSN:2227-7390
DOI:10.3390/math13152427
Fuente:Engineering Database