Optimized Vehicle Patrol Scheduling: Balancing Efficiency and Coverage in Urban Safety Operations

Efficient vehicle patrol scheduling is crucial for enhancing urban safety, ensuring security, and optimizing operational efficiency. Traditional scheduling methods often struggle to balance multiple objectives under constraints such as limited vehicles and patrol times. Addressing these challenges can improve response times, reduce operational costs, and maintain effective security coverage. In this work, we propose a twofold solution: a Mixed-Integer Linear Programming (MILP) model designed for small-scale problems to guarantee optimal solutions, and two heuristic approaches—Adaptive Heuristic-Based Patrol Scheduling (AHBPS) and Genetic-Based Dynamic Vehicle Patrol Scheduling (GDVPS)—to address more complex, dynamic scenarios. Our results demonstrate significant improvements in both efficiency and coverage, with GDVPS outperforming AHBPS in more intricate networks. These findings highlight the potential for GDVPS as a dynamic, robust, and scalable solution, warranting further development to enhance urban safety systems.