Comparative Analysis of Metaheuristic Algorithms for Emergency Response Vehicle Routing

Emergency Response Vehicle Routing, a vital application of the Vehicle Routing Problem (VRP), presents unique challenges in transportation and logistics, where optimized solutions can dramatically improve response times and resource allocation. To address this complex problem, metaheuristic algorithms play a key role in providing efficient and practical solutions. This research presents a detailed performance evaluation of metaheuristic algorithms applied to Emergency Response Vehicle Routing. The evaluated algorithms include Genetic Algorithm (GA), Ant Colony Optimization (ACO), Tabu Search (TS), and Simulated Annealing (SA). Their performance is analyzed based on key metrics such as execution time, cache performance, memory utilization, and solution quality. Profiling tools such as gperftools, <chrono>, and Valgrind’s Callgrind provide insights into metrics like interrupts, cache misses, bytes, and instructions executed. Results indicate that while Simulated Annealing achieves the fastest execution time with minimal resource consumption, the Genetic Algorithm excels in delivering high-quality solutions. Ant Colony Optimization stands out in complex dynamic routing tasks, making it highly effective for emergency response scenarios. This research provides valuable guidance on selecting the most appropriate algorithm for time-sensitive applications, particularly in emergency response, transportation, and logistics, where optimization and computational efficiency are critical factors.