Quantum competitive decision algorithm for the emergency siting problem under given deadline conditions

Allocating emergency resources effectively is an essential aspect of disaster preparation and response. The Emergency Siting Problem (ESP) involves identifying the best places to locate emergency services in order that it can serve the most people in the least amount of time. Maintaining time limitations is of greatest significance in situations where each second matters, such as during disasters or public health emergencies. In this study, we concentrate on the difficulty of solving the ESP under extreme time limits. In this research, Genetic-adaptive reptile search optimization (GRSO) is proposed to provide a different way to solve the ESP problem within the constraints of limited time. The proposed GRSO method takes into account travel times, prospective facility places, and the geographic location of demand sites while keeping to the established time restrictions. In this study, the proposed method demonstrating superior performance accuracy in locating transportation facilities under extreme time limits for Emergency Service Planning (ESP), outperforming established optimization strategies and heuristics commonly applied to ESP problems. A fitness function is created to assess the standard of responses based on elements including response speed, coverage, and meeting deadlines. The GRSO algorithm has been modified and altered to handle the distinctive features of the ESP, such as precise facility placements and time constraints. Simulated and real-world datasets describing emergency circumstances are used in computational research to confirm the efficiency of the proposed method. The results are evaluated with established optimization strategies and heuristics generally applied to ESP problems. Results show that the GRSOapproach provides solutions that are more in pace with time limit constraints without sacrificing sufficient degrees of coverage or response time.