Fire Vehicle Scheduling Optimization Problem with Variable Demands in Emergency Rescue

Public emergencies are characterized by their suddenness, variability, and critical time constraints. To address the rapidly increasing demands for rescue services, ensure timely and effective responses, this paper considers the randomness in the distribution of accident locations and the uncertainty regarding the timing and quantities of demands. The decision-making process is adaptive, evolving over time and influenced by real-time environmental information. A rolling optimization approach is introduced to manage fluctuating demand to optimize the scheduling plan for fire vehicles. A systematic method is presented, dynamically segmenting the problem based on the occurrence of accident points. Each segment updates the immediate status of the fire station, the fire vehicles, and the accident scene upon receiving a new alarm. This ensures the efficient allocation of vehicles to new emergencies. The article validates the reliability and effectiveness of the model by simulating several scenario changes and demonstrates its practicality and feasibility through comparative data analysis.