Human-centric decision optimization for emergency personnel allocation on highways: a behavioural-informed metaheuristic approach

Abstract Existing highway emergency personnel allocation patterns exhibit imbalances in the workload of emergency personnel on some road sections. On certain road sections with low traffic volumes and minimal toll revenue, the operational costs for emergency personnel are excessively high. Therefore, an efficient and economical method is required for the allocation of highway emergency personnel to respond to complex and dynamic highway emergencies. We established an optimization model for emergency personnel allocation based on an improved emperor penguin optimization (EPO) algorithm. First, the model introduces assumptions regarding the resource consumption of highway emergency tasks, guaranteed task operation time and sequential order of task execution by emergency personnel. The highway emergency personnel allocation problem is described mathematically. The optimization objectives of the model include minimizing the weighted processing time of emergency events, maximizing the workload balance and minimizing deployment costs. Constraints on emergency resource allocation are also defined. We propose an improved EPO algorithm that focuses on the initialization and optimization stages, improving parameter configuration, position updating, iterative convergence and disaster evaluation. Data from a traffic management office in Kunming, China were used for case analysis. We focused on the relationship between emergency events and the locations of emergency protection sites. We also examined the allocation of resources for traffic and natural disasters. Additionally, the resource deployment costs before and after optimization were compared. The results demonstrate that the proposed model reduces the cost of resource allocation by 30.6% and greatly improves the optimization of highway emergency staffing.