## 1 Introduction  
Freeways are essential transportation corridors in modern economies and societies, and any disruption or damage to the road network can have serious repercussions. Moreover, freeways serve as critical routes for rescue and evacuation following large-scale disaster events [1]. Major natural disasters or human-made disasters, though infrequent, have extensive and severe impacts [2]. Wen these regional emergencies occur, the emergency management sector encounters additional challenges, including the integration of information, the coordination of resources, and the dispatch of materials [3]. For instance, the 2023 Sichuan Jinyang '8-21' flash flood and the Gansu '12-18' earthquake in China were large-scale disasters that necessitated simultaneous rescue efforts at multiple locations, resulting in concurrent emergency response (CER). To prevent the spread of a catastrophe, multiple emergency management agencies must respond simultaneously by supplying resources and dispatching personnel to various affected locations. This concurrent response presents greater challenges compared to a single emergency response (SER) [4]. Therefore, effectively managing CER, coordinating rescue forces on freeways and their surroundings, and minimizing disaster losses are of great practical significance. Generally, after an emergency occurs on a freeway, the corresponding road management department is directly responsible for dispatching nearby rescue forces. The most crucial aspect of this process is ensuring rapid arrival at the scene. For this reason, topics such as travel time prediction in emergencies [5] and emergency vehicle path planning [6] have been widely discussed by scholars. Further, when facing a CER situation, to avoid blind rescue efforts, the relevant road management department will swiftly establish an emergency response team to make resource scheduling decisions for the region. The specific response procedure is illustrated in Figure 1. At this stage, the focus shifts to how to efficiently schedule resources to maximize rescue effectiveness, while also considering factors such as social impact. Two challenges arise in decision-making for CER: (i) The challenge of equitable resource allocation. Resource competition can occur among multiple disaster sites, with varying degrees of urgency. Addressing the competing needs between different priority disaster sites to ensure a prompt and reasonable response is a significant challenge; (ii) The difficulty of rapidly configuring multiple resources and disaster sites simultaneously. Due to the heterogeneity of events, more types of resources are needed at each disaster site. This requires coordinating multiple resource sites to respond collectively, necessitating the handling of more complex dispatch information in the decision-making process. In this paper, the optimization of the emergency vehicle (EV) scheduling problem for the concurrent emergency response is addressed through model construction and algorithm development, aligned with the actual emergency rescue work requirements of freeways. The main contributions of this paper can be summarized as follows:  
1. Joint scheduling model for CER situation: from the perspective of the freeway emergency response process, a joint scheduling model for EVs is developed to address concurrent emergencies involving multiple disaster and resource sites. 2. Casualty risk function for rescue equity: considering the rescue equity problem in CER situations, a casualty risk function based on the rescue waiting time is designed to effectively coordinate the competition among disaster sites. 3. Tailored optimization algorithm for rapid decision-making: to achieve the rapid configuration of multiple resources and disaster sites, a heuristic algorithm termed adaptive dual evolutionary particle swarm optimization (ADEPSO) is tailored for the proposed scheduling model and its superiority in large-sized instances is verified. 4.