An integrated decision framework and optimization model to forward deployment and rearward movement in city public emergency events

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Abstract
Integrating forward deployment and rearward movement is a challenge in city public emergency events. An integrated decision framework and optimization model were constructed to minimize response completion time in the first stage and to improve treatment effectiveness in the second stage. A hybrid solution approach combining a genetic algorithm and dynamic programming was developed to address the coordinated rescue-dispatch problem in the first stage and the hospital medical-resource scheduling problem in the second stage. A real-world case study and sensitivity analyses were conducted to evaluate the effectiveness and applicability of the proposed framework. The results show that the proposed approach can reduce emergency-vehicle deployment requirements compared with reference dispatch configurations. Furthermore, the sensitivity analyses reveal that learning and fatigue effects have a significant impact on emergency medical scheduling. Incorporating learning effects improves system performance, whereas fatigue effects tend to reduce operational efficiency during prolonged rescue operations. When both effects are considered simultaneously, coordinated scheduling yields superior overall system performance. The proposed framework provides practical decision support for integrated urban emergency response under resource and traffic constraints.

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