Abstract The highly complex and dynamically uncertain nature of forest fire management necessitates the optimization of task scheduling and emergency resource delivery decisions. To enhance the performance of forest fire emergency response and reduce disaster losses, this study constructs a mixed-integer nonlinear programming model aimed at maximizing firefighting performance in affected areas. The model integrates key factors such as fire spread rate, disaster relief time constraints, and resource demand urgency. It addresses uncertain parameters—such as resource delivery time and fire point recovery time—by applying an interval number-based deterministic processing method. To solve this problem, an improved Tabu Search-Simulated Annealing hybrid algorithm (ITS-SAA), is developed. ITS-SAA improves four types of neighborhood operators and repair operators to improve the algorithm. Compared with TS, SAA, Immune Optimization Algorithm (IOA) and Differential Evolution Algorithm (DE), the ITS-SAA achieves an average optimization improvement of 5.89%, 3.14%, 73.20% and 68.94% respectively. The results show: (1) The ITS-SAA demonstrates both reliability and effectiveness. (2) There exists an optimal threshold for resource allocation. (3) In scenarios with insufficient delivery resources, merely increasing the number of firefighting teams yields limited improvements. Decision-makers should properly configure the number of delivery teams and firefighting teams, with priority given to enhancing logistical delivery capacity.
Zhou et al. (Tue,) studied this question.
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