## 2 Literature Review  
The VRPTW is a classical optimization problem. [PERSON] [14] first proposed a push-forward insertion heuristic algorithm to solve the VRPTW in 1987. Since then, numerous innovative algorithms have been developed to address the VRPTW. For example, [PERSON] et al. [15] proposed a parallel insertion heuristic method to construct parallel paths in VRP, while [PERSON] et al. [16] introduced a greedy insertion heuristic based on a look-ahead heuristic to enhance traditional vehicle routing methods. However, these algorithms do not guarantee population diversity. [PERSON] et al. [17] addressed this issue by combining the three aforementioned heuristic methods and developing an improved push-forward insertion heuristic (PFIH) strategy to maintain population diversity in solving the VRPTW in prefabricated systems. In addition, [PERSON] et al. [18] conducted a survey on metaheuristics for the VRPTW and analyzed the effectiveness of various approaches. [PERSON] et al. [19] developed a combined evolutionary multitask algorithm for solving multi-objective VRPTW, and [PERSON] et al. [20] proposed a constraint-aware policy optimization for VRPTW. [PERSON] et al. [21] employed an adaptive large neighborhood search (ALNS) algorithm for the VRPTW, while [PERSON] et al. [22] introduced a hybrid novice local search for VRPTW. [PERSON] et al. [23] presented a three-phase heuristic approach for multi-trip VRPTW and resource synchronization on heterogeneous facilities. As research on VRPTW has deepened, its applications have broadened significantly. [PERSON] et al. [24] developed a hybrid algorithm based on a modified rank-based ant system for heterogeneous fixed fleet open VRPTW. [PERSON] et al. [25] presented an efficient hybrid genetic search with advanced diversity control for a large range of time-constrained vehicle routing problems, introducing several features to manage the temporal dimension. [PERSON] et al. [26] improved a variable neighborhood search-based algorithm with adaptive local search for VRPTW and multi-depot problems, with an aim to reduce vehicle fleet size. [PERSON] et al. [27] proposed a hybrid brainstorm optimization algorithm for the VRPTW to minimize the number of unserved customers. [PERSON] et al. [28] presented an improved ALNS algorithm to effectively solve large-scale cases of green VRPTW with multiple depots. [PERSON] [29] investigated a general variable neighborhood search metaheuristic to minimize the total distance traveled. [PERSON] et al. [30] designed a hybrid genetic algorithm with a large neighborhood search to minimize transport distance and energy consumption. [PERSON] et al. [31] developed an improved neighborhood search algorithm to reduce total costs, including vehicle dispatch and transportation expenses. Although there are numerous examples of the VRPTW being applied in various fields, the practical application of the helicopter dispatching problem after a disaster presents unique challenges. As research on VRPTW progresses, neural networks have emerged as a new direction for solving this problem. [PERSON] et al. [32] proposed a pointer neural network to address VRPTW, utilizing a global attention mechanism to improve convergence speed and results. [PERSON] et al. [33] trained an artificial neural network to predict costs, i.e., distance and time, in VRPTW. In summary, research on VRPTW remains as a key topic of discussion, with significant implications for various fields. However, few studies have explored the application of VRPTW to post-disaster rescue helicopter routing. In addition, current post-disaster rescue strategies predominantly focus on vehicular methods, often overlooking the critical role of helicopters in rescue operations. Therefore, it is necessary to design unique algorithm strategies tailored to the characteristics of helicopter rescue missions. Although substantial progress has been made in VRP research over the past few decades, it remains an open problem.