# Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand
## 2 Related Literature  
In this section, we briefly outline the existing literature that is most relevant to our work. Readers who require a more comprehensive summary and investigation can refer to the studies conducted by [PERSON] et al. (2016) and [PERSON] et al. (2017). [PERSON] and [PERSON] (2018) were the first scholars to propose the Flying Sidekick Traveling Salesman Problem (FSTSP) and establish a Mixed Integer Linear Programming (MILP) model, which laid the foundation for the drone-vehicle routing problem that has garnered considerable attention from both academia and industry. In this problem, a truck carries a drone to serve various customers. Each drone flight can only serve one customer. [PERSON] et al. (2019) solved the FSTSP problem by modifying the objective function from minimizing delivery time to minimizing delivery costs. They proposed two heuristic methods--TSP-Local Search (TSP-LS) and Greedy Rand Adaptive Search Procedure (GRASP)--to address this issue. [PERSON] et al. (2020) presented the traveling salesman problem with drone (TSPD) that confined the drone retrieval node to the same location as the drone launch node. They designed a hybrid algorithm combining local search and dynamic programming to solve the problem. Meanwhile, [PERSON] et al. (2021) considered conducting along-route operations in TSPD, implying that the implementation of launch and retrieve can be performed along the route segment to enable the drone to serve a wider area. [PERSON] and [PERSON] (2022) focused on discovering a better route based on TSPD to achieve a broader drone delivery area. They developed a nonlinear programming model and demonstrated its effectiveness. [PERSON] and [PERSON] (2023) devised a decomposition-based iterative algorithm for moderately scaled TSPD problems. [PERSON] and [PERSON] (2024) added drone stations to TSPD and proposed the traveling salesman problem with drone stations. [PERSON] et al. (2025) investigated the bi-objective TSPD problem and proposed an improved non-dominated sorting genetic algorithm to tackle this problem. Additionally, certain studies focused on exploring exact methods ([PERSON] and [PERSON], 2026; [PERSON] et al., 2027) and heuristic algorithms ([PERSON] and [PERSON], 2028; [PERSON] et al., 2029) for FSTSP/TSPD. [PERSON] et al. (2020) extended the FSTSP problem, proposed a novel variant of a truck carrying multiple drones--the multiple traveling salesman problem with drones--and established a mathematical model and heuristic algorithm for it. [PERSON] and [PERSON] (2011) conducted a thorough study of the Multiple Flying Sidekicks Traveling Salesman Problem (MFSTSP), incorporating the energy consumption model of drones as a function of package weight, speed, and operation time. They proposed a three-stage iterative heuristic algorithm that can handle instances with up to 100 customers. Moreover, [PERSON] et al. (2020) proposed truck-assisted multi-drone package delivery and provided a hybrid genetic algorithm to solve the problem. [PERSON] et al. (2020) proposed a novel MFSTSP problem of using a parking lot to assist with truck-drone package delivery services, thereby developing an alternative solution for FSTSP. [PERSON] et al. (2020), [PERSON] et al. (2020), [PERSON] et al. (2020), and others investigated exact and heuristic algorithms to solve the MFSTSP problem. The study by [PERSON] et al. (2020) extended the FSTSP problem by adding the factor that the drone can visit multiple customers in a single trip, that is, the truck-drone team logistics problem, and designed an iterative greedy heuristic to solve it. [PERSON] et al. [38] studied the multi-drone multi-visit traveling salesman problem and assumed that the energy consumption of drones is associated with the flight time, self-weight, and payload; they also proposed a multi-start tabu search to answer up to 100 customers' questions. [PERSON] et al.