# Relief Aircraft Dispatch Strategies Based on Different Levels of Information Sharing Systems
## 2 Literature Review  
Our research focuses on air relief activities and establishing a model that can employ different transportation strategies to dispatch aircraft based on demand requests. Hence, we refer to research and methods for real-time dispatching and route planning or dynamic route planning. In dealing with real-time dispatchment questions, some researchers use Markov decision processes (MDPs). To improve the computational efficiency, [PERSON] et al. [7] improved the traditional one-step rollout policy in the MDP by developing a pre- and post-decision state. Concerning the allocation question of emergency medical vehicles, [PERSON] et al. [8] formulated an MDP model that dynamically determines the type of vehicle(s) to be dispatched. Aiming at the military medical aircraft dispatchment question, [PERSON] et al. [9] presented an MDP model to optimize sequential resource allocation decisions under demand uncertainties (severity, number, and location) and service times. Several MDP models have been presented for dispatching emergency management system assets to spatially distributed patients that maximizes the fraction of patients who responded within a fixed time frame [10; 11; 12]. Other researchers have used the framework of agent-based simulation to manage real-time dispatchment questions in different fields. This framework is widely used in researching autonomous vehicles (AVs), taxis, and rideshares. [PERSON] et al. [13] utilized the framework of an agent-based simulation to model different agents, such as autonomous vehicles, travelers, and intelligent fleet operators, to compare different AV-traveler assignment strategies (control policies). Agent-based models have been designed and applied to simulate the performance and estimate the potential benefits of a sharing AV (SAV) system with dynamic ridesharing [14; 15]. They used an agent-based simulation model to estimate different system operation scenarios to investigate the potential impact of the SAV system on urban parking demand. [PERSON] et al. [16] designed an agent-based simulation to investigate the effect of combing ridesharing programs and AVs on the total fleet size, travel distance, and emissions, similar to [PERSON] et al. [17]. Regarding taxi dispatchment, [PERSON] et al. [18] developed a tool to understand the behavior of taxi markets to policy regulations and support decision-makers using this framework. [PERSON] et al. [19] simulated the situation of electric taxicab fleets in an inner-city. [PERSON] et al. [20] proposed and evaluated efficient real-time taxi dispatching strategies using an agent-based simulation. Similar models have also been proposed in the literature [21; 22; 23]. From the analysis of the literature, MDP has an advantage in the sequential resource allocation question, whereas agent-based simulation is good at comparing different strategies using different indexes. Our research is aimed at comparing various transportation methods under different information and communication systems. Hence, we select the agent-based simulation framework, which can easily calculate different indexes and combine different routing planning methods that can solve the local routing problem repeatedly. To propose a dynamic flight method, we refer to a similar model used in taxi-dispatching or ambulance fleet management in this section. Regarding the taxi dispatchment, [PERSON] et al. [24] assumed that customer itineraries may change during the execution of the planned delivery schedule and then proposed an iterative solution framework in which an active delivery schedule is re-optimized whenever a customer itinerary update is revealed. [PERSON] et al. [25] presented a new heuristic algorithm for assigning taxis to customers; the algorithm considers taxi reassignments, which may lead to globally better solutions, and an economic compensation scheme, to incentivize private drivers to agree with the proposed modifications in their assigned clients. [PERSON] et al.