## 2 Literature  
DRTs are door-to-door transportation services that provide dial-a-ride pickup/delivery services [9; 10; 11]. They have often been described as an extension of two fundamental vehicle routing and scheduling problems [12; 13], in which the vehicle routing problem (VRP) assigns some vehicles to visit a set of geographically dispersed locations, while the pickup and delivery problem (PDP) moves the number of goods from certain pickup locations to certain delivery locations. However, there are also distinct differences between the VRPs and PDPs discussed so far and DRTs. VRPs and PDPs focus on the transportation of goods, while DRTs are concerned with the transportation of people. Thus, DRTs address more additional issues and are more complex than VRPs and PDPs. Most reviews with DRTs are related to service and passenger convenience [14; 11]. Since FRTs are not efficient due to low population density, DRTs perform better in these scheduling areas, especially those with a weak transportation infrastructure [15]. In general, passengers are picked up and delivered in special time windows, resulting in the VRP with time windows (VRPTW) and PDP with time windows (PDPTW)problems [16]. Ina recent review of VRPTWs and PDPTWs, another variant was found when the vehicle can immediately undertake another route after returning to the depot, which is referred to as the vehicle routing problem with multiple use of vehicles [8; 17; 18]. Although DRTs with multiple use of vehicles are more realistic, the main challenge in addressing multi-vehicle compared with single-vehicle DRTs involves the inability of direct applications of the solution techniques for the single use of vehicles case to search the solution space efficiently for multi-vehicle problems. Another variant arises when vehicles can return to any of these depots after picking up passengers when there are multiple depots [19; 20; 21]. DRTswith multiple depots are more complex than traditional ones since vehicles from different depots have different travel costs related to the distance from each depot, thus affecting the routing of all vehicles. Therefore, it is very important to choose optimal depots for each vehicle to reduce the operation costs in the problem. Other relevant studies on DRTs address the coordination between design of the passenger rail service, station spacing, the design of bus routes, and headways using integrated, robust, and bi-level programming optimization [22; 23; 24; 25]. The above varieties of DRTs are addressed from various objectives, including minimizing the operating costs of the fleet [3; 11; 26], the shortest length or travel time of the route [27; 28], the minimal fleet size required for shuttle service [1; 22], ride time, and waiting time of passengers [4; 15]. Fortunately, these objectives normally do not affect the properties of DRTs and, thus, similar models and algorithms can be adopted to solve the problem with different objectives. As the problem belongs to NP-hard problems, heuristics are most frequently used to handle a large number of vehicle routing and scheduling variants for DRTs [29; 30; 31]. Some scholars have tried to develop route-building heuristics by first generating a set of feasible routes, before searching fine-tuned initial solutions [3; 4; 28; 32]. Furthermore, these developed route-building-based heuristics, which are the three most widely used metaheuristics, are often adopted to contend with VRPTW, including evolutionary algorithm [26], genetic algorithms [27; 32; 33; 34; 35] and table search [36; 37; 38; 39]. Although most of the aforementioned studies have successfully handled a variety of DRTs, the following critical issues deserve further investigation:  
1. Traditional DRTs only consider passengers' single time windows and few of them take multiple time windows into account. This implies a lack of integrated operation to guide passenger boarding in the specified time periods from several preferred time windows and routing of transit from selected demand points to destinations. 2.