## 2 Literature Review
[19] considered the VRPTW with two objectives: minimizing total cost in a fuzzy random environment and maximizing customer average satisfaction. They proposed the Global-local-neighbor particle swarm optimization with exchangeable particles (GLNPSO-ep) method. The GLNPSO-ep method was applied to the transportation of materials for an actual hydroelectric power construction project in China, and as a result, the cost was greatly reduced, without reducing customer satisfaction. [PERSON] et al. [20] proposed VRPTSW taking into account transportation costs (total distance, number of vehicles used, and driver's total expected overtime) and service costs. The travel time of each vehicle was considered using a stochastic method. To solve the proposed problem, they started with the initial solution and derived the optimal value through the Tabu search algorithm method. [PERSON] et al. [21] analyzed VRPTSW using the meta heuristic method of an artificial bee colony (ABC). This approach was taken to minimize travel distance, number of vehicle and time violation. The proposed Bee_VRPTSW method showed good quality solutions and calculation time. This methodology suggests that various vehicle routing problem (VRP with Hard Time Windows, Heterogeneous Fleet VRP, Fleet Size and Mix Vehicle Routing Problem with TimeWindows) can be utilized. [PERSON] et al. [22] considered the semi-soft time window constraint, a way to confer a penalty when arriving late and no penalty for early arrival. They compared mixed integer programming (MIP), a genetic algorithm, and a simulated annealing algorithm. The MIP model was good for small sizes; it was suggested that the large size of the VRPSTW, the better in terms of quality and CPU time of the simulated annealing (SA) algorithm. [PERSON] [23] applied a penalty cost if the service time starts early or starts late. They proposed the new iterative route construction and improvement (IRCI) method and applied [PERSON]'s benchmark problems for VRPSTW. [PERSON] et al. [24] aims to find a robust solution that offers the best performance in the worst-case scenario of VRPSTW under these uncertain travel times. As a result, uncertainty has the greatest effect on finding the VRPSTW optimal value and demonstrated that robust deviation and relative robustness are good consideration criteria. In contrast to VRPTW, it has been considered in terms of a simultaneous delivery and pick-up service when visiting customers, not considering the visiting time for VRPSDP, which was first introduced by [PERSON] [25]. [PERSON] et al. [26] considered problems with heterogenous VRP with multiple delivery and pick-up (HVRPSDP). They developed the HLS algorithm by applying simulated annealing (SA) and Tabu search (TS) to solve the problem, and proposed that a strategy to relocate goods in a vehicle can reduce transportation costs for VRPSDP. [PERSON] et al. [27] studied a vehicle routing problem with multiple delivery and pickup, time windows, and three-dimensioning loading (3L-VRPDPTW) that take into account the volume of backhaul products collected when the vehicle visits a line haul node. To solve the problem of 3L-VRPSDPTW, they applied the heuristic algorithm of a Local Search Open Space (LS_OS) and a Local Search Deepest Bottom left fill (LS_DBLF) a deepest-bottom-left-fill and ALNS. [PERSON] et al. [28] studied the VRPSDP model taking into account the set-up cost, distance cost, and penalty cost for violating time windows of the vehicle, they designed a two-step approach to solve the problem. The first step was to apply the Improved Genetic Algorithm (I-GA) heuristic to minimize the set-up and distance costs for the vehicle, and Improved Genetic Algorithm2 (I-GA2) to find the global optimum through I-GA. Most studies related to VRP have presented derived models like VRPTW, VRPSDP, and VRPSDPTW by addressing several different conditions, such as time window constraints, violation penalties, the predefined visiting sequences, and the capacity of vehicles.