## 2 Literature Review  
The VRP, a nondeterministic polynomial time hardness (NP-hardness) problem [12], refers to an optimization problem related to routing management, first devised by [PERSON] and [PERSON] [5] in 1959. It started as a simple problem of determining the optimal routes for a fleet of vehicles dispatching from a single terminal to multiple variants, including ones that limit vehicle capacity and delivery time, have multiple terminals or allow the customer to select a delivery option. Obtaining the exact optimal solution for the VRP is possible if the customer nodes are not too large for a mathematical solver. However, real VRP applications tend to have a substantial number of customer nodes. Therefore, heuristic methods are often chosen to solve the VRP [13]. There are several studies that focused on the VRP of post office deliveries with different strategies, objectives and constraints. [PERSON] et al. [14] conducted an experiment to solve the multi-objective VRP with only one constraint--the route distance--using a large number of geographical delivery locations (~30,000 points) in Autur Nogueira, Brazil. [PERSON] et al. [9] tried to solve the VRP for a post office in which each customer could select a choice of vehicle routing problem delivery options (VRPDOs) with varying places and times, such as receiving parcels at home in the evening or at work during office hours. In addition, the VRP with a time window (VRPTW) was investigated by [PERSON] et al. [15], who developed an optimization model for a network of post offices with aims to minimize the total operational cost within time constraints (i.e., office working hours). Various algorithms were employed to solve the VRP and its variants. Most are metaheuristic, including differential evolution (DE) and particle swarm optimization (PSO). Other metaheuristic algorithms such as the large neighborhood search (LNS) [16] and hybrid intelligent algorithms [17] were also adopted for the VRP. DE is a population-based optimization method that is similar to other evolutionary algorithms in terms of conventional approaches (e.g., mutation and cross-over operations). However, it utilizes a less stochastic and greedier solving strategy [18]. DE has been successfully applied to many VRP studies. [PERSON] et al. [19] employed DE to find solutions to the stochastic VRP with simultaneous pickup and delivery by using actual bus service data in Anbessa, Ethiopia. Their objective was to minimize both the total travel distance and number of vehicles, and the algorithm yielded superior performance compared with that of a traditional savings algorithm. [PERSON] [20] utilized DE (hybridized with a genetic operator) for a multi-trip VRP with backhauls and a heterogeneous fleet (MTVRPB). They devised an optimization method specifically for a company that needed to retrieve glass bottles (soft drinks) back from clients after delivery with a single objective (i.e., minimizing the total distance traveled). [PERSON] et al. [18] used both DE and genetic algorithms to find an optimal solution to the VRP with simultaneous delivery and pick-up and time windows (VRP-SDPTW). They built a mathematical model using mixed integer programming for their proposed VRP variant and yielded satisfying optimized results. [PERSON] et al. [21] applied a hybrid discrete differential evolution (HDDE) to the split delivery VRP (SDVRP). The authors focused on vehicle routing management to deliver large supplies of hospital items split between multiple unmanned transport vehicles--a suitable solution in the pandemic era--with the objective of minimizing the total distance traveled, and the proposed HDDE algorithm produced superior performance compared with the forest-based tabu search (FBTS) and genetic algorithm (GA) in many problem instances. There are also studies that applied DE and local search to solve the VRP. For instance, [PERSON] and [PERSON] [22] proposed a DE algorithm integrated with local search to solve the capacitated VRP.