###### Abstract  
Generally, transportation costs account for approximately half of the total operation expenses of a logistics firm. Therefore, any effort to optimize the planning of vehicle routing would be substantially beneficial to the company. This study focuses on a postman delivery routing problem of the Chiang Rai post office, located in the Chiang Rai province of Thailand. In this study, two metaheuristic methods--particle swarm optimization (PSO) and differential evolution (DE)--were applied with particular solution representation to find delivery routings with minimum travel distances. The performances of PSO and DE were compared along with those from current practices. The results showed that PSO and DE clearly outperformed the actual routing of the current practices in all the operational days examined. Moreover, DE performances were notably superior to those of PSO. p 2021 11 2021 14 March 2021  
involved in deploying such systems. Nevertheless, relying on skills and experience alone might not be suitable for a problem with exceptionally high complexities. The VRP is one of the optimization problems that seeks optimized routing for vehicles that traverse either to deliver or pickup goods. An approach to finding a solution to the VRP might be to determine a set of routes having a minimized total distance traveled by all the vehicles involved [5]. Other VRP variations include enforcing the total delivery time to be within a certain time window [6], limiting vehicle capacity [7], having stochastic customer demands [8] or allowing customers to select their delivery options [9]. Generally, the problem sizes (such as number of vehicles and customer nodes) of the VRP that can be solved for exact optimized solutions by mathematical methods are limited [10], due to the inherent complexity of the VRP. However, actual VRP applications usually have large problem sizes, containing approximately hundreds or thousands of vehicles and customers (e.g., a beverage firm that has to deliver millions of bottles per day to a large number of depots and small businesses). Therefore, solving for optimized solutions by mathematical procedures alone is impractical for most applications. As a result, heuristic and metaheuristic optimization algorithms are usually selected as a pragmatic method to solve the VRP, since they can obtain feasible solutions relatively fast and the quality of those solutions is decent enough for real uses [11]. This study investigated the postman operations of the Chiang Rai post office, which is a logistics establishment that does not apply any VRP optimization techniques, and its delivery drivers have to rely on their own expertise and plans in order to traverse between customer locations. The routing of two delivery vehicles was tracked, in which they dispatched parcels to customers in their responsible areas for 50 operational days. The routing information consisted of traveled paths, distance covered and the actual geographic coordinates of customer locations which the vehicles visited each day. Then, we performed the routing optimization procedures based on those customer coordinates, using the metaheuristics algorithms for all the operational days of those delivery vehicles. Since the delivery routes depended on the skills and experience in the area of their drivers, those routes might not be well optimized. Moreover, if new drivers were to replace the previous ones, they would have to learn anew and gain enough experience to better traverse the area. Thus, the efficiency of the delivery process was not consistent. As such, this research paper was focused on providing a smart routing decision, solving for VRP solutions such that the delivery performance could improve remarkably and be more consistent. Two metaheuristic algorithms were applied--particle swarm optimization (PSO) and differential evolution (DE)--and integrated with local search techniques.