@article{Arree_Klinaubon_Kornphan_Rattananonsatien_Khotchasit_2022, place={Songkhla, Thailand}, title={Solving Vehicle Routing Problem: A Case Study of ABC Drinking Water Company Limited}, volume={14}, url={https://so01.tci-thaijo.org/index.php/ecbatsu/article/view/250661}, abstractNote={<p>ABC Drinking Water Co., Ltd. is one of the water drinking companies in Nakhon Ratchasima province. The company encountered the problem of goods transportation as the new freight transport employees could not effectively manage the vehicle routing, resulting in the reverse transport route over the original route. This problem affected the increase in transportation costs. This research aims to optimize the vehicle routing for drinking water transportation by solving vehicle routing problems with four methods as the Saving Algorithm method, the Nearest Neighbor Algorithm method, Microsoft Excel Solver program, and VRP Spreadsheet Solver program. The collection of routes and location coordinate data was for 20 delivery rounds of drinking water with the A-GPS tracker application and created the transport routes by the Google Maps program. The result showed that the mean distance before vehicle routing was 21.56 ± 8.86 km. The mean distance from the Microsoft Excel Solver was the shortest, about 20.98 ± 8.83 km (decreased 2.69%). While the vehicle routing from Saving Algorithm, the Nearest Neighbor Algorithm, and the VRP Spreadsheet Solver affected the increase of the transportation distance that the average distance of them was 22.06 ± 8.90 km (increased 2.32%), 21.92 ± 9.61 km (increased 1.67%), and 22.15 ± 9.23 km (increased 2.74%), respectively. However, the average distance before vehicle routing and the average distance obtained from four vehicle routing methods were not statistically different (P>0.05). The vehicle routing of water drinking transportation in this research could be used to plan for goods transportation and create the work instruction for transport employees to be the standard operation.</p>}, number={3}, journal={Economics and Business Administration Journal Thaksin University}, author={Arree, Worapon and Klinaubon, Phanuphong and Kornphan, Sippakorn and Rattananonsatien, Thanapat and Khotchasit, Chirattikan}, year={2022}, month={Jun.}, pages={1–24} }