<![CDATA[The increasing number of MSMes and the high demand for fast delivery present challenges for logistics companies in managing efficient routes and courier placement. Without proper clustering, warehouse placement and picking points are often uncoordinated, causing delivery delays and increased operational costs. This research aims to group MSMe and warehouse locations based on distance, delivery volume and demand patterns to find optimal zones for courier placement, thereby increasing the efficiency of delivery routes, reducing operational time and costs, and improving the quality of delivery services. The courier clustering management application uses the Scrum method, with database design based on class diagrams and functional processes through use cases and activity diagrams. Admin maps delivery areas based on sub-districts and determines fixed prices for each regional cluster. The process of optimizing the clustering of MSMe locations in City Geographic data for new shipments is retrieved via the Google Maps API, sending the complete address to the Geocodes API endpoint. The clustering process uses the K-Means method to group MSMe locations based on geographic proximity. The test results show the efficiency of logistics delivery as well as cheaper and fairer rates thanks to the application of flat rates for all courier service users, both for long and short distances.]]>
Copyrights © 2024
