Optimization of route distance using k-NN algorithm for on-demand food delivery
Keywords:crowd-sourcing, hybrid optimization on-demand food-delivery, k nearest neighbor algorithm (KNN), route optimization, traditional search (ts), urban logistic
Customers are now more able to purchase goods over the phone or the Internet, and the ability for those purchases to be delivered safely to the customer’s location is proliferating. On-request meal delivery, where customers submit their food orders online, and riders deliver them, is growing in popularity. The cutting-edge urban food application necessitates incredibly efficient and adaptable continuous delivery administrations toward quick delivery with the shortest route. However, signing up enough food parcels and training them to use such food-seeking frameworks is challenging. This article describes a publicly supported web-based food delivery system. IoT (Internet of Things) and 3G, 4G, or 5G developments can attract public riders to act as publicly sponsored riders delivering meals using shared bikes or electric vehicles. The publicly funded riders are gradually distributed among several food suppliers for food delivery. This investigation promotes an online food ordering system and uses K-Nearest Neighbor calculations to address the Traveling Salesman Problem (TSP) in directing progress. The framework also uses the Global Positioning System (GPS) on Android-compatible mobile devices and the TOM-TOM Routing API to obtain coordinates for planning purposes. To evaluate the presentation of the proposed approach, recreated limited scope and certifiable enormous scope on-request food delivery occurrences are used. Compared to the conventional methodology, the proposed strategy reduces the delay time. Each rider will receive the most direct route to the order delivery address. The delivery delay time is reduced by approximately 10–15 minutes for every order. The food supplier can determine whether an item is available to the rider; thus, the food supplier can add an order to the rider having the shortest way.
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