An application of adaptive large neighborhood search for pickup and delivery problem with time windows (PDPTW): A case of food delivery service provider

Abstract

Food“delivery service nowadays plays a crucial part in people's lives. It helps people save time, effort, and money. However, because the demand for home delivery services is increasing, fast delivery companies are increasing, so the industry is extremely competitive. In order to be competitive in this service industry, the quality of service in terms of delivery time, as well as optimal operation, will help businesses to survive and thrive. Therefore, this study focuses on applying the model and Adaptive Large Neighborhood Search (ALNS) algorithm to solve the Pickup and Delivery Problem with Time Windows for food delivery service problems by using Python language. The result shows that the algorithm outperforms the model from the optimal objective to the computing time of about 90%, which is essential for the on-demand food delivery service because, in this industry, timing is critical to providing customer satisfaction and service rate re-use. On the other hand, it has been demonstrated that time window variations and the number of drivers allocated to deliver orders are highly associated; the shorter the time windows, the more drivers will be assigned. This follows the same pattern when compared with different drivers' maximum capacity.