| dc.description.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. | en_US |
