DROP-OFF PREDICTION MODELS AND ROUTE OPTIMIZATION FOR COST-EFFECTIVE DELIVERY OPERATIONS

The role of integrating drop-off prediction models with route optimization approaches in improving the cost-efficacy of delivery processes is an area examined in this paper. The experimental results obtained from deep learning along with evolutionary algorithms in this paper point towards strong evidence that there are significant enhancements in fare and cost effective routes. As such advanced techniques, they might bring about a positive impact to the extent that the delivery of logistics may be made in an environmentally friendly and economically efficient manner.