An Advanced System to Enhance and Optimize Delivery Operations in a Smart Logistics Environment

Abstract

Optimization of order dispatch operations and delivery time prediction is a major concern in supply chains, mainly for e-commerce, which requires the implementation of advanced solutions to reduce delivery time, minimize costs and maximize customer satisfaction. In practice, they fail to warrant scalable and sustainable solutions as the numbers of orders become larger. For that, proper prediction and optimization for delivery operations are required for optimal logistics management. This paper presents an advanced logistics service, which warrants dynamic coordination among all the actors in the smart logistics environment. The proposed advanced shipping system consists of two main parts: the delivery prediction model to compute the expected arrival time, and a hybrid optimization model to tackle path issues. We demonstrate that the advanced system consistently outperforms conventional standard dispatching methods, which means that the proposed approach effectively contributes to optimizing the distribution chain and reducing costs.

Keywords

• Smart logistics
• deep learning
• LSTM
• multi-objective optimization
• intelligent systems