Optimization Analysis of the Emergency Logistics Identification Method Based on the Deep Learning Model under the Background of Big Data

Abstract

Vehicle routing, which is effective and efficient, is a dominant aspect of supply chain management in general and deep learning (DL) in particular. It is also a right step towards the fuel conservation and environmental concern in disposing used commodities. Economics of logistics and transportation plays a major role in deciding the competitiveness of the product, either new or used, in the market. With the upward trends of fuel and logistics costs, manufacturing industries have little option other than keeping the cost of transportation the lowest. Many organizations now started implementing lesser expensive and proper transport modes to keep the maintenance of supply chain cost to the minimum. Proper handling of returned commodities to recover value without affecting the environment may need appropriate techniques or methodologies. This paper deals with the routing of vehicles with energy conservation as the agenda in the value recovering method named as repair service work. It is done through a big data-based deep learning model, in a multicommodity environment. Here, the transportation of commodities to repair service facilities is given an in-depth focus to reduce the energy use. The minimization of the distance traveled by the truck fleet reduces the energy consumption by the trucks. This article deals with the optimization of emergency logistic with the assistance of deep learning approach, whereas this approach attains 57.19 km with 6 optimized routes. The process of emergency flow control is attained effectively using deep learning approach.