The Research Of Refined Oil Transportation Optimization Based On The Internet Of Things

ABSTRACT: In March2012, refined oil price was upward again, and affected the associated commodities, and the accidents in transportation of refined oil continue to occur. Facing this status quo, oil enterprises can reduce cost in order to survive and a large part of the cost of finished oil origins from logistics process, such as storage, transport and others. So in this increasingly fierce competitive environment, only the enterprises may have the competitiveness in oil product market and profit space.only by optimizing these specific aspects of logistics. Refined oil storage is now the main depot and the difficulty of optimization in storage is high and has the limitation. So this paper focused on the transportation optimization, the optimization will mainly involves the whole transportation process in the safety, reliability and reducing transportation cost and rescue and other problems after the accident. In transportation means, the achievement of distribution system is difficult, so this article takes a means transforming from the traditional process to the distribution system, which is the oil transported to sales store and gas station transport according to the actual situation, and sales store can also serve some demand for small gas station. In this means, the paper is designed to better monitor the transportation process, collect data, reduce the occurrence of accidents, rescue in time and optimize route through implementing Internet of things technology in a base station (control point) to the,finally achieving the safety and high efficiency of transportation. The base station (control points) requires the largest coverage and shortest distance of each demand point weighted, and then establishes the stochastic programming model, and solves it by genetic algorithm. After the establishing of the base station, decision points use the Internet of things technology at the same time to obtain the data to correct classical path choice model, at last getting reasonable transportation path optimization results. In the paper, there are15Figures,8Tables,51References.