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Optimization Of The Delivery For Electricity Supplies Based On Internet Of Things

Posted on:2016-06-25Degree:MasterType:Thesis
Country:ChinaCandidate:X Y LiFull Text:PDF
GTID:2309330470471251Subject:Logistics engineering
Abstract/Summary:PDF Full Text Request
It is of great significance of improving the logistic distribution efficiency to reduce logistic costs and increase economic benefits. However, viewing the current states of the logistic management of electric power and material distribution, there are still high inventory costs, not timely delivery, the low level of information technology, the poor quality of logistics personnel, and so on, which have adverse effect on normal production of electricity enterprises. Therefore, the power companies start to attach great importance to the management of electricity supplies, enhance the material distribution model and optimize and adjust the management processes.With the wide use of the internet of things this advanced network technology in produce and life of the world, domestic electricity companies are developing to the direction of information technology and intensive management, trying to optimize and improve the electric supplies distribution by combining the technology of the internet of things on the basis of traditional material distribution model. So as to saving operating costs, and enhance the competitiveness of enterprises by improving the efficiency of the enterprise.The purpose of this paper is to solve the problems existed in the distribution of electricity supplies. Then combine the technology of the internet of things to build an optimization model of power supplies distribution, aimed to saving the costs of material distribution after meeting the needs of normal production and achieving the best economic benefit.Finally, combined with the actual data of a project, using genetic algorithms to solve and simulate the model, so as to do financial analysis of the above model.
Keywords/Search Tags:Internet of things, Power supplies, route optimization, genetic algorithms
PDF Full Text Request
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