Lean Logistics Of Baiyin City Tobacco Company

Logistics cost management as a core part of enterprise logistics management can not only help them to reduce cost and improve management efficiency and economic benefit, but it can also enhance the enterprise market competitiveness. Due to the presence of the monopoly system of national tobacco, and as typical logistics distribution tobacco commercial enterprises, it has characteristics different from others. However, the logistics cost management is still important part of tobacco commercial enterprise. Pull tobacco logistics scheduling method,which regards customer demand services as the core,can help to achieve the effective integration and reasonable configuration of logistics resources, and greatly improve the logistics efficiency and service quality of the tobacco commercial enterprises. Presently,domestic study in this area is not full, there is still a big research space. This paper will study on tobacco logistics distribution scheduling integrated optimization method based on the actual needs of tobacco logistics distribution in Baying Gansu Province.The main research contents and results include six parts:The first part is an introduction, including research background significance, as well as Current research, and the contents of this paper to solve analysis and research ideas.The second part is about the analysis and an overview of the theoretical foundation of lean logistics Tobacco logistics scheduling problems, mainly introduces the theory of lean thinking principles of lean production. And focus on the analysis and elaboration of the basic status of China's tobacco logistics, development status and problems in the basic theory of lean logistics.(3)The third part is the implementation of lean logistics dispatch method of tobacco. Mainly from the description and classification, lean logistics problem elements; many aspects of the basic framework, lean logistics application of modern management system to realize the architecture analysis and implementation of methods of analysis.(4)In the fourth part, put forward the algorithm of the model of comprehensive optimization of tobacco logistics distribution scheduling, lean K-MEANS clustering algorithm, ant colony algorithm. The K-MEANS clustering algorithm is adopted, according to the retailer's geographic location and demand, and truck cargo capacity limit test, will retail clients classification, each type of packet is departure from the marketing department. Capacity according to the customer the total demand and truck, determined from the marketing department at least need to start several times, that is, retailers need to several groups. A clustering algorithm in the classification of the each group, through the ant colony algorithm to find approximate optimal path and the total distance, according to the speed and each retailers Business Hours, that each packet is needed for total delivery time, then according to the delivery member the job time reasonable arrangements for each delivery man need distribution which group.(5)In the fifth part, taking the Baiyin district tobacco marketing distribution scheduling service integrated optimization of tobacco companies under the jurisdiction of Baiyin as an example, using the K-MEANS algorithm and genetic algorithm, calculate the distribution vehicle number needed, and distribution lines per car.(6)The sixth part is the conclusion, mainly expounds the research results of this paper and the innovative points, writing ideas and future research prospects of this kind of problem.In this paper, the K-MEANS clustering algorithm and genetic algorithm combining tobacco lean logistics scheduling optimization method is put forward, which is a more practical and efficient method to reduce the logistics costs of tobacco circulation enterprises, improve the quality of logistics services, and enhance the competitiveness of enterprises, which is an important reference not only for the reasonable implementation of tobacco logistics distribution scheduling, but also for the scheduling optimization of other industries.