考慮時間懲罰成本和關(guān)稅成本的跨境電商海外倉選址研究
發(fā)布時間：2019-03-10 21:36
【摘要】：隨著全球經(jīng)濟一體化進程的不斷發(fā)展和信息技術(shù)的日新月異,跨境電子商務(wù)發(fā)展迅速。相對于發(fā)達國家而言,我國物流業(yè)比較落后,現(xiàn)有的物流配送模式跟不上業(yè)務(wù)發(fā)展需要。近幾年,海外倉的出現(xiàn)成為促進跨境電子商務(wù)快速發(fā)展的一大推手,其適用范圍極廣,而且海外倉的大量使用可攤薄企業(yè)物流成本。但是,海外倉建設(shè)的主要問題是租用或自建海外倉成本較高,容易產(chǎn)生貨品積壓,這對企業(yè)貨品需求預(yù)測和對海外倉的選址決策都提出了更高的要求。本文以中國跨境電子商務(wù)企業(yè)為研究對象,根據(jù)海外倉的實際建設(shè)需求,從市場需求等角度考慮,在國內(nèi)外選址研究的基礎(chǔ)上,考慮海外倉建設(shè)費用,因是否建設(shè)海外倉而產(chǎn)生的運輸方式不同引起的運輸成本的差異,空間運輸距離,超出顧客要求的時間而產(chǎn)生的時間懲罰成本等因素,建立考慮時間懲罰成本的海外倉選址模型。再從實際出發(fā),因海外倉跨庫運輸涉及關(guān)稅成本問題,因此在考慮時間懲罰成本的基礎(chǔ)上,加上考慮關(guān)稅的影響,最終建立考慮時間懲罰成本和關(guān)稅成本的數(shù)學(xué)模型。通過對模型特點進行分析,運用遺傳算法、粒子群算法和遺傳粒子群混合算法對目標(biāo)函數(shù)進行求解,用MATLAB軟件得以實現(xiàn)。最后,通過案例仿真證明模型是有效可行的。從成本效率、時間效率和穩(wěn)定性三方面對遺傳算法、粒子群算法和遺傳粒子群混合算法的運算結(jié)果作對比,可得遺傳粒子群混合算法運算效果比前兩種算法要好,能更有效降低企業(yè)成本。本文的創(chuàng)新點是根據(jù)跨境電商海外倉的建設(shè)要求,從實際出發(fā)考慮時間懲罰成本和關(guān)稅成本建立數(shù)學(xué)模型,并通過設(shè)計遺傳算法、粒子群算法和遺傳粒子群混合算法對模型進行求解,通過對比證明遺傳粒子群混合算法更優(yōu)。從而為跨境電子商務(wù)企業(yè)建設(shè)海外倉提供科學(xué)的方法和依據(jù),提高企業(yè)運營效率和競爭力,促進國際經(jīng)濟貿(mào)易的發(fā)展。
[Abstract]:With the continuous development of global economic integration and the rapid development of information technology, cross-border e-commerce develops rapidly. Compared with developed countries, China's logistics industry is relatively backward, the existing logistics distribution mode can not keep up with the needs of business development. In recent years, the emergence of overseas warehouse has become a big push to promote the rapid development of cross-border e-commerce, which has a wide range of applications, and a large number of use of overseas warehouse can dilute the logistics cost of enterprises. However, the main problem of overseas warehouse construction is that the cost of renting or self-building overseas warehouse is high and it is easy to produce a backlog of goods, which puts forward higher requirements for forecasting the demand for goods of enterprises and making decisions on the location of overseas warehouses. In this paper, Chinese cross-border e-commerce enterprises as the research object, according to the actual construction needs of overseas warehouse, from the point of view of market demand, on the basis of domestic and foreign siting research, consider the overseas warehouse construction costs, Factors such as the difference in transportation costs caused by the different modes of transportation resulting from the construction of overseas warehouses, the distance of space transportation, the time penalty costs incurred in excess of the time required by the customer, and so on. An overseas warehouse location model considering time penalty cost is established. On the basis of considering the time penalty cost and the influence of the tariff, the mathematical model considering the time penalty cost and the tariff cost is set up at the end of the paper, which is based on the consideration of the time penalty cost and the effect of the tariff on the cross-storeroom transportation of the overseas warehouse. By analyzing the characteristics of the model, using genetic algorithm, particle swarm optimization and genetic particle swarm optimization hybrid algorithm to solve the objective function, using MATLAB software to achieve. Finally, the simulation results show that the model is effective and feasible. The results of genetic algorithm, particle swarm optimization and genetic particle swarm optimization are compared from three aspects of cost efficiency, time efficiency and stability. The results show that the genetic particle swarm optimization algorithm is better than the first two algorithms, and that the genetic particle swarm optimization algorithm is more effective than the first two algorithms in terms of cost efficiency, time efficiency and stability. Can reduce the cost of the enterprise more effectively. The innovation of this paper is to set up a mathematical model of time penalty cost and tariff cost according to the construction requirements of cross-border e-commerce overseas warehouse, and design the genetic algorithm through the design of genetic algorithm. Particle Swarm Optimization (PSO) and genetic Particle Swarm Optimization (GPSO) are used to solve the model, and it is proved that the genetic PSO hybrid algorithm is better than the genetic PSO hybrid algorithm. In order to provide scientific methods and basis for cross-border e-commerce enterprises to build overseas warehouse, improve the efficiency and competitiveness of enterprises, promote the development of international economic and trade.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F724.6;F125
本文編號：2438053
[Abstract]:With the continuous development of global economic integration and the rapid development of information technology, cross-border e-commerce develops rapidly. Compared with developed countries, China's logistics industry is relatively backward, the existing logistics distribution mode can not keep up with the needs of business development. In recent years, the emergence of overseas warehouse has become a big push to promote the rapid development of cross-border e-commerce, which has a wide range of applications, and a large number of use of overseas warehouse can dilute the logistics cost of enterprises. However, the main problem of overseas warehouse construction is that the cost of renting or self-building overseas warehouse is high and it is easy to produce a backlog of goods, which puts forward higher requirements for forecasting the demand for goods of enterprises and making decisions on the location of overseas warehouses. In this paper, Chinese cross-border e-commerce enterprises as the research object, according to the actual construction needs of overseas warehouse, from the point of view of market demand, on the basis of domestic and foreign siting research, consider the overseas warehouse construction costs, Factors such as the difference in transportation costs caused by the different modes of transportation resulting from the construction of overseas warehouses, the distance of space transportation, the time penalty costs incurred in excess of the time required by the customer, and so on. An overseas warehouse location model considering time penalty cost is established. On the basis of considering the time penalty cost and the influence of the tariff, the mathematical model considering the time penalty cost and the tariff cost is set up at the end of the paper, which is based on the consideration of the time penalty cost and the effect of the tariff on the cross-storeroom transportation of the overseas warehouse. By analyzing the characteristics of the model, using genetic algorithm, particle swarm optimization and genetic particle swarm optimization hybrid algorithm to solve the objective function, using MATLAB software to achieve. Finally, the simulation results show that the model is effective and feasible. The results of genetic algorithm, particle swarm optimization and genetic particle swarm optimization are compared from three aspects of cost efficiency, time efficiency and stability. The results show that the genetic particle swarm optimization algorithm is better than the first two algorithms, and that the genetic particle swarm optimization algorithm is more effective than the first two algorithms in terms of cost efficiency, time efficiency and stability. Can reduce the cost of the enterprise more effectively. The innovation of this paper is to set up a mathematical model of time penalty cost and tariff cost according to the construction requirements of cross-border e-commerce overseas warehouse, and design the genetic algorithm through the design of genetic algorithm. Particle Swarm Optimization (PSO) and genetic Particle Swarm Optimization (GPSO) are used to solve the model, and it is proved that the genetic PSO hybrid algorithm is better than the genetic PSO hybrid algorithm. In order to provide scientific methods and basis for cross-border e-commerce enterprises to build overseas warehouse, improve the efficiency and competitiveness of enterprises, promote the development of international economic and trade.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F724.6;F125
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