Research On The Online Ordering Policy For Return And Resalable Product

Under the rapid development of e-commerce market environment, through the network channels to sell goods become more and more frequent, at present, the behavior of the customers to purchase goods on the internet has become a popular trend.However, some goods purchased through the network channels are often difficult to let customers fully satisfied because of the reason that customer can not direct perception;at the same time, China’s "consumer protection law" in order to ensure the rights and interests of customers, the provisions of the customer if the goods are not satisfied, can in the effective time return the goods unconditionally to the retailers, and because the return freight etc. borne by the merchants, therefore, online shopping resulted in return rate is greatly improved.In addition, if the goods are in good condition can be regaled by retailers. Based on the actual background, this paper studies the problem that how to order product for retailers with return and resalable background. Existing methods assume that the demand distribution information is known, but in reality due to customer preferences are difficult to predict, it is difficult to accurately describe the distribution information of demand, Once the assumption changes, the consequence of these methods will be far from the optimal solution. In recent years, research on online problem and its competitive strategy has gradually emerged, they can not assume any prior distribution information of demand, thus they can make up for the shortcomings of previous studies. In this thesis, using the research methods of online problem and competitive strategy to study the ordering problem under these background.The main research work and conclusions of this paper are as follows:First, Based on the online theory, combined with the reality, established the online decision model with return and resalable product, according to the characteristics of problem modifies the existing competitive ratio concept, put forward the concept of competitive measuring ratio, gives the corresponding optimal competitive strategies,and prove the optimal strictly competitive measure ratio, combined with example analysis study the effects of system parameters on the optimal ordering strategy.Second, In the current fierce market competition environment, in order to improve the sales of goods and the commodity market share, supplier will generally according to the retailer’s order quantity to give the retailer a certain quantity discount. Under the basicmodel of return and resalable product, put forward the retailer’s ordering problem under the condition of existing quantity discount. Modify the mathematical model, discussion and analysis two models that all-units discount and incremental quantity discount, gives retailers competitive ratio analysis and ordering policies in view of the two discount,finally combined with example analysis study the effects of system parameters on the optimal ordering policies. The results of the study show that, these two kinds of discount can stimulate the order, but the ordering strategy of competitive ratio relate to the critical value of order quantity that generated discount quantity, retailers can not only take into account the only condition of discount.Third, before making order policies, retailers tend to make reasonable prediction on demand generally, thus assisted ordering strategy formulation, so retailers can get more income, and improve their competitive advantage. When there are some expectations, is actually gave the retailer of certain information, through the effective use of these information, the retailer can obtain certain expected return. Therefore, this paper considers ordering policy under some expected in the case of returned and resalable products, studies the risk reward model, and for the first time put forward the method of maximum returns and minimizing deviation to make ordering policy, finally combined with example analysis study the effects of system parameters on the optimal ordering policies.The research and conclusion of this paper not only enriches the related studies, have complementary roles of the existing conclusions, but there are more guidance for the online ordering decision making problems in reality.