Location Of Fresh Product Self-collection Cabinet In E-commerce

Fresh e-commerce is known as the last blue ocean in the field of e-commerce.Since 2012,there has been a constant influx of capital.However,after 2016,our country's fresh e-commerce performance declined,the loss increased,and even fell into the tide of bankruptcy.In addition to lack of the fine operation and management,the short of the end distribution has been became one of the key problems that restrict the development of fresh e-commerce.Most of the current distribution models is "home delivery",or to reduce the distance between the stores and consumers to improve customer satisfaction.However,because of the rise of labor cost and storefront rent,the above model has been unable to adapt to the development of the current fresh e-commerce.The emergence of fresh product self-collection cabinet will effectively solve the short board facing the current fresh e-commerce.The characteristics of fresh product self-collection cabinet that 24h refrigerated storage can make the delivery time more flexible,and at the same time,it can significantly reduce labor cost.It is also an effective way to solve the safety problem of terminal delivery and delivery.However,the development of the cold chain logistics in China is still in the exploration stage,and fresh product self-collection cabinet is one new thing for our country.There is no scientific theory to guide the practice of the enterprise.The core of this dissertation is to study the location of the fresh product self-collection cabinet,in order to provide an effective location method to guide the decision of the stakeholders.Firstly,through analyzing the connotation and features of fresh product self-collection cabinet to describe the characteristics of that,then to study the influencing factors of the fresh product self-collection cabinet in location problem,combining with the host and guest body,the service process and the development,this is the basis for mining the causes of enterprise decision making and consumer preference.Secondly,this dissertation sets up a Bi-level location model to describe the relationship between enterprise decision and consumer preference behavior.Besides considering the construction cost,operation cost and distribution cost,the dissertation also combines the characteristics of fresh product self-collection cabinet,adding fresh penalty cost and distance penalty cost due to the decrease of freshness and the decrease of demand.By analyzing the consumer's self-collection behavior,the lower level model which can reflect the consumer's preference behavior is quantified into two aspects:the penalty cost brought by the freshness reduction on way home and the customer's self-collection cost.Besides,this dissertation sets up the independent decision variables of upper and lower levels to depict the relationship between enterprises and consumers,this relationship is satisfied the application premise of bi level programming and realistic situation.Bydevising to the matching genetic algorithm based on consumers to solve the case,then verify its feasibility,finally,applying sensitivity analysis of product freshness,demand lose rate and self-collection cost of consumers to get the conclusions of this dissertation.The contribution of this dissertation is find that the freshness and demand lose rate and the self-collection cost of consumers will have a profound impact on the final decision.