Research On Multi-cycle Credit Quota Decision Of Supply Chain Finance In A Platform

With the rapid development of Internet finance and the slowdown of the growth of the trading market,the supply chain financial market has great potential,and the market share among various model entities is becoming increasingly fierce.Under the limited funding constraints,a reasonable quota allocation and appropriate The time of payment is the key to highlighting your own competitiveness.For the logistics platform,after signing the cooperation clause with the funder,how to make full use of the supply chain platform to provide high-quality supply chain financial services by virtue of its own advantages,the importance of making reasonable decisions gradually emerges.In this context,A supply chain platform decided to integrate supply chain finance into the original supply chain service.However,due to the limited amount of authorization signed by the funder,it is necessary to reasonably allocate the best quota for the chain companies it serves.Financing period.Based on the above background,this paper studies the supply chain financial credit quota decision of the third-party supply chain platform as follows:(1)Analyze and summarize the related concepts and constituents of supply chain finance,and compare the advantages and disadvantages of supply chain finance under the three dominant modes.Then analyze the status quo of A platform and B enterprise and its cooperation status.Then it analyzes the status quo of upstream and downstream suppliers and distributors of B enterprises,analyzes the existing problems,and proposes appropriate customized decisions.(2)Based on the platform and its problem scenario,a multi-cycle credit quota decision model based on dynamic planning supply chain finance was established.The decision stage,constraint conditions and state transition of each stage are analyzed,and then the optimal goal of the periodic decision is introduced into the supply chain financial decision model,so as to establish the maximum sum of the service satisfaction rate of the upstream and downstream nodes applying for service.The ideal model for multi-stage decision making.Then the ideal model is analyzed and the shortcomings of its dimensionality disaster are found.Therefore,the enterprise clustering idea is used to simplify the ideal model,so that the existing algorithm can be used to obtain a satisfactory solution in a limited time.(3)Based on the above model,respectively design a clustering algorithm that conforms to the platform scenario and a genetic algorithm that solves the final allocation quota.The PAM algorithm that meets the needs of the scene is selected and its weight is corrected.Then the genetic algorithm based on real coding is designed,and its selection,intersection and variation.(4)Pre-processing the collected data,and then combining the designed clustering algorithm to obtain the corresponding categories of all enterprises,and assigning the category names according to the characteristics of the enterprises to which they belong,and analyzing and formulating the corresponding financing deadlines..According to the clustering results,the designed genetic algorithm is used to solve the model,and the credit quotas allocated by each enterprise are obtained.Then the results are compared with the traditional financing methods,and the influencing factors of the conclusions are analyzed.