The purpose of this paper is to propose a model for effective data filling and precise prediction, which is used to solve the prediction problem of sequential data with…
The purpose of this paper is to propose a model for effective data filling and precise prediction, which is used to solve the prediction problem of sequential data with the characteristics of poor information, high growth and containing extraordinary points.
After proving that the three principles of smooth sequence are not a sufficient condition for the judgement of sequence smoothness, judgement rules for sequence smoothness based on smoothness efficiency is introduced. Based on the non‐homogenous discrete grey model (NDGM) model which fits for high growth sequence, model error caused by equal weight mean value is analyzed, and mean value generation weight efficiency is optimized by the method of differential. Prediction steps that fit sequences with high growth, poor information and containing extraordinary points is established on the basis of equal weight mean value generation efficiency.
The results are convincing: previous judgement rules used for sequence smoothness do not fit for the high growth sequence, new judgement rules introduced are more effective for high growth sequence. Sequence filling algorithm based on differential ration not only improve the filling of high growth sequence, but also enhance the prediction precision of these sequences.
The method exposed in the paper can be used to solve the prediction problem of sequences with poor information, high growth and containing extraordinary points, and it was proved in the cases of large and medium company new products income and Ufida Software Company. What is more, the method is also helpful in aspects of corporate financial control and strategy‐making process.
The paper succeeds in proposing a new interpolation algorithm that is superior to ordinary mean value generation method in the aspects of generation and prediction and to grey interpolation algorithm in the aspect of information volume by defining sequence smoothness efficiency and introducing smoothness judgement rules that are easy to compute and fits for high growth sequence and not limited to monotonicity sequence.
The operational management of cold chain logistics has an important impact on the quality of cold chain products, but the service delivery process is subject to a series…
The operational management of cold chain logistics has an important impact on the quality of cold chain products, but the service delivery process is subject to a series of potential problems such as product loss and cold storage temperature in the actual operation.
In this paper, the whole cold chain logistics system and risk events are analyzed. A Bayesian network is used for modeling and simulation to identify the main influencing factors and to conduct a sensitivity analysis of the main factors.
It is found that the operation of cold chain logistics systems can be divided into four links according to the degree of influence as follows: transportation and distribution, processing and packaging, information processing and warehousing. Transportation and distribution is the most influential factor of system failure, and extreme weather is the most risky event. At the same time, the four risk events that have the greatest impact on the operation of the cold chain system are in descending order: transportation equipment failure, extreme weather, unqualified pre-cooling and violation operation.
Therefore, enterprises should develop appropriate interventions for securing the transportation services, design strategies to deal with extreme weather conditions prior to and in the early stage of product delivery, and prepare additional effective measures for managing emergency events.