Probabilistic Linguistish Multi-attributes Decision Making Methods And Their Application For Food Quality Design

In the 14th Five-Year Plan,it is proposed to promote high-quality development as the theme and achieve the fundamental purpose of meeting the people’s growing needs for a better life.However,high-quality development requires continuous improvement of the quality of products and services.People rely on food as their priority,and the improvement of food quality is particularly important.Design is the source of quality.Taking improving the design level of food quality as the key entry point,strengthening the management of food quality is an issue worthy of further exploration.Quality design includes three stages:requirement analysis,conceptual design and process design.Among them,the first and second stages are the top priority of the quality design process,and both have multi-attribute language group decision-making problems in uncertain environments.Probabilistic language can completely record the positive and negative language evaluation results and their probability distribution provided by the group.With the upgrading of food consumption structure,pure natural forest food without artificial synthetics has well catered to the new needs of consumers who have paid more attention to the health and greenness of food in recent years.However,the design of forest food with market potential can not meet the diversified needs of consumers.Therefore,this paper takes forest food as an example to study the food quality design.This paper uses the probabilistic language multi-attribute decision-making method to solve the multi-attribute decision-making problem in the quality design process.First,in order to measure the distance between two probabilistic language term sets,the probabilistic language Euclidean distance and the probability language Hamming distance are defined and applied.In the index weight calculation and fuzzy decision-making method;secondly,the probability language score function is defined,which is used to compare the size of two probability language term sets;thirdly,the probability language HM operator and weighted HM operator are defined,which are used for aggregation Experts evaluate the information and discuss its basic nature.Based on the above theoretical research and improvement,two multi-attribute decision-making models are established for the first-stage customer demand analysis in the food quality design process and the decision-making problems in the second-stage food quality design scheme selection.Model one is a probabilistic language QFD decision model based on HM operator,and model two is a probabilistic language TODIM decision model based on Hamming distance.The feasibility of the proposed method is verified by the case of Qiyun Shannan sour jujube cake.Finally,the scientific validity of the proposed method is further demonstrated through comparative analysis and sensitivity analysis.On the one hand,this paper applies the probabilistic language multi-attribute decision-making method to the process of food quality design,which can enrich the research results of food quality management.On the other hand,this paper systematically analyzes the deficiencies of the existing probabilistic language term set theory,improves it on the basis of previous research,and contributes to the research and application of probabilistic language multi-attribute decision-making methods.