Optimization Research On Green Building Energy Saving Scheme Based On Linguistic Cubic Hesitant Variables

The construction industry is an important pillar industry that promotes the development of the national economy.It is inextricably linked to the improvement of people’s living standards.In recent years,with the rapid development of today’s social economy and the continuous advancement of urbanization,the construction industry is booming while bringing a series of environmental pollution problems.In order to so lve the problem of the deterioration of ecological environment and the shortage of resources,China has introduced corresponding policies to vigorously develop green buildings,promote energy conservation and emission reduction,and establish an ecological civilization.Developing a reasonable and effective energy-saving scheme according to the corresponding green building helps improve the energy-saving effect of the building.Therefore,it is vitally significant for investors to choose from various green building energy-saving schemes the most efficient and reasonable one during the decision-making process.Firstly,the concept of linguistic cubic hesitant variable and its operation relation are proposed.The weighted arithmetic averaging operator and the linguistic score function of the weighted geometric averaging operator are defined.The multi-attribut e decision-making method based on linguistic cubic hesitant variable is established and its rationality is proved by examples;Secondly,the paper analyzes different aspects of green building energy-saving measures,establishes an evaluation index system for green building energy-saving scheme,and divides the evaluation index system into top-down,total-to-subordinate main target index layers and sub-target index layers.The target layer includes five aspects: energy utilization index,building quality index,environmental load index,construction management index and personal safety index.Then the five main target indicators are subdivided separately to obtain a green building energy-saving plan with 18 sub-target indicators.Evaluate the indicator system and explain the significance of each evaluation indicator as an evaluation indicator;Finally,a multi-attribute decision-making method based on linguistic cubic hesitant variable for green building energy-saving scheme is constructed byscrutinizing specific examples.The weight of the main target index and the sub-target index is determined based on the established evaluation index system,and the linguistic score is determined.The weighted arithmetic averaging operator linguistic score function of linguistic cubic hesitant variable of each sub-target indicator,and weights it to obtain the linguistic scores of the five main target indicators,and finally obtains the linguistic scores of each green building energy-saving scheme.The scores are ranked,and the highest scores are the optimal energy-saving scheme.The factors that influence the different indicators in the evaluation index system of green building energy-saving schemes are analyzed to verify the optimal energy-saving scheme.The applicability of linguistic cubic hesitant variable multi-attribute decision-making method in optimizing the green building energy-saving scheme.For the first time,the multi-attribute decision making method of linguistic cubic hesitant variable is applied to the optimization decision-making of green building energy-saving scheme.A new linguistic concept linguistic cubic hesitant variable based on the combination of linguistic cubic variables and hesitant linguistic variables is used and the different evaluation indicators of the green buildings energy-saving scheme are scored,and the weighted aggregation operator is used to calculate the optimal scheme.This method is used to solve the uncertainty encountered by the green building decision makers in selecting the optimal multi-attribute decisionmaking problem.The paper providing a new method for the optimization of green building energy-saving schemes,has certain application value for the actual decisionmaking of green building energy-saving schemes.