Multi-attributive group decision evaluation model under the condition of uncertain information
Huimin Li
Abstract
Huimin Li
Abstract
Owing to the complexity in the construction safety management,integrating expert′s knowledge and experiences to make appropriate decisions is necessary.TOPSIS(technique for order performance by similarity to ideal solution) is a practical and useful technique in dealing with multi-attribute decision making problems,and has been widely employed in construction management.Combined with traditional TOPSIS and grey number theory,the article proposes a multi-attribute group decision making model under the condition of uncertain information in the construction safety performance evaluation.Firstly,interval grey number evaluation matrix is applied to describe the uncertain decision information by expert.Secondly,a linear transformation function is adopted to construct the normalized grey decision matrix to avoid the information risk.Besides,Minkowski distance function is further integrated to overcome the over effects of weighing in the original TOPSIS technique.Finally,an application example shows that the proposed model is reasonable and efficient,and can easily extend to similar decision problems.
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Owing to the complexity in the construction safety management,integrating expert′s knowledge and experiences to make appropriate decisions is necessary.TOPSIS(technique for order performance by similarity to ideal solution) is a practical and useful technique in dealing with multi-attribute decision making problems,and has been widely employed in construction management.Combined with traditional TOPSIS and grey number theory,the article proposes a multi-attribute group decision making model under the condition of uncertain information in the construction safety performance evaluation.Firstly,interval grey number evaluation matrix is applied to describe the uncertain decision information by expert.Secondly,a linear transformation function is adopted to construct the normalized grey decision matrix to avoid the information risk.Besides,Minkowski distance function is further integrated to overcome the over effects of weighing in the original TOPSIS technique.Finally,an application example shows that the proposed model is reasonable and efficient,and can easily extend to similar decision problems.
Key concepts: TOPSIS, Ideal solution, Decision matrix, Computer science, Minkowski distance, Attributive, Data mining, Construct (python library)