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Combined Approach for Government E-Tendering Using GA and TOPSIS with Intuitionistic Fuzzy Information.

Wang Y, Xi C, Zhang S, Zhang W, Yu D - PLoS ONE (2015)

Bottom Line: As E-government continues to develop with ever-increasing speed, the requirement to enhance traditional government systems and affairs with electronic methods that are more effective and efficient is becoming critical.TOPSIS is employed to search for the optimal tenderer.A prototype system is built and validated with an illustrative example from GeT to verify the feasibility and availability of the proposed approach.

View Article: PubMed Central - PubMed

Affiliation: School of Information, Zhejiang University of Finance and Economics, Hangzhou, China.

ABSTRACT
As E-government continues to develop with ever-increasing speed, the requirement to enhance traditional government systems and affairs with electronic methods that are more effective and efficient is becoming critical. As a new product of information technology, E-tendering is becoming an inevitable reality owing to its efficiency, fairness, transparency, and accountability. Thus, developing and promoting government E-tendering (GeT) is imperative. This paper presents a hybrid approach combining genetic algorithm (GA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to enable GeT to search for the optimal tenderer efficiently and fairly under circumstances where the attributes of the tenderers are expressed as fuzzy number intuitionistic fuzzy sets (FNIFSs). GA is applied to obtain the optimal weights of evaluation criteria of tenderers automatically. TOPSIS is employed to search for the optimal tenderer. A prototype system is built and validated with an illustrative example from GeT to verify the feasibility and availability of the proposed approach.

No MeSH data available.


Related in: MedlinePlus

Computational results of the proposed GA-based weight optimization method.
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pone.0130767.g004: Computational results of the proposed GA-based weight optimization method.

Mentions: Fig 4 presents the computational results of the proposed GA-based weight optimization method. The experiment was developed in the C# programming language. We can determine from the figure that the fitness value finally converged to approximately 0.97. Actually, based on the calculated results, the converged fitness value was 0.9730 and the corresponding optimal weight vector was {0.219, 0.193, 0.179, 0.202, 0.205}, i.e., the weights of the evaluation criteria “safety”, “function”, “artistry”, “feasibility”, and “price” are 0.219, 0.193, 0.179, 0.202, and 0.205, respectively. The weight of “safety” is the greatest; the weight of “artistry” is the least. The weights of “feasibility” and “price” are similar and are slightly greater than “function”. This result fits the actual situation reasonably well and confirms the effectiveness of the GA-based weight optimization method.


Combined Approach for Government E-Tendering Using GA and TOPSIS with Intuitionistic Fuzzy Information.

Wang Y, Xi C, Zhang S, Zhang W, Yu D - PLoS ONE (2015)

Computational results of the proposed GA-based weight optimization method.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493125&req=5

pone.0130767.g004: Computational results of the proposed GA-based weight optimization method.
Mentions: Fig 4 presents the computational results of the proposed GA-based weight optimization method. The experiment was developed in the C# programming language. We can determine from the figure that the fitness value finally converged to approximately 0.97. Actually, based on the calculated results, the converged fitness value was 0.9730 and the corresponding optimal weight vector was {0.219, 0.193, 0.179, 0.202, 0.205}, i.e., the weights of the evaluation criteria “safety”, “function”, “artistry”, “feasibility”, and “price” are 0.219, 0.193, 0.179, 0.202, and 0.205, respectively. The weight of “safety” is the greatest; the weight of “artistry” is the least. The weights of “feasibility” and “price” are similar and are slightly greater than “function”. This result fits the actual situation reasonably well and confirms the effectiveness of the GA-based weight optimization method.

Bottom Line: As E-government continues to develop with ever-increasing speed, the requirement to enhance traditional government systems and affairs with electronic methods that are more effective and efficient is becoming critical.TOPSIS is employed to search for the optimal tenderer.A prototype system is built and validated with an illustrative example from GeT to verify the feasibility and availability of the proposed approach.

View Article: PubMed Central - PubMed

Affiliation: School of Information, Zhejiang University of Finance and Economics, Hangzhou, China.

ABSTRACT
As E-government continues to develop with ever-increasing speed, the requirement to enhance traditional government systems and affairs with electronic methods that are more effective and efficient is becoming critical. As a new product of information technology, E-tendering is becoming an inevitable reality owing to its efficiency, fairness, transparency, and accountability. Thus, developing and promoting government E-tendering (GeT) is imperative. This paper presents a hybrid approach combining genetic algorithm (GA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to enable GeT to search for the optimal tenderer efficiently and fairly under circumstances where the attributes of the tenderers are expressed as fuzzy number intuitionistic fuzzy sets (FNIFSs). GA is applied to obtain the optimal weights of evaluation criteria of tenderers automatically. TOPSIS is employed to search for the optimal tenderer. A prototype system is built and validated with an illustrative example from GeT to verify the feasibility and availability of the proposed approach.

No MeSH data available.


Related in: MedlinePlus