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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

Graphical interface for identifying tenderers.
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pone.0130767.g010: Graphical interface for identifying tenderers.

Mentions: Finally, by clicking the “Identify tenderers” button in the top right corner of the window of Fig 10, we can obtain the ranking list of the 12 candidate tenderers with their corresponding closeness coefficient value in decreasing order, including telephone number and address. Because of the tiny variability of weight values, the ranking of all candidate tenderers is also not invariable, i.e., the relative ranking of tenderers with almost the same closeness coefficient values may vary as the corresponding weight values change. Nonetheless, the optimal tenderer still can be recognized. It is just a matter of the number of the optimal tenderer, i.e., there may be exist several optimal tenderers. In this example, the decoration firm “Minzhong” has the greatest closeness coefficient value, 0.663, meaning this firm is the optimal tenderer from the government search. That is, “Minzhong” is the most suitable tenderer for the government requirements among the 12 candidate tenderers.


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)

Graphical interface for identifying tenderers.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0130767.g010: Graphical interface for identifying tenderers.
Mentions: Finally, by clicking the “Identify tenderers” button in the top right corner of the window of Fig 10, we can obtain the ranking list of the 12 candidate tenderers with their corresponding closeness coefficient value in decreasing order, including telephone number and address. Because of the tiny variability of weight values, the ranking of all candidate tenderers is also not invariable, i.e., the relative ranking of tenderers with almost the same closeness coefficient values may vary as the corresponding weight values change. Nonetheless, the optimal tenderer still can be recognized. It is just a matter of the number of the optimal tenderer, i.e., there may be exist several optimal tenderers. In this example, the decoration firm “Minzhong” has the greatest closeness coefficient value, 0.663, meaning this firm is the optimal tenderer from the government search. That is, “Minzhong” is the most suitable tenderer for the government requirements among the 12 candidate tenderers.

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