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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 inputting ratings of experts.
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pone.0130767.g008: Graphical interface for inputting ratings of experts.

Mentions: Upon setting the evaluation criteria, the ratings of 5 experts on the 5 evaluation criteria and 12 candidate tenderers must be input, including expert ID, tenderer name, criterion name, and corresponding rating. During this process, the ratings are expressed in vague language. Herrera and Martinez [47] illustrated a seven-term linguistic term set and used a triangular fuzzy number as a linguistic descriptor. Inspired from this, we propose a nine-term linguistic term set and apply FNIFS as the linguistic descriptor, presented as “Perfect = <(0.88,1,1),(0,0,0)>”, “Very high = <(0.75,0.88,1),(0,0,0)>”, “Higher = <(0.62,0.75,0.88),(0,0,0.12)>”, “High = <(0.50,0.62,0.75),(0,0.13,0.25)>”, “Medium = <(0.38,0.50,0.62),(0.13,0.25,0.38)>”, “Low = <(0.26,0.38,0.50),(0.25,0.38,0.50)>”, “Lower = <(0.13,0.26,0.38),(0.38,0.50,0.62)>”, “Very Low = <(0,0.13,0.26),(0.50,0.62,0.74)>”, and “None = <(0,0,0.13),(0.62,0.74,0.87)>”. If a file exists with evaluation information in “txt” format, the users can simply import the file. In this example, the evaluation information was given as supporting information file (S1 Data). Alternatively, users can manually enter the evaluation information. An inexperienced user can select ratings that are expressed as vague language. Then, by clicking the “Add rating” button, the rating of one corresponding criterion of one tenderer rated by one expert will be presented as the corresponding FNIFS in the window of Fig 8. The first line “1 Dafutu safety a = 0.5 b = 0.62 c = 0.75 l = 0.00 m = 0.13 p = 0.25 (High)” means the FNIFS expression of the rating provided by “expert 1” for the criterion “safety” of tenderer “Dafutu” is <(0.5,0.62,0.75),(0,0.13,0.25)> and its corresponding vague language is “High”. An experienced user can edit the ratings to make them more practical for the specified context by double clicking the corresponding row. Users can also delete ratings by clicking the “Delete rating” button.


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 inputting ratings of experts.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0130767.g008: Graphical interface for inputting ratings of experts.
Mentions: Upon setting the evaluation criteria, the ratings of 5 experts on the 5 evaluation criteria and 12 candidate tenderers must be input, including expert ID, tenderer name, criterion name, and corresponding rating. During this process, the ratings are expressed in vague language. Herrera and Martinez [47] illustrated a seven-term linguistic term set and used a triangular fuzzy number as a linguistic descriptor. Inspired from this, we propose a nine-term linguistic term set and apply FNIFS as the linguistic descriptor, presented as “Perfect = <(0.88,1,1),(0,0,0)>”, “Very high = <(0.75,0.88,1),(0,0,0)>”, “Higher = <(0.62,0.75,0.88),(0,0,0.12)>”, “High = <(0.50,0.62,0.75),(0,0.13,0.25)>”, “Medium = <(0.38,0.50,0.62),(0.13,0.25,0.38)>”, “Low = <(0.26,0.38,0.50),(0.25,0.38,0.50)>”, “Lower = <(0.13,0.26,0.38),(0.38,0.50,0.62)>”, “Very Low = <(0,0.13,0.26),(0.50,0.62,0.74)>”, and “None = <(0,0,0.13),(0.62,0.74,0.87)>”. If a file exists with evaluation information in “txt” format, the users can simply import the file. In this example, the evaluation information was given as supporting information file (S1 Data). Alternatively, users can manually enter the evaluation information. An inexperienced user can select ratings that are expressed as vague language. Then, by clicking the “Add rating” button, the rating of one corresponding criterion of one tenderer rated by one expert will be presented as the corresponding FNIFS in the window of Fig 8. The first line “1 Dafutu safety a = 0.5 b = 0.62 c = 0.75 l = 0.00 m = 0.13 p = 0.25 (High)” means the FNIFS expression of the rating provided by “expert 1” for the criterion “safety” of tenderer “Dafutu” is <(0.5,0.62,0.75),(0,0.13,0.25)> and its corresponding vague language is “High”. An experienced user can edit the ratings to make them more practical for the specified context by double clicking the corresponding row. Users can also delete ratings by clicking the “Delete rating” button.

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