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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

Operational procedure of finding the optimal tenderer.
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pone.0130767.g005: Operational procedure of finding the optimal tenderer.

Mentions: The purpose of the example is to demonstrate the search for the tenderer with the greatest closeness coefficient value in a specified context. Fig 5 illustrates the operational procedure of determining the optimal tenderer using the proposed approach. To begin, several candidate tenderers are screened from all the effective tenderers that are saved in the tenderer registry. The evaluation criteria are selected or input. The evaluation experts provide their ratings on each criterion of the tenderers and their overall ratings on each tenderer. Then, the proposed approach infers the tenderer with the greatest closeness coefficient value from all the candidate tenderers. The historical expert ratings and tenderer information are extracted from a historical expert rating repository and tenderer ontology repository, respectively. Our previous researches [45–46] have developed a rich body of OWL-based (OWL, ontology Web language) service ontologies that can provide valid reference for the current approach.


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)

Operational procedure of finding the optimal tenderer.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0130767.g005: Operational procedure of finding the optimal tenderer.
Mentions: The purpose of the example is to demonstrate the search for the tenderer with the greatest closeness coefficient value in a specified context. Fig 5 illustrates the operational procedure of determining the optimal tenderer using the proposed approach. To begin, several candidate tenderers are screened from all the effective tenderers that are saved in the tenderer registry. The evaluation criteria are selected or input. The evaluation experts provide their ratings on each criterion of the tenderers and their overall ratings on each tenderer. Then, the proposed approach infers the tenderer with the greatest closeness coefficient value from all the candidate tenderers. The historical expert ratings and tenderer information are extracted from a historical expert rating repository and tenderer ontology repository, respectively. Our previous researches [45–46] have developed a rich body of OWL-based (OWL, ontology Web language) service ontologies that can provide valid reference for the current approach.

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