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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

Example of mutation operation.
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pone.0130767.g003: Example of mutation operation.

Mentions: The process of mutation is applied to a single offspring after the crossover exercise. Inspired from our previous work [44], we propose a new mutation operator to apply to a chromosome three times to obtain the greatest number of possible variations. The following steps, with an illustrative example in Fig 3, demonstrate this new mutation operator:


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)

Example of mutation operation.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0130767.g003: Example of mutation operation.
Mentions: The process of mutation is applied to a single offspring after the crossover exercise. Inspired from our previous work [44], we propose a new mutation operator to apply to a chromosome three times to obtain the greatest number of possible variations. The following steps, with an illustrative example in Fig 3, demonstrate this new mutation operator:

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