Metaheuristic Based Automated Evaluation Method for Public Investment Project

Date

2015-09-25

Author

Yılmaz, Tulug Figen
Karagöz, Pınar

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The public investment projects are essential to improve the state economy and life of people. Since the resources are limited, developing automated project performance evaluation systems are required. In this work, we propose a metaheuristic algorithm based approach for automated project evaluation. Project evaluation involves selecting the attributes effective for evaluation, determining the weights of the selected attributes and determining cut-off values for project evaluation. In the attribute selection part of this model, the effect of supervised learning based models are elaborated. For determining attribute weights and cutoff values, we build a Genetic Algorithm (GA) based solution. The proposed system is tested on the evaluation process of 493 World Bank projects. The results are compared with the classical project evaluation technique using the Analytical Hierarchy Process (AHP) and classification based models. Experimental results show that the proposed approach provides considerable improvement for project evaluation accuracy.

Subject Keywords

Metaheuristic computation, Project evaluation system, Supervised learning

URI

https://hdl.handle.net/11511/80504
https://www.scopus.com/record/display.uri?eid=2-s2.0-84959215330&origin=resultslist&sort=plf-f&src=s&st1=&st2=&sid=ba11595946a7b7d4b98fa32934e7cb4c&sot=b&sdt=b&sl=93&s=TITLE-ABS-KEY+%28Metaheuristic+Based+Automated+Evaluation+Method+for+Public+Investment+Project%29&relpos=0&citeCnt=0&searchTerm=

Conference Name

13th International Symposium on Operational Research, SOR(2015)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

T. F. Yılmaz and P. Karagöz, “Metaheuristic Based Automated Evaluation Method for Public Investment Project,” presented at the 13th International Symposium on Operational Research, SOR(2015), Bled; Slovenia, 2015, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/80504.