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Time-cost-quality-risk Trade-off Project Scheduling Problem In Oil And Gas Construction Projects: Fuzzy Logic And Genetic Algorithm

Submitted2021-07-23
Last Update2022-03-24
TitleTime-cost-quality-risk Trade-off Project Scheduling Problem In Oil And Gas Construction Projects: Fuzzy Logic And Genetic Algorithm
Author(s)Author #1
Author title:Doctor and Assistant Professor
Name: Sayyid Ali Banihashemi
Org: Payame Noor University
Country: 0
Email: banihashemi@pnu.ac.ir

Author #2
Author title:Professor and Doctor
Name: Mohammad Mohammad
Org: CENTRUM Cat�lica Graduate Business School, Lima, Peru. Pontificia Universidad Cat�lica del Per�, Lima, Peru.
Country: Peru
Email: mo.kzadeh@gmail.com

Other Author(s)
Contact AuthorAuthor #2
Alt Email: mo.kzadeh@gmail.com
Telephone: 0051902613298
KeywordsMulti-objective optimization, Time-cost-quality-risk trade-off, Fuzzy logic, Metaheuristic algorithm, NSGA-II, Oil and gas construction project
AbstractTime, cost and quality known as project iron triangle are among the important goals and objectives of any project. New agreements in construction industry, which pay more attention to the quality of project implementation while reducing time and cost demonstrate the growing importance of these three primary objectives of projects. In this paper, an optimization model with three objective functions of cost, risk and quality considering their relationships with time is presented. In the cost objective function, the associated costs of compressing project activities together with the costs of delay are taken into account. The risk objective deals with the probability of risk occurrence and its impact on project other objectives. The quality objective function expresses the degree of reduction in the quality level of compressed activities. The model was also validated by solving the small size problems with the exact method and NSGA-II algorithm (Non-dominated Sorting Genetic Algorithm). The proposed model was implemented in a construction project of oil storage tanks. Due to the NP-Hard and large-sized problem, the model was solved by using the metaheuristic NSGA-II algorithm. The results showed that the NSGA-II algorithm achieved close to the optimal solution. Therefore, the NSGA-II algorithm can be exploited for solving large size problems with more confidence.
Paperview paper 6091.pdf (328KB)

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