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Journals / Teknik Dergi / 2019 / Cilt: 30 - Sayı: 6

Time-Cost Trade-Off Optimization with a New Initial Population Approach

Vedat TOĞAN
Mohammad Azim EIRGASH
Pages
9561–9580
Publication Date
2019-11-01
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It can produce good solutions as effective as with other techniques applied for simultaneous optimization of TCTPs.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Considering the competitive environment in all industries, completion on time is crucial for the stakeholders of a project.
📊
Key Findings & Data:Hence, it is aimed to descend the randomness on initial population and to decrease the searching effort to catch the optimal set of time-cost alternatives in the search space.
💡
Conclusion & Impact:It can produce good solutions as effective as with other techniques applied for simultaneous optimization of TCTPs.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Construction project · time-cost trade-off problem · multi-objective optimization

Abstract

Considering the competitive environment in all industries, completion on time is crucial for the stakeholders of a project. This favorable target is achieved by finding the optimal set of time-cost alternatives and this is known as time-cost trade-off problem (TCTP) in the literature. In this study, a new initial population approach is presented to improve the quality of the optimal set of time-cost alternatives. It put a predefined number of the solutions of the single objective TCTP into the initial population of teaching learning-based algorithm, which is utilized as an optimizer for the multi-objective optimization of TCTP. Hence, it is aimed to descend the randomness on initial population and to decrease the searching effort to catch the optimal set of time-cost alternatives in the search space. The proposed methodology is tested on a series of benchmark problems and the obtained results are compared with those available in the technical literature. It can produce good solutions as effective as with other techniques applied for simultaneous optimization of TCTPs.

Özet

Considering the competitive environment in all industries, completion on time is crucial for the stakeholders of a project. This favorable target is achieved by finding the optimal set of time-cost alternatives and this is known as time-cost trade-off problem (TCTP) in the literature. In this study, a new initial population approach is presented to improve the quality of the optimal set of time-cost alternatives. It put a predefined number of the solutions of the single objective TCTP into the initial population of teaching learning-based algorithm, which is utilized as an optimizer for the multi-objective optimization of TCTP. Hence, it is aimed to descend the randomness on initial population and to decrease the searching effort to catch the optimal set of time-cost alternatives in the search space. The proposed methodology is tested on a series of benchmark problems and the obtained results are compared with those available in the technical literature. It can produce good solutions as effective as with other techniques applied for simultaneous optimization of TCTPs.

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Cite This Article

TOĞAN, V., EIRGASH, M. A. (2019). Time-Cost Trade-Off Optimization with a New Initial Population Approach. Teknik Dergi, Cilt: 30 - Sayı: 6, 9561–9580. https://ajindex.com/articles/51223

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