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.
Initial population Metaheuristic algorithm Non-dominating sorting Teaching learning-based optimization
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.
Construction project time-cost trade-off problem multi-objective optimization metaheuristic algorithm
Birincil Dil | İngilizce |
---|---|
Konular | İnşaat Mühendisliği |
Bölüm | Makale |
Yazarlar | |
Yayımlanma Tarihi | 1 Kasım 2019 |
Gönderilme Tarihi | 29 Mart 2018 |
Yayımlandığı Sayı | Yıl 2019 Cilt: 30 Sayı: 6 |