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

Year 2019, Volume: 30 Issue: 6, 9561 - 9580, 01.11.2019

Vedat Toğan , Mohammad Azim Eırgash

https://doi.org/10.18400/tekderg.410934

Cited By: 7

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.

Keywords

Initial population, Metaheuristic algorithm, Non-dominating sorting, Teaching learning-based optimization

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

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.

Keywords

Construction project, time-cost trade-off problem, multi-objective optimization, metaheuristic algorithm

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