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<p>Time and cost are two critical and often conflicting objectives in construction project management, especially under conditions of uncertainty. This study proposes a novel optimization model that integrates Fuzzy set theory with the non-dominated sorting genetic algorithm III (NSGA-III) to solve the time-cost trade-off (TCT) problem in construction projects. Activity durations and direct costs are modeled using Triangular Fuzzy Numbers (TFNs) to capture inherent uncertainties arising from variable site conditions, labor productivity, resource availability, and cost fluctuations. The fuzzy parameters are defuzzified using the expected value (EV) method incorporating an optimism–pessimism index (λ), enabling conversion into crisp values for optimization. NSGA-III is employed to simultaneously minimize total project time and cost, generating a Pareto-optimal set of solutions that reflect trade-offs between the two objectives. A real-world case study involving 21 construction activities with multiple execution modes validates the model. The results demonstrate that the proposed Fuzzy-NSGA-III approach outperforms conventional multi-objective algorithms in terms of convergence, diversity, and solution quality. Performance metrics such as generational distance (GD), hypervolume (HV), and quality metric (QM) confirm the robustness of the model. This approach provides an effective decision-support tool for construction planners to make informed choices in managing project schedules and budgets under uncertainty. </p>

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Optimizing time and cost in construction under uncertainty: a fuzzy-driven NSGA-III optimization approach

  • 期刊论文

  • 发表日期：

    2025年5月21日

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  • Bimalendu Dash,
  • Víctor Daniel Jiménez Macedo,
  • Dileep Kumar Mohanachandran,
  • Kiran Sree Pokkuluri,
  • V. Rathinakumar,
  • Krushna Chandra Sethi

摘要

Time and cost are two critical and often conflicting objectives in construction project management, especially under conditions of uncertainty. This study proposes a novel optimization model that integrates Fuzzy set theory with the non-dominated sorting genetic algorithm III (NSGA-III) to solve the time-cost trade-off (TCT) problem in construction projects. Activity durations and direct costs are modeled using Triangular Fuzzy Numbers (TFNs) to capture inherent uncertainties arising from variable site conditions, labor productivity, resource availability, and cost fluctuations. The fuzzy parameters are defuzzified using the expected value (EV) method incorporating an optimism–pessimism index (λ), enabling conversion into crisp values for optimization. NSGA-III is employed to simultaneously minimize total project time and cost, generating a Pareto-optimal set of solutions that reflect trade-offs between the two objectives. A real-world case study involving 21 construction activities with multiple execution modes validates the model. The results demonstrate that the proposed Fuzzy-NSGA-III approach outperforms conventional multi-objective algorithms in terms of convergence, diversity, and solution quality. Performance metrics such as generational distance (GD), hypervolume (HV), and quality metric (QM) confirm the robustness of the model. This approach provides an effective decision-support tool for construction planners to make informed choices in managing project schedules and budgets under uncertainty.