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<p>This study presents a hybrid metaheuristic framework integrating Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Multi-objective Particle Swarm Optimization (MOPSO) to solve the Time–Cost–Environmental Sustainability Trade-off (TCEST) problem for sustainable construction planning in Nepal. The model simultaneously minimizes project completion time and cost while maximizing environmental performance, tailored to the Nepalese construction context. A detailed case study of a G + 1 residential building in the Kathmandu Valley—comprising 21 activities with multiple execution modes—validates the framework. The hybrid algorithm generates a well-distributed Pareto front, offering diverse trade-off solutions relevant to Nepal’s unique cost structures, resource availability, and environmental priorities, such as reducing reliance on imported materials and minimizing riverbed material extraction. Performance metrics confirm the framework’s superiority over standalone algorithms. The Weighted Sum Method is applied to identify the most suitable solution based on stakeholder priorities. Correlation and trade-off analysis further reveal key interdependencies among objectives. Validation results indicate high predictive accuracy (R² &gt; 0.96) and Pareto front quality. Overall, the hybrid NSGA-III–MOPSO framework provides a robust, data-driven decision support system for Nepalese stakeholders to balance project timelines, budgets, and sustainability goals effectively.</p>

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Integrated hybrid NSGA-III–MOPSO model for sustainable time–cost optimization in residential building construction in Nepal

  • 期刊论文

  • 发表日期：

    2026年5月7日

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  • Subash Kumar Bhattarai,
  • Nischal Silwal,
  • Mahesh Sharma,
  • Asim Lohani,
  • Mukesh Kafle

摘要

This study presents a hybrid metaheuristic framework integrating Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Multi-objective Particle Swarm Optimization (MOPSO) to solve the Time–Cost–Environmental Sustainability Trade-off (TCEST) problem for sustainable construction planning in Nepal. The model simultaneously minimizes project completion time and cost while maximizing environmental performance, tailored to the Nepalese construction context. A detailed case study of a G + 1 residential building in the Kathmandu Valley—comprising 21 activities with multiple execution modes—validates the framework. The hybrid algorithm generates a well-distributed Pareto front, offering diverse trade-off solutions relevant to Nepal’s unique cost structures, resource availability, and environmental priorities, such as reducing reliance on imported materials and minimizing riverbed material extraction. Performance metrics confirm the framework’s superiority over standalone algorithms. The Weighted Sum Method is applied to identify the most suitable solution based on stakeholder priorities. Correlation and trade-off analysis further reveal key interdependencies among objectives. Validation results indicate high predictive accuracy (R² > 0.96) and Pareto front quality. Overall, the hybrid NSGA-III–MOPSO framework provides a robust, data-driven decision support system for Nepalese stakeholders to balance project timelines, budgets, and sustainability goals effectively.