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<p>Sustainable construction bidding is critical for emerging economies facing rapid urbanization, limited resources, and growing environmental challenges. Traditional bidding methods often prioritize cost and time, neglecting essential sustainability criteria such as quality, energy efficiency, environmental impact, and site safety. This study introduces a comprehensive multi-objective optimization framework, time-cost-quality-energy-environment-safety (TCQEES), implemented using an enhanced evolutionary algorithm, the opposition-based non-dominated sorting genetic algorithm III (OBNSGA-III). By incorporating opposition-based learning, reference-point-based selection, and generation jumping, the OBNSGA-III improves convergence speed and solution diversity in high-dimensional Pareto search spaces. The framework is applied to a real-world case involving the sustainable retrofit of a commercial building in Manipal, India, optimizing 11 retrofit aspects across six sustainability objectives. A post Pareto analysis further classifies the optimal solutions into cost focused, sustainability oriented, and time driven categories, aligning with diverse stakeholder preferences. Comparative analysis with NSGA-II, NSGA-III, MOPSO, and MOACO shows that OBNSGA-III outperforms these methods in terms of hypervolume, spread, and computational efficiency. The model delivers diverse, Pareto-optimal bidding strategies, enabling decision-makers to balance economic feasibility, environmental responsibility, and safety. The proposed TCQEES-OBNSGA-III framework offers a practical, scalable tool for integrating sustainability into construction procurement, particularly in the Global South. It supports the implementation of Sustainable Development Goals (SDGs) by fostering intelligent, multi-criteria decision-making in infrastructure development.</p>

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Advanced multi-objective bid optimization using TCQEES and opposition-based NSGA-III: a sustainable approach for developing economies

  • 期刊论文

  • 发表日期：

    2025年8月21日

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  • Manish Bharadwaj,
  • Manoj Patwardhan,
  • Kamal Sharma

摘要

Sustainable construction bidding is critical for emerging economies facing rapid urbanization, limited resources, and growing environmental challenges. Traditional bidding methods often prioritize cost and time, neglecting essential sustainability criteria such as quality, energy efficiency, environmental impact, and site safety. This study introduces a comprehensive multi-objective optimization framework, time-cost-quality-energy-environment-safety (TCQEES), implemented using an enhanced evolutionary algorithm, the opposition-based non-dominated sorting genetic algorithm III (OBNSGA-III). By incorporating opposition-based learning, reference-point-based selection, and generation jumping, the OBNSGA-III improves convergence speed and solution diversity in high-dimensional Pareto search spaces. The framework is applied to a real-world case involving the sustainable retrofit of a commercial building in Manipal, India, optimizing 11 retrofit aspects across six sustainability objectives. A post Pareto analysis further classifies the optimal solutions into cost focused, sustainability oriented, and time driven categories, aligning with diverse stakeholder preferences. Comparative analysis with NSGA-II, NSGA-III, MOPSO, and MOACO shows that OBNSGA-III outperforms these methods in terms of hypervolume, spread, and computational efficiency. The model delivers diverse, Pareto-optimal bidding strategies, enabling decision-makers to balance economic feasibility, environmental responsibility, and safety. The proposed TCQEES-OBNSGA-III framework offers a practical, scalable tool for integrating sustainability into construction procurement, particularly in the Global South. It supports the implementation of Sustainable Development Goals (SDGs) by fostering intelligent, multi-criteria decision-making in infrastructure development.