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<p>This research introduces a comprehensive multi-objective optimization framework for retrofitting projects, addressing six critical performance dimensions: Time, Cost, Quality, Energy consumption, Safety, and Environmental impact Trade-off (TCQESET). The proposed model leverages an enhanced Opposition-Based Non-Dominated Sorting Genetic Algorithm III (OBNSGA-III) to generate optimal trade-off solutions that balance these competing objectives. Innovative formulations, such as bivariate normal distribution for quality assessment and fuzzy logic for safety risk evaluation, are incorporated to improve model realism. Opposition-based learning enhances population diversity through enriched initialization and generation jumping, ensuring better convergence and broader solution exploration. A case study of a commercial building retrofitting project in Delhi-NCR demonstrates the model’s effectiveness, outperforming benchmark algorithms like NSGA-III and MOPSO in generating diverse Pareto-optimal solutions. Sensitivity analyses further validate the robustness of the model. The results indicate that sustainable retrofitting strategies can be achieved without disproportionate sacrifices in cost, time, or quality. This study establishes OBNSGA-III as a powerful decision-support tool for project managers and policymakers seeking to implement efficient, safe, and environmentally responsible retrofitting strategies within complex urban infrastructure environments.</p>

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Opposition-based NSGA-III framework for multi-objective optimization of retrofitting projects: balancing time, cost, quality, energy, safety, and environmental impact

  • 期刊论文

  • 发表日期：

    2025年10月31日

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  • Atif Ahmad,
  • Sarvesh Vyas

摘要

This research introduces a comprehensive multi-objective optimization framework for retrofitting projects, addressing six critical performance dimensions: Time, Cost, Quality, Energy consumption, Safety, and Environmental impact Trade-off (TCQESET). The proposed model leverages an enhanced Opposition-Based Non-Dominated Sorting Genetic Algorithm III (OBNSGA-III) to generate optimal trade-off solutions that balance these competing objectives. Innovative formulations, such as bivariate normal distribution for quality assessment and fuzzy logic for safety risk evaluation, are incorporated to improve model realism. Opposition-based learning enhances population diversity through enriched initialization and generation jumping, ensuring better convergence and broader solution exploration. A case study of a commercial building retrofitting project in Delhi-NCR demonstrates the model’s effectiveness, outperforming benchmark algorithms like NSGA-III and MOPSO in generating diverse Pareto-optimal solutions. Sensitivity analyses further validate the robustness of the model. The results indicate that sustainable retrofitting strategies can be achieved without disproportionate sacrifices in cost, time, or quality. This study establishes OBNSGA-III as a powerful decision-support tool for project managers and policymakers seeking to implement efficient, safe, and environmentally responsible retrofitting strategies within complex urban infrastructure environments.