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<p>Sustainable retrofitting of infrastructure involves complex trade-offs between project duration, cost, and environmental impact. This study introduces a novel multi-objective optimization framework using the opposition-based learning multi-objective teaching–learning-based optimization (OBL-MOTLBO) algorithm. The proposed time-cost-environmental trade-off (TCET) model aims to minimize retrofitting time (RT), cost (RC), and carbon-equivalent emissions (REI) simultaneously. A case study conducted in the Delhi-NCR region spans eleven retrofitting domains, each with three intervention options. The OBL-MOTLBO algorithm outperforms established methods (e.g., NSGA-II, MOACO) in generating a diverse, converged Pareto front. To support decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied, allowing prioritization of solutions under different stakeholder preferences. Among 18 Pareto-optimal solutions, Solution 17 (RT = 98 days, RC = $295,000, REI = 320,000&#xa0;kg CO₂-eq) ranks highest across all scenarios. The framework offers a robust, scalable method for sustainable retrofit planning, integrating economic and environmental objectives into data-driven decision-making.</p>

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Optimization of sustainable retrofitting using OBL-MOTLBO: a multi-objective approach to time, cost, and environmental trade-offs

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Kiran Sree Pokkuluri,
  • Devara Pavan Nagendra,
  • Amir Prasad Behera,
  • Manmohan Singh,
  • Sudhanshu Maurya,
  • T. C. Manjunath

摘要

Sustainable retrofitting of infrastructure involves complex trade-offs between project duration, cost, and environmental impact. This study introduces a novel multi-objective optimization framework using the opposition-based learning multi-objective teaching–learning-based optimization (OBL-MOTLBO) algorithm. The proposed time-cost-environmental trade-off (TCET) model aims to minimize retrofitting time (RT), cost (RC), and carbon-equivalent emissions (REI) simultaneously. A case study conducted in the Delhi-NCR region spans eleven retrofitting domains, each with three intervention options. The OBL-MOTLBO algorithm outperforms established methods (e.g., NSGA-II, MOACO) in generating a diverse, converged Pareto front. To support decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied, allowing prioritization of solutions under different stakeholder preferences. Among 18 Pareto-optimal solutions, Solution 17 (RT = 98 days, RC = $295,000, REI = 320,000 kg CO₂-eq) ranks highest across all scenarios. The framework offers a robust, scalable method for sustainable retrofit planning, integrating economic and environmental objectives into data-driven decision-making.