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<p>This study presents a comprehensive multi-objective optimization framework for retrofitting projects by integrating seven critical performance dimensions: time, cost, quality, energy consumption, safety, environmental impact, and client satisfaction. A novel opposition-based non-dominated sorting genetic algorithm III (OBNSGA-III) is proposed to address the high dimensionality and complex trade-offs inherent in retrofitting decisions. Key innovations include the dual application of opposition-based learning during population initialization and offspring generation, the use of a bivariate normal distribution to model quality as a function of time and cost, and the application of fuzzy logic for safety risk evaluation. The proposed framework is validated using a real-world case study involving 11 retrofitting aspects and 33 intervention options. The OBNSGA-III algorithm successfully generated 18 Pareto-optimal solutions. Among them, the best-performing solution achieved a project duration of 30 days, a quality index of 0.913, and a client satisfaction score of 4.7, outperforming benchmark algorithms such as NSGA-III, MOPSO, and OB-MODE across 13 standard performance indicators, including hypervolume (0.92) and generational distance (1.35). These results underscore the model’s ability to deliver diverse, high-quality trade-off solutions under real-world constraints. The TCQESEC framework provides a robust decision-support tool for project managers and policymakers, enabling sustainable, efficient, and client-centric retrofitting strategies in complex urban infrastructure environments.</p>

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Balancing complexity in retrofitting: an opposition-based NSGA-III framework for time, cost, quality, energy, safety, environment, and client satisfaction

  • 期刊论文

  • 发表日期：

    2025年10月15日

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  • Miguel Villagómez-Galindo,
  • Ana Beatriz Martínez-Valencia,
  • Sudhanshu Maurya,
  • Sushma Jat,
  • Gaurav Shrivastava,
  • T. C. Manjunath

摘要

This study presents a comprehensive multi-objective optimization framework for retrofitting projects by integrating seven critical performance dimensions: time, cost, quality, energy consumption, safety, environmental impact, and client satisfaction. A novel opposition-based non-dominated sorting genetic algorithm III (OBNSGA-III) is proposed to address the high dimensionality and complex trade-offs inherent in retrofitting decisions. Key innovations include the dual application of opposition-based learning during population initialization and offspring generation, the use of a bivariate normal distribution to model quality as a function of time and cost, and the application of fuzzy logic for safety risk evaluation. The proposed framework is validated using a real-world case study involving 11 retrofitting aspects and 33 intervention options. The OBNSGA-III algorithm successfully generated 18 Pareto-optimal solutions. Among them, the best-performing solution achieved a project duration of 30 days, a quality index of 0.913, and a client satisfaction score of 4.7, outperforming benchmark algorithms such as NSGA-III, MOPSO, and OB-MODE across 13 standard performance indicators, including hypervolume (0.92) and generational distance (1.35). These results underscore the model’s ability to deliver diverse, high-quality trade-off solutions under real-world constraints. The TCQESEC framework provides a robust decision-support tool for project managers and policymakers, enabling sustainable, efficient, and client-centric retrofitting strategies in complex urban infrastructure environments.