Opposition-Based NSGA-III Framework for Multi-Objective Optimization of Retrofitting Projects: Balancing Time, Cost, Quality, Energy, Safety, and Environmental Impact

Abstract

This study presents a hybrid multi-objective optimization framework combining NSGA-III with Opposition-Based Learning (OBL) to improve urban infrastructure retrofitting by optimizing project duration, cost, quality, safety, and carbon emissions.

Introduction

This research proposes a novel Time–Cost–Quality–Energy–Safety–Environmental Impact Trade-off (TCQESET) optimization framework for building retrofitting projects using an Opposition-Based Non-Dominated Sorting Genetic Algorithm III (OBNSGA-III). The framework is validated through a real-world commercial retrofitting case study in Delhi-NCR to demonstrate how multi-objective optimization can support sustainable, cost-effective, and performance-oriented decision-making. The study aims to optimize six conflicting objectives simultaneously: minimizing project time, cost, energy consumption, safety risk, and environmental impact while maximizing quality. It also enhances the standard NSGA-III algorithm with Opposition-Based Learning (OBL) to improve population diversity, exploration capability, convergence speed, and solution quality. Performance is compared with benchmark algorithms such as NSGA-III, MOPSO, and OB-MODE using metrics including Generational Distance, Hypervolume, Spacing Metric, Quality Metric, and Computational Time.

The proposed methodology models a retrofitting project as a set of multiple activities, each having several alternative retrofitting options characterized by different values of time, cost, quality, energy consumption, safety, and environmental impact. The OBNSGA-III algorithm efficiently searches the solution space to identify Pareto-optimal solutions that balance these six objectives. The model is validated using an established benchmark problem from previous research and subsequently applied to a real commercial retrofitting project in Delhi-NCR consisting of 11 retrofit aspects, including structural reinforcement, energy efficiency improvements, safety enhancements, environmental impact reduction, smart building integration, water efficiency, waste management, and community engagement. Each aspect includes three alternative retrofit options with quantified performance indicators.

The case study generated 22 Pareto-optimal solutions, each representing a different combination of retrofit strategies and illustrating trade-offs among project duration, cost, quality, energy consumption, safety risk, and environmental impact. The solutions varied considerably, with project durations ranging from 50 to 96 days, costs averaging approximately ?2.61 million, quality indices around 0.86, average energy consumption of 1238.68 kWh, safety risk score of 2.86, and environmental impact of 1.32 kg CO?-equivalent. These diverse solutions enable decision-makers to select retrofit strategies based on project priorities, whether emphasizing lower cost, shorter completion time, improved quality, reduced energy use, enhanced safety, or lower environmental impact.

The results demonstrate that the proposed OBNSGA-III framework effectively handles the complex multi-objective nature of retrofit planning by generating well-distributed Pareto-optimal solutions that provide balanced trade-offs among conflicting objectives. Trade-off plots and comparative analyses further illustrate the relationships between different performance criteria, helping planners understand the impact of selecting one objective over another. Overall, the proposed TCQESET optimization framework offers a practical and robust decision-support tool for sustainable infrastructure retrofitting, enabling urban planners and construction professionals to make informed, data-driven decisions that improve project performance while supporting long-term sustainability goals.

Conclusion

This research proposed and validated a novel hybrid multi-objective optimization framework for enhancing the performance of urban infrastructure retrofitting projects. Recognizing the limitations of traditional Time–Cost–Trade-Off (TCT) models, the study introduced a many-objective formulation integrating five critical performance dimensions: project duration, cost, quality, safety, and carbon emissions.

References

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Copyright

Copyright © 2026 Shivam Dwivedi, Rakesh Gupta, Mukesh Pandey. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.