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<p>This study presents a multi-objective optimization approach for enhancing responsible sourcing, consumption, and production in construction supply chains, aligning with Sustainable Development Goal 12 (SDG 12). Using the Non-Dominated Sorting Genetic Algorithm III (NSGA-III), the research addresses complex trade-offs between environmental impact, cost-effectiveness, and social responsibility in construction projects. Data from industry case studies, including real-world construction projects, and simulations reflecting varying material costs, emissions regulations, and logistical challenges were used to validate the model. The findings reveal Pareto-efficient solutions, with up to a 9.4% reduction in carbon emissions and 3.3% cost savings while achieving a 7% improvement in social responsibility metrics. Sensitivity analysis demonstrates the model’s robustness to changes in material costs and supply chain disruptions. These results underscore NSGA-III’s effectiveness in generating optimized solutions that minimize environmental footprint, enhance resource efficiency, and promote ethical practices. This research provides actionable insights for construction firms and policymakers, offering a scalable model to integrate sustainable practices into construction supply chains and advance SDG 12 objectives.</p>

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Multi-objective optimization of responsible sourcing, consumption, and production in construction supply chains: an NSGA-III approach toward achieving SDG 12

  • 期刊论文

  • 发表日期：

    2025年1月8日

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  • Manish Bharadwaj,
  • Manoj Patwardhan,
  • Kamal Sharma

摘要

This study presents a multi-objective optimization approach for enhancing responsible sourcing, consumption, and production in construction supply chains, aligning with Sustainable Development Goal 12 (SDG 12). Using the Non-Dominated Sorting Genetic Algorithm III (NSGA-III), the research addresses complex trade-offs between environmental impact, cost-effectiveness, and social responsibility in construction projects. Data from industry case studies, including real-world construction projects, and simulations reflecting varying material costs, emissions regulations, and logistical challenges were used to validate the model. The findings reveal Pareto-efficient solutions, with up to a 9.4% reduction in carbon emissions and 3.3% cost savings while achieving a 7% improvement in social responsibility metrics. Sensitivity analysis demonstrates the model’s robustness to changes in material costs and supply chain disruptions. These results underscore NSGA-III’s effectiveness in generating optimized solutions that minimize environmental footprint, enhance resource efficiency, and promote ethical practices. This research provides actionable insights for construction firms and policymakers, offering a scalable model to integrate sustainable practices into construction supply chains and advance SDG 12 objectives.