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<p>The construction of groundwater pollution treatment plants presents a complex scheduling challenge due to the need to balance timely project delivery, cost-efficiency, carbon footprint reduction, and environmental risk mitigation. Traditional scheduling models often overlook these multidimensional sustainability objectives. This study proposes a novel hybrid optimization–decision-making framework integrating non-dominated sorting genetic algorithm III (NSGA-III) with the technique for order of preference by similarity to ideal solution (TOPSIS) to generate and rank Pareto-optimal construction schedules. A real-world case study of an 18-activity, 5 MLD groundwater treatment plant in North India is analyzed, considering three execution modes per activity—fast-track (FTM), balanced (BEM), and eco-sustainable (ESEM). Each mode is characterized by distinct durations, costs, carbon emissions, and risk levels. The NSGA-III algorithm efficiently explores the solution space, producing diverse Pareto fronts, while TOPSIS aids in selecting the most balanced schedule. Results demonstrate that the proposed framework outperforms existing methods (e.g., NSGA-II, MOPSO, MOACO) in terms of IGD, HV, and coverage metrics. Strong trade-offs and correlations are observed among the four objectives, highlighting the need for integrated decision-making in sustainable infrastructure development. This study offers a replicable, optimization-driven approach for policy-makers and practitioners to schedule treatment plant construction projects under sustainability constraints.</p>

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Sustainable multi-objective scheduling of groundwater treatment plant construction using NSGA-III–TOPSIS

  • 期刊论文

  • 发表日期：

    2026年4月2日

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  • Bijay Kumar Singh,
  • Jasvir Singh

摘要

The construction of groundwater pollution treatment plants presents a complex scheduling challenge due to the need to balance timely project delivery, cost-efficiency, carbon footprint reduction, and environmental risk mitigation. Traditional scheduling models often overlook these multidimensional sustainability objectives. This study proposes a novel hybrid optimization–decision-making framework integrating non-dominated sorting genetic algorithm III (NSGA-III) with the technique for order of preference by similarity to ideal solution (TOPSIS) to generate and rank Pareto-optimal construction schedules. A real-world case study of an 18-activity, 5 MLD groundwater treatment plant in North India is analyzed, considering three execution modes per activity—fast-track (FTM), balanced (BEM), and eco-sustainable (ESEM). Each mode is characterized by distinct durations, costs, carbon emissions, and risk levels. The NSGA-III algorithm efficiently explores the solution space, producing diverse Pareto fronts, while TOPSIS aids in selecting the most balanced schedule. Results demonstrate that the proposed framework outperforms existing methods (e.g., NSGA-II, MOPSO, MOACO) in terms of IGD, HV, and coverage metrics. Strong trade-offs and correlations are observed among the four objectives, highlighting the need for integrated decision-making in sustainable infrastructure development. This study offers a replicable, optimization-driven approach for policy-makers and practitioners to schedule treatment plant construction projects under sustainability constraints.