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<p>The construction of wastewater treatment plants (WWTPs) is a complex, multi-objective decision-making problem involving trade-offs between project duration, cost, environmental impact, energy efficiency, and land use. Traditional project planning approaches often focus on cost and time minimization while neglecting sustainability considerations. This study proposes a hybrid multi-objective optimization framework integrating Non-Dominated Sorting Genetic Algorithm III (NSGA-III) and Multi-Objective Differential Evolution (MODE) to generate Pareto-optimal solutions that balance competing objectives in WWTP construction. The proposed framework formulates a multi-objective optimization model incorporating constraints such as budget limits, execution mode dependencies, and environmental impact thresholds. A case study demonstrates the effectiveness of the hybrid approach, showing that accelerated construction modes reduce project duration but increase cost and emissions, whereas eco-friendly modes improve energy efficiency and sustainability at the expense of longer timelines. The results indicate that the hybrid NSGA-III and MODE approach outperforms traditional optimization techniques, producing diverse and well-distributed Pareto-optimal solutions. This research provides a robust decision-support tool for policymakers, engineers, and project managers, enabling them to select the most suitable WWTP execution strategy based on project priorities. The findings contribute to advancing sustainable infrastructure development, ensuring cost-effective, time-efficient, and environmentally responsible wastewater management solutions.</p>

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A hybrid multi-objective optimization approach for wastewater treatment plant construction: balancing time, cost, environmental impact, energy efficiency, and land use using NSGA-III and MODE

  • 期刊论文

  • 发表日期：

    2025年3月17日

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  • Vijay Kumar,
  • Lilesh Gautam,
  • Ritu Dahiya

摘要

The construction of wastewater treatment plants (WWTPs) is a complex, multi-objective decision-making problem involving trade-offs between project duration, cost, environmental impact, energy efficiency, and land use. Traditional project planning approaches often focus on cost and time minimization while neglecting sustainability considerations. This study proposes a hybrid multi-objective optimization framework integrating Non-Dominated Sorting Genetic Algorithm III (NSGA-III) and Multi-Objective Differential Evolution (MODE) to generate Pareto-optimal solutions that balance competing objectives in WWTP construction. The proposed framework formulates a multi-objective optimization model incorporating constraints such as budget limits, execution mode dependencies, and environmental impact thresholds. A case study demonstrates the effectiveness of the hybrid approach, showing that accelerated construction modes reduce project duration but increase cost and emissions, whereas eco-friendly modes improve energy efficiency and sustainability at the expense of longer timelines. The results indicate that the hybrid NSGA-III and MODE approach outperforms traditional optimization techniques, producing diverse and well-distributed Pareto-optimal solutions. This research provides a robust decision-support tool for policymakers, engineers, and project managers, enabling them to select the most suitable WWTP execution strategy based on project priorities. The findings contribute to advancing sustainable infrastructure development, ensuring cost-effective, time-efficient, and environmentally responsible wastewater management solutions.