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<p>Subcontractor selection in construction projects involves complex decision-making due to the conflicting nature of multiple performance criteria, particularly time, cost, and quality (TCQ). This study proposes a novel Opposition-Based Grey Wolf Optimizer (OBL-GWO) to effectively solve the multi-objective TCQS trade-off optimization problem in subcontractor selection. The proposed approach integrates opposition-based learning (OBL) into the standard Grey Wolf Optimizer (GWO) to enhance population diversity, accelerate convergence, and improve exploration–exploitation balance. A discrete 20-activity construction project is considered as a case study, where each activity can be executed by alternative subcontractors with varying performance levels. The optimization framework evaluates project duration and total cost using the Critical Path Method (CPM), while quality are incorporated as additional objective functions within a multi-objective optimization setting. The performance of the proposed OBL-GWO is benchmarked against Discrete Particle Swarm Optimization (DPSO) and NDSII-GWO algorithm. To ensure a robust comparative assessment, widely accepted performance metrics, including Hypervolume (HV) and Spread (SP), are employed to evaluate the convergence and diversity of the obtained Pareto fronts. The results demonstrate that OBL-GWO consistently achieves superior performance in terms of solution quality, convergence speed, and distribution uniformity compared to the competing algorithms. Specifically, the proposed method provides a well-distributed Pareto front with higher HV values and lower SP values, indicating better diversity and proximity to the true Pareto-optimal front. The findings confirm that incorporating opposition-based learning significantly enhances the optimization capability of GWO for complex multi-objective construction problems. This study contributes a robust and efficient decision-support tool for subcontractor selection, enabling project managers to achieve balanced and sustainable trade-offs among time, cost and quality objectives.</p>

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An opposition-based multi-objective Grey Wolf optimizer for time–cost–quality trade-off optimization in subcontractor selection

  • 期刊论文

  • 发表日期：

    2026年5月27日

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  • Rakesh Gupta,
  • Miguel Villagómez-Galindo,
  • Gohar Ali,
  • T. C. Manjunath,
  • K. Ravibabu,
  • Surajit Murasingh

摘要

Subcontractor selection in construction projects involves complex decision-making due to the conflicting nature of multiple performance criteria, particularly time, cost, and quality (TCQ). This study proposes a novel Opposition-Based Grey Wolf Optimizer (OBL-GWO) to effectively solve the multi-objective TCQS trade-off optimization problem in subcontractor selection. The proposed approach integrates opposition-based learning (OBL) into the standard Grey Wolf Optimizer (GWO) to enhance population diversity, accelerate convergence, and improve exploration–exploitation balance. A discrete 20-activity construction project is considered as a case study, where each activity can be executed by alternative subcontractors with varying performance levels. The optimization framework evaluates project duration and total cost using the Critical Path Method (CPM), while quality are incorporated as additional objective functions within a multi-objective optimization setting. The performance of the proposed OBL-GWO is benchmarked against Discrete Particle Swarm Optimization (DPSO) and NDSII-GWO algorithm. To ensure a robust comparative assessment, widely accepted performance metrics, including Hypervolume (HV) and Spread (SP), are employed to evaluate the convergence and diversity of the obtained Pareto fronts. The results demonstrate that OBL-GWO consistently achieves superior performance in terms of solution quality, convergence speed, and distribution uniformity compared to the competing algorithms. Specifically, the proposed method provides a well-distributed Pareto front with higher HV values and lower SP values, indicating better diversity and proximity to the true Pareto-optimal front. The findings confirm that incorporating opposition-based learning significantly enhances the optimization capability of GWO for complex multi-objective construction problems. This study contributes a robust and efficient decision-support tool for subcontractor selection, enabling project managers to achieve balanced and sustainable trade-offs among time, cost and quality objectives.