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Related Experiment Video

Updated: Aug 11, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Multiple objective immune wolf colony algorithm for solving time-cost-quality trade-off problem.

Guanyi Liu1, Xuemei Li1, Khalid Mehmood Alam2

  • 1Department of Economics and Management, Beijing Jiaotong University, Beijing, Beijing, China.

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|February 9, 2023
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Summary
This summary is machine-generated.

This study introduces a novel nonlinear time-cost-quality model for construction projects, incorporating a bonus-penalty mechanism. An optimized algorithm effectively balances project time, cost, and quality, demonstrated through a railway project example.

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Area of Science:

  • Construction Management
  • Optimization Algorithms
  • Project Management

Background:

  • The time-cost-quality trade-off is crucial in construction projects.
  • Traditional models often simplify the complex relationships between these factors.
  • There is a need for advanced methods to optimize project outcomes.

Purpose of the Study:

  • To establish a nonlinear time-cost-quality model for construction projects.
  • To introduce a bonus-penalty mechanism to enhance traditional models.
  • To develop an effective optimization algorithm for solving the trade-off problem.

Main Methods:

  • Developed a nonlinear time-cost-quality model considering quality's relationship with time.
  • Introduced a bonus-penalty mechanism into the time-cost model.
  • Proposed a multi-objective immune wolf colony optimization algorithm, combining wolf colony and immune algorithms.

Main Results:

  • The proposed nonlinear model effectively captures the complex trade-offs.
  • The hybrid optimization algorithm demonstrated improved accuracy and convergence.
  • The method's effectiveness was validated using a real-world railway construction project.

Conclusions:

  • The developed nonlinear time-cost-quality model with a bonus-penalty mechanism offers a robust approach.
  • The multi-objective immune wolf colony optimization algorithm provides an efficient solution for complex project trade-offs.
  • This integrated approach enhances decision-making for optimizing construction project time, cost, and quality.