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<p>To avoid project overruns, it is critical in the construction business to inspect and supervise progress at each step of development. In an efficient circumstance, accomplishing a building project in the minimum amount of time and money is critical. The current research focuses on Genetic Algorithm (GA) for scheduling and optimization solvers in the MATLAB platform for bridge construction. The bridge construction activities are planned and scheduled using Primavera software. In general, bridge construction projects are iterative, large, and complex, and thus necessitate a thorough examination of performance data under extreme conditions. The GA optimization solvers’ performance is directly related to the GA parameters that have been determined based on the project’s aim and actual duration project for a viable solution. Shortening the duration of any construction project typically involves hiring additional labour, using more construction equipment, and adopting specialized building procedures, all of which increase costs and time accountability for the client. Finally, it is concluded that Genetic Algorithms (GA) provide fitness functions for time cost optimization of bridge construction projects by computing objective functions and constraints. It is also concluded that Time Cost Optimization was successful in reducing the total costs of the bridge project.</p>

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Genetic Algorithm-Based Optimization of Time and Cost in Bridge Construction: A Case Study

  • 期刊论文

  • 发表日期：

    2026年2月3日

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  • Lalitesh Sinha,
  • Akash Jaiswal,
  • Archana Keerti Chowdhary

摘要

To avoid project overruns, it is critical in the construction business to inspect and supervise progress at each step of development. In an efficient circumstance, accomplishing a building project in the minimum amount of time and money is critical. The current research focuses on Genetic Algorithm (GA) for scheduling and optimization solvers in the MATLAB platform for bridge construction. The bridge construction activities are planned and scheduled using Primavera software. In general, bridge construction projects are iterative, large, and complex, and thus necessitate a thorough examination of performance data under extreme conditions. The GA optimization solvers’ performance is directly related to the GA parameters that have been determined based on the project’s aim and actual duration project for a viable solution. Shortening the duration of any construction project typically involves hiring additional labour, using more construction equipment, and adopting specialized building procedures, all of which increase costs and time accountability for the client. Finally, it is concluded that Genetic Algorithms (GA) provide fitness functions for time cost optimization of bridge construction projects by computing objective functions and constraints. It is also concluded that Time Cost Optimization was successful in reducing the total costs of the bridge project.