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<p>Facility layout planning is crucial in construction projects due to its significant impact on project time and cost. The strategic location and capacity selection of tower cranes, given their high cost, are essential components of this process, alongside the placement of material supply point. Addressing this complex, combinatorial, and NP-hard decision-making problem, this study employs a comprehensive analysis of ten advanced metaheuristic algorithms to optimize the type and location of tower cranes with material supply points at construction sites. By formulating the problem as a mathematical model, the objective function seeks to minimize the overall material transportation cost while considering job site constraints. To evaluate the performance of these algorithms, we designed three real-world scenarios, providing a robust assessment framework. Our findings highlight that Ant Colony Optimization (ACO) delivers superior performance compared to other algorithms, excelling in both execution time and cost efficiency in this problem. This study's contribution lies in its exhaustive approach to problem-solving, offering valuable insights into algorithmic performance across varied construction scenarios.</p>

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Tower crane location optimization problem: a comprehensive metaheuristic algorithm approach

  • 期刊论文

  • 发表日期：

    2025年3月19日

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  • Roya Amiri,
  • Amirhossein Tahmouresi,
  • Vahid Momenaei Kermani,
  • Seyedali Mirjalili,
  • Javad Majrouhi Sardroud

摘要

Facility layout planning is crucial in construction projects due to its significant impact on project time and cost. The strategic location and capacity selection of tower cranes, given their high cost, are essential components of this process, alongside the placement of material supply point. Addressing this complex, combinatorial, and NP-hard decision-making problem, this study employs a comprehensive analysis of ten advanced metaheuristic algorithms to optimize the type and location of tower cranes with material supply points at construction sites. By formulating the problem as a mathematical model, the objective function seeks to minimize the overall material transportation cost while considering job site constraints. To evaluate the performance of these algorithms, we designed three real-world scenarios, providing a robust assessment framework. Our findings highlight that Ant Colony Optimization (ACO) delivers superior performance compared to other algorithms, excelling in both execution time and cost efficiency in this problem. This study's contribution lies in its exhaustive approach to problem-solving, offering valuable insights into algorithmic performance across varied construction scenarios.