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April 22, 2026Applied Computational Intelligence and Soft Computing0 citationsOpen Access

Enhancing Construction Shift Schedules With the Sand Cat Arithmetic Optimization Algorithm

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VPVu Hong Son PhamTDThu DauLHLe Gia Phuc Huynh

Key Points

  • The aim is to optimize shift scheduling by balancing time, cost, and night shifts using a new algorithm.
  • Utilized a hybrid sand cat arithmetic optimization algorithm (SC-AOA).
  • Integrated sand cat swarm optimization (SCSO) and arithmetic optimization algorithm (AOA).
  • Conducted case studies to validate effectiveness.
  • SC-AOA showed superior performance in optimizing time-cost-utilization work shift trade-off.
  • Efficiently utilized search and exploitation factors for better results.
  • Demonstrated flexibility in adjusting to various scheduling scenarios.

Abstract

This paper addresses shift scheduling to optimize time and cost while considering the impact of night shifts. It involves determining work shift options, sequencing project tasks, and taking labor constraints into account to provide options that optimize time‐cost trade‐off (TCTO), and the number of night shift hours, or can be called as the time‐cost–utilization work shift trade‐off (TCUT) problem. The approach involves the use of a hybrid sand cat arithmetic optimization algorithm (AOA) (SC‐AOA), which integrates sand cat swarm optimization (SCSO) and AOA. This method balances exploration and exploitation phases effectively, improving convergence accuracy and avoiding local optima. The SC‐AOA model is validated through case studies, demonstrating superior performance in optimizing TCUT compared to previous algorithms. Results from experiments on two specific case studies indicated that the SC‐AOA efficiently utilized factors during the search and exploitation processes, while also flexibly adjusting to achieve optimal results. This confirms that SC‐AOA is a powerful and efficient tool in construction project management. The paper’s approach addresses the critical TCUT problem in construction management, providing a more efficient and flexible scheduling solution. Its validation through real‐world case studies highlights its practical applicability and potential to significantly improve construction project outcomes.

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Cite This Study

Pham et al. (2026) studied this question.

synapsesocial.com/papers/69e865126e0dea528dde9b30https://doi.org/10.1155/acis/9939592
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