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<p>This study proposes a Digital Twin-integrated multi-objective dynamic operation scheduling (MO-DOS) framework for pipeline planning in industrial oil transportation. The framework addresses the complexity of multi-objective scheduling under practical operational constraints, including transport duration, energy efficiency, resource utilization, and safety risk. Pareto front optimization is systematically integrated with the Dynamic Window Approach (DWA) to enable real-time dynamic routing under these competing objectives. To improve performance in complex field environments, a field-adaptive heuristic extension, DWA-H, is incorporated into DWA. By integrating high-fidelity virtual models with physical pipeline networks, the Digital Twin-based platform supports real-time scheduling, monitoring, and optimization in industrial oil processing facilities. The proposed framework is validated using real transportation work orders and benchmarked against the traditional First-In-First-Out (FIFO) strategy and the standard MO-DOS with DWA method. Experimental results demonstrate that the proposed Digital Twin-enabled MO-DOS framework improves oil transport efficiency by approximately 54.7% and reduces operational costs by about 25%. This study establishes a scalable and deployable multi-objective scheduling framework, demonstrating the integration of Digital Twin technology and dynamic optimization for industrial applications.</p>

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Multi-objective dynamic operation scheduling framework: practical application of digital twin in an edible oil processing plant

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月21日

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  • Chia-Jen Lin,
  • Chin-Sheng Chen,
  • Feng-Chieh Lin

摘要

This study proposes a Digital Twin-integrated multi-objective dynamic operation scheduling (MO-DOS) framework for pipeline planning in industrial oil transportation. The framework addresses the complexity of multi-objective scheduling under practical operational constraints, including transport duration, energy efficiency, resource utilization, and safety risk. Pareto front optimization is systematically integrated with the Dynamic Window Approach (DWA) to enable real-time dynamic routing under these competing objectives. To improve performance in complex field environments, a field-adaptive heuristic extension, DWA-H, is incorporated into DWA. By integrating high-fidelity virtual models with physical pipeline networks, the Digital Twin-based platform supports real-time scheduling, monitoring, and optimization in industrial oil processing facilities. The proposed framework is validated using real transportation work orders and benchmarked against the traditional First-In-First-Out (FIFO) strategy and the standard MO-DOS with DWA method. Experimental results demonstrate that the proposed Digital Twin-enabled MO-DOS framework improves oil transport efficiency by approximately 54.7% and reduces operational costs by about 25%. This study establishes a scalable and deployable multi-objective scheduling framework, demonstrating the integration of Digital Twin technology and dynamic optimization for industrial applications.