AI 中文总结
针对传统RTN模型计算效率与适用性受限的问题，提出连续RTN模型，其采用连续变量建模离散工业过程约束，求解速度较传统模型快10倍且成本更低。
AI 中文摘要
资源-任务网络（RTN）模型已被广泛应用于表示炼钢等复杂工业过程（IP）的技术约束，为工业需求响应提供基础。然而，传统RTN模型包含大量二元变量，且对非柔性和柔性过程采用不同的建模形式，限制了其计算效率与适用性。为系统性提升工业过程模型的计算性能，我们提出连续RTN模型（cRTN），这是一种新型建模方法，采用连续变量表示生产任务与进度，并将其整合为统一且计算友好的形式，用于离散工业过程的技术约束，包括资源平衡、任务执行、等待时间限制和生产目标。与传统模型相比，cRTN在保持相同精度的同时，具有更少的二元变量、更短的求解时间和更好的可扩展性。基于钢铁厂的数值测试表明，典型情况下cRTN的求解速度比传统模型快10倍，且随着批次规模增大仍保持可处理性，而传统模型中批次规模增大会导致问题规模变大、求解时间不可行。cRTN还通过解决传统模型中报告的舍入误差问题，实现了能源成本的降低。
英文摘要
The resource-task network (RTN) model has been widely applied to represent the technical constraints of complex industrial processes (IPs) such as steel-making, providing the basis for industrial demand response. However, the legacy RTN model contains numerous binary variables and applies different formulations for non-flexible and flexible processes, restricting its computational efficiency and applicability. To systematically improve the computational performance of IP models, we propose continuous RTN model (cRTN), a novel modeling approach that uses continuous variables to represent production tasks and progresses, which are then integrated into unified as well as computationally favorable formulations for the technical constraints in discrete IPs, including resource balance, task execution, waiting time limits, and production targets. Compared to the legacy models, cRTN features fewer binary variables, shorter solving time, and better scalability while maintaining the same accuracy. Numerical tests based on a steel plant demonstrate that cRTN is in typical cases 10 times faster than legacy models and remains tractable with increasing batch sizes, which in legacy models leads to larger problem scales and infeasible solving time. cRTN also achieves a reduction in energy costs by resolving the issue of rounding errors reported in legacy models.
CommentsPublished in: IEEE Transactions on Smart Grid ( Volume: 16, Issue: 6, November 2025)
