سال انتشار:

2020

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عنوان انگلیسی مقاله:

A deep reinforcement learning approach for chemical production scheduling

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ترجمه فارسی عنوان مقاله:

یک رویکرد یادگیری تقویتی عمیق برای برنامه ریزی تولید مواد شیمیایی

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منبع:

Sciencedirect - Elsevier - Computers and Chemical Engineering, 141 (2020) 106982. doi:10.1016/j.compchemeng.2020.106982

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نویسنده:

Christian D. Hubbs a , Can Li a , Nikolaos V. Sahinidis a , ∗, Ignacio E. Grossmann a , John M. Wassick b

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چکیده انگلیسی:

This work examines applying deep reinforcement learning to a chemical production scheduling process to account for uncertainty and achieve online, dynamic scheduling, and benchmarks the results with a mixed-integer linear programming (MILP) model that schedules each time interval on a receding horizon basis. An industrial example is used as a case study for comparing the differing approaches. Results show that the reinforcement learning method outperforms the naive MILP approaches and is competitive with a shrinking horizon MILP approach in terms of profitability, inventory levels, and customer service. The speed and flexibility of the reinforcement learning system is promising for achieving real-time optimiza- tion of a scheduling system, but there is reason to pursue integration of data-driven deep reinforcement learning methods and model-based mathematical optimization approaches.
Keywords: Machine learning | Reinforcement learning | Optimization | Scheduling | Stochastic programming

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قیمت: رایگان

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توضیحات اضافی:

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