عنوان انگلیسی مقاله:
Reinforcement learning framework for freight demand forecasting to support operational planning decisions
ترجمه فارسی عنوان مقاله:
چارچوب یادگیری تقویتی پیش بینی تقاضای حمل بار برای پشتیبانی از تصمیمات برنامه ریزی عملیاتی
Sciencedirect - Elsevier - Transportation Research Part E, 137 (2020) 101926. doi:10.1016/j.tre.2020.101926
Lama Al Hajj Hassan, Hani S. Mahmassani⁎, Ying Chen
Freight forecasting is essential for managing, planning operating and optimizing the use of resources.
Multiple market factors contribute to the highly variable nature of freight flows, which
calls for adaptive and responsive forecasting models. This paper presents a demand forecasting
methodology that supports freight operation planning over short to long term horizons. The
method combines time series models and machine learning algorithms in a Reinforcement
Learning framework applied over a rolling horizon. The objective is to develop an efficient
method that reduces the prediction error by taking full advantage of the traditional time series
models and machine learning models. In a case study applied to container shipment data for a US
intermodal company, the approach succeeded in reducing the forecast error margin. It also allowed
predictions to closely follow recent trends and fluctuations in the market while minimizing
the need for user intervention. The results indicate that the proposed approach is an effective
method to predict freight demand. In addition to clustering and Reinforcement Learning, a
method for converting monthly forecasts to long-term weekly forecasts was developed and tested.
The results suggest that these monthly-to-weekly long-term forecasts outperform the direct long
term forecasts generated through typical time series approaches.
Keywords: Freight demand forecasting | Time series | Reinforcement learning | Rolling horizon