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【科苑经管国际学术论坛】Hing Kai Chan:Improve Supply Chain and Logistics Forecasting by Machine Learning Techniques(7月4日)
发布时间:2018/7/3

科苑经管国际学术论坛

 

报告题目:Improve Supply Chain and Logistics Forecasting by Machine Learning Techniques
 

报告人:Hing Kai Chan,University of Nottingham
 

报告时间:2018年7月4日(周三)上午10:45—12:00
 

报告地点:中国科学院大学中关村校区青年公寓7号楼401
 

报告摘要:Data driven research has been very popular in the last few years. Machine learning is an important element of this strand of research. In this presentation, machine learning approaches are employed to make prediction on two supply chain and logistics applications: the demand of healthcare products and the container throughput of a port. The two applications share some similarities: (i) A number of socio-economic data are extracted and considered as the inputs to the prediction model; (ii) Some of these parameters are not directly linked to the prediction in previous studies; and (iii) A correlation or cluster analysis is first conducted to reveal the relationship of those parameters. In order to verify the models, real-life data were collected and employed. Results indicate that incorporating such data, some of which are apparently irrelevant to the prediction, could improve the forecasting accuracy. It is also found that various machine learning approaches are also useful in the prediction, compared to some traditional time-series models, such as ARIMA. That being said, machine learning approaches are not necessarily the best method. In this presentation, more details will be discussed.