| Summary: | 碩士 === 國立臺灣大學 === 工業工程學研究所 === 106 === There are many uncertain supply and uncertain demand supply issues in supply chain management and logistics process, such as supply disturbance and demand instability; manufacturing process, material delivery, production quality and market order stochastic. Abundant of previous papers discuss about one side uncertainty, and the research work on both side uncertainties is not a lot. A bike-sharing concept of supply and demand, which is determine surplus side or deficit side by the stock level of the site. Surplus sites with more stocks supply some stocks to deficit station. The basic data means the setting of surplus deficit site using to generate sample probability, and then the supply and demand functions required by matching model are obtained through conversion. This matching uncertainty supply demand model can be separate into two stage model: discontinuous dispatching model and continuous dispatching model. Simulation method can generate many cases, and these cases can be used to explore the feature of random supply and demand cases. Parameter settings and model output discussion would give out reason to choose variable to use in machine learning model. Machine learning model can fit the function of the data pre-processing and scheduling logic behind the data. There are three issues about data format, variable in training data and data volume are researched. After the best model under this research been found, this model could be applied to obtain relocation information without data pre-processing and optimization calculating work.
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