Lewen Yin



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Lewen Yin, Applied Mathematics
Faculty Mentor: Professor Joaquin Carbonara, Mathematics

The purpose of my project was to predict transportation rates in new regions for existing business models. In the current market, many supply-chain models exist to support managerial decisions - expansion, closure, acquisition, and organic growth. In most cases, the current state is not optimal in terms of cost, efficiency, or service. Network modeling enables us to diagnose the current state and examine what alternative scenarios might look like. Currently, Rich’s has transportation rates for existing markets. They need to expand their major Distribution Centers (DCs). They have historical transportation rates into those markets for the weight brackets and transportation modes (Truckload TL, Multi-Stop TL, Less than Truckload LTL, Intermodal IM) that they use. When they do network design problems and try to assess if there should be a new DC added, they have historical transportation rates to use for the potential lanes. So, we need to use data analysis methods and the historical rates to predict costs for new lanes. Rather than using the straight market rates, we run a comparison between historical rates and the market rates from TransPlace for the same lanes, to get a ratio, such as 1.1 or 0.87, depending on mode. In this project, we focus on the MSTL mode (Multi Stop Truck Loads), and we use Python to process data analysis and prediction. Then we discuss how to create a better approach for calculating the ratio for the optimization of urban transportation. We first need to create a better approach for calculating the ratio, then we need to explore differences in high density lanes (frequency of shipments), variations in load size, and market regions (urban to rural and vice versa). Finally, we incorporate external data (population density, employment rate, etc.) into the prediction.

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Physical Sciences and Mathematics

Finding Optimal Transportation Rates in New Regions
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