Proposal Summary and Objectives

Efficient movement of freight is vital to the economic advancement of urban areas. With enormous disruptions in today's supply and demand chains, it is essential to have an accurate and connected inventory, and data to guide business decisions. Nowadays, information technology (IT) is the fuel of most supply chain business. Traditional approaches for studying international freight network design often relies on prior assumptions, which are not always accurate or even available. This is particularly true when dealing with unprecedented events. In addition, data often have high dimensions and volume, and information is hard to extract. This makes it difficult to process using traditional data processing applications and existing data management tools. Big Data (BD) is an emerging set of techniques of global interest, especially within the transportation industry. Advances in data mining techniques to support intelligent transportation systems has become a strong tool, but still underexplored. There is evidence of the enormous potential to improve supply chain disruption of freight transportation modeling using these advanced computing techniques. The development and application of advanced Machine learning (ML) models depend critically on the availability of data in tackling the freight network design problem and major disruptions. Under global disruptions, this is an invaluable feature that increase both efficiency and profitability and ensure adequate supply.

Funding Amount:
Status: Active
Duration: Sep 1, 2021 - Aug 31, 2022