Big Data Analytics: Optimization Models and Algorithms with Applications in Smart Food Supply Chains and Networks

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A data set is defined as big data if it is large in volume (e.g., measured in petabyte or exabyte units), grows with a high velocity (e.g., real time data from Internet of Things sensors) or comes in a variety of forms (e.g., from video, images and text). Big Data analytics is a three pronged process that uses data to: describe the trends and patterns in the data using visualization and graphics techniques; predict future patterns using statistical and data mining tools; and prescribe alternatives for decision makers using optimization and simulations tools. With the wide availability of data, both in large volumes and high-speed streaming data, supply chain partners are looking for ways to harness this data for optimization. In this proposal we introduce the concept of big data shared analytics and focus on developing innovative methodologies and solutions for complex problems for big data shared analytics in smart supply chains. This 5-year research program includes two theoretical areas 1) Optimization in decentralized environments with big data, 2) Multi-level optimization in decentralized environments and two application areas: 3) Sustainable operations in smart food supply chains, and 4) Network Analytics.  The impacts of this research program will be felt at two levels. 1) From a scientific point of view, we will introduce new complex problems in the growing field of optimization with big data that have not yet been treated in the scientific literature. We will study their fundamental properties and develop new models and algorithms to solve them. 2) From an economic and societal point of view, this research program addresses problems of major importance for supply chain planners. Our models and algorithms will integrate and balance economic, environmental and social concerns. In our research on collaboration logistics platforms we will propose new models for new problems in an important area of the digital economy: shared collaboration platforms. Finally, our research in the area of food supply chains will provide companies with tools to increase their efficiency while at the same time lowering their impact on the environments and the society at large. Our proposed research will support in training 20 highly qualified personnel (HQP) over five years. These HQPs will be trained in big data analytics and help fill the marketplace needs in this growing area.