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Data Science, Stochastic and Optimization

Mission Statement

Production and Operations Management has focused on developing models to generate understandings and facilitate decision making. With the emergence of new technologies, concepts, and business models, it has become increasingly important to integrate data into our research. In many other fields, the amount of effort dedicated to data analytics has been increasing at an accelerated speed in the past few years. Yet, the science of data analytics for production and operations management is still not mature. The department of "Data Science, Stochastic and Optimization" is established to encourage contributions in this area. We invite submissions that either applies data science, stochastic models and optimization techniques to develop novel models to address production and operations management issues, or advance the methodologies to enhance machine learning/artificial intelligence, stochastic models and optimization techniques that can address business challenges in a wide range of applications in production and operations management. We highly encourage research that integrates machine learning/artificial intelligence into stochastic analysis and optimization.

Many new concepts, technologies, business models have rapidly appeared along with the rapid generation of variety of data. Studies that explore the implications of, for example, Block Chain, Social Network, Shared Economy, Online Market Place, and Industrial Internet of Things on production and operations decision making require innovative modeling and sometimes new analytical approaches. As many concepts and technologies are still evolving and highly dynamic, we wish to work with the authors to shape the directions of research in this new area to make our unique presence.

We also wish to articulate that mere applications of machine learning/artificial intelligence, stochastic or optimization approaches on some data or contexts are not the focus of this department. For example, it may be more appropriate to submit to the department for which the domain of the study is concerned when the major contribution is on generating new insights to the specific application, even though machine learning/artificial intelligence, stochastic or optimization approaches are used.

Departmental Editors

Professor Qi (Annabelle) Feng
Purdue University

Professor Zuo-Jun (Max) Shen
University of California at Berkeley

Senior Editors

Anil Aswani, UC Berkeley
Xin Chen, University of Illinois Urbana Champion
Leon Chu, University of Southern California
William B. Haskell, National University of Singapore
Ho-Yin Mak, University of Oxford
Nan Yang, University of Miami

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