Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …

Data-driven decision-making has garnered growing interest as a result of the increasing
availability of data in recent years. With that growth many opportunities and challenges have …

Incorporating side observations in decision making can reduce uncertainty and boost
performance, but it also requires that we tackle a potentially complex predictive relationship …

We develop a tractable and flexible data-driven approach for incorporating side information
into multi-stage stochastic programming. The proposed framework uses predictive machine …

Data driven optimisation has become one of the research frontiers in operations
management and operations research. Likewise, the recent academic interest in big data …

Big data provide new opportunities to tackle one of the main difficulties in decision-making
systems—the uncertain behavior that follows unknown probability distribution. Standard …

The predict-then-optimize framework is fundamental in practical stochastic decision-making
problems: first predict unknown parameters of an optimization model, then solve the problem …

Data-driven optimization uses contextual information and machine learning algorithms to
find solutions to decision problems with uncertain parameters. While a vast body of work is …

The development of the Internet of things (IoT) and online platforms enables companies and
governments to collect data from a much broader spatial and temporal area in the logistics …

In the operations research literature, data driven analyses using big data are receiving more
and more interest and attention. However, big data driven operational analyses are still …