Approximation schemes for data-driven optimization problems with an emphasis on inventory management

With the increase of data in our era, data-driven optimization becomes a hot topic of research. In this talk I will present provably near-optimal approximation schemes for such problems, both in single and multiple decision-making settings, and give as examples the data-driven newsvendor and single-item inventory control problems. This work has recently been accepted for publication in INFORMS Journal on Computing.



Short Bio:
Nir Halman is a senior research associate of Operations Research and Operations Management at the Jerusalem school of business administration, the Hebrew University of Jerusalem. His research focuses on developing new methodologies to either solve or approximate optimization problems with emphasis on application areas such as scheduling, operations and inventory management. His work has been published in leading journals such as those of INFORMS (OR, Math. of OR, J. on Computing) and SIAM (J. on Optimization, J. on Discrete Mathematics, J. on Computing).