This paper presents a binary optimization framework for modeling dynamic resource allocation problems. The framework (a) allows modeling flexibility by incorporating different objective functions, alternative sets of resources and fairness controls; (b) is widely applicable in a variety of...

In this paper, we combine robust optimization and the idea of ∊-arbitrage to propose a tractable approach to price a wide variety of options. Rather than assuming a probabilistic model for the stock price dynamics, we assume that the conclusions of probability theory, such as the central limit...

We propose both robust and data-driven approaches to a fluid model of call centers that incorporates random arrival rates with abandonment to determine staff levels and dynamic routing policies. We test the resulting models with real data obtained from the call center of a US bank. Computational...