Phebe Vayanos

Robust Optimization with Decision-Dependent Information Discovery

  • Phebe Vayanos
  • Angelos Georghiou
  • Han Yu
    • Categories Robust Optimization Tags , , ,

    Robust optimization (RO) is a popular paradigm for modeling and solving two- and multi-stage decision-making problems affected by uncertainty. In many real-world applications, such as R&D project selection, production planning, or preference elicitation for product or policy recommendations, the time of information discovery is decision-dependent and the uncertain parameters only become observable after an often costly … Read more

    Robust Multiclass Queuing Theory for Wait Time Estimation in Resource Allocation Systems

  • Chaithanya Bandi
  • Nikolaos Trichakis
  • Phebe Vayanos
    • Categories Applications - OR and Management Sciences, Robust Optimization

    In this paper we study systems that allocate different types of scarce resources to heterogeneous allocatees based on predetermined priority rules, e.g., the U.S. deceased-donor kidney allocation system or the public housing program. We tackle the problem of estimating the wait time of an allocatee who possesses incomplete system information with regard, for example, to … Read more

    Data-driven learning in dynamic pricing using adaptive optimization

  • Dimitris Bertsimas
  • Phebe Vayanos
    • Categories Applications - OR and Management Sciences, Robust Optimization Tags , , , , , ,

    We consider the pricing problem faced by a retailer endowed with a finite inventory of a product offered over a finite planning horizon in an environment where customers are price-sensitive. The parameters of the product demand curve are fixed but unknown to the seller who only has at his disposal a history of sales data. … Read more

    A constraint sampling approach for multi-stage robust optimization

  • Daniel Kuhn
  • Berc Rustem
  • Phebe Vayanos
    • Categories Robust Optimization Tags , , ,

    We propose a tractable approximation scheme for convex (not necessarily linear) multi-stage robust optimization problems. We approximate the adaptive decisions by finite linear combinations of prescribed basis functions and demonstrate how one can optimize over these decision rules at low computational cost through constraint randomization. We obtain a-priori probabilistic guarantees on the feasibility properties of … Read more