Real Options: A Survey

This survey paper provides an overview of real options, in particular the connection with financial options, valuation methods (analytical methods vs numerical methods based on simulation, lattice approximations to stochastic processes and finite-difference methods) and a wide array of application areas, from R&D to operations management to renewable energy project selection. Citation Technical report, Lehigh … Read more

Robust Binary Optimization using a Safe Tractable Approximation

We present a robust optimization approach to 0-1 linear programming with uncertain objective coefficients based on a safe tractable approximation of chance constraints, when only the first two moments and the support of the random parameters is known. We obtain nonlinear problems with only one additional (continuous) variable, for which we discuss solution techniques. The … Read more

Robust risk adjustment in health insurance

Risk adjustment is used to calibrate payments to health plans based on the relative health status of insured populations and helps keep the health insurance market competitive. Current risk adjustment models use parameter estimates obtained via regression and are thus subject to estimation error. This paper discusses the impact of parameter uncertainty on risk scoring, … Read more

Robust Investment Management with Uncertainty in Fund Managers’ Asset Allocation

We consider a problem where an investment manager must allocate an available budget among a set of fund managers, whose asset allocations are not precisely known to the investment manager. In this paper, we propose a robust framework that takes into account the uncertainty stemming from the fund managers’ allocation, as well as the more … Read more

Optimizing healthcare network design under reference pricing and parameter uncertainty

Healthcare payers are exploring cost-containing policies to steer patients, through qualified information and financial incentives, towards providers offering the best value proposition. With Reference Pricing (RP), a payer or insurer determines a maximum amount paid for a procedure, and patients who select a provider charging more pay the difference. In a Tiered Network (TN), providers … Read more

Recent Advances in Robust Optimization and Robustness: An Overview

This paper provides an overview of developments in robust optimization and robustness published in the academic literature over the past five years. Citation Technical report, LAMSADE, Universite Paris-Dauphine, Paris, France. (2012) Article Download View Recent Advances in Robust Optimization and Robustness: An Overview

Multi-Range Robust Optimization vs Stochastic Programming in Prioritizing Project Selection

This paper describes a multi-range robust optimization approach applied to the problem of capacity investment under uncertainty. In multi-range robust optimization, an uncertain parameter is allowed to take values from more than one uncertainty range. We consider a number of possible projects with anticipated costs and cash flows, and an investment decision to be made … Read more

Robust Timing of Markdowns

We propose an approach to the timing of markdowns over a finite time horizon that does not require the precise knowledge of the underlying probabilities, instead relying on range forecasts for the arrival rates of the demand processes, and that captures the degree of the manager’s risk aversion through intuitive budget-of-uncertainty functions. These budget functions … Read more

Robust Optimization with Multiple Ranges: Theory and Application to R&D Project Selection

We present a robust optimization approach when the uncertainty in objective coefficients is described using multiple ranges for each coefficient. This setting arises when the value of the uncertain coefficients, such as cash flows, depends on an underlying random variable, such as the effectiveness of a new drug. Traditional robust optimization with a single range … Read more

Robust Linear Optimization With Recourse

We propose an approach to linear optimization with recourse that does not involve a probabilistic description of the uncertainty, and allows the decision-maker to adjust the degree of robustness of the model while preserving its linear properties. We model random variables as uncertain parameters belonging to a polyhedral uncertainty set and minimize the sum of … Read more