Decision-making under uncertainty has a long and distinguished history in operations research. However, most of the existing solution techniques suffer from the curse of dimensionality, which restricts their application to small and medium-sized problems, or they rely on simplifying modelling assumptions (e.g. absence of recourse actions). Recently, a new solution technique has been proposed, w...

In the context of recent oil prices fall and resulting budgetary problems faced by KSA, it is very interesting to examine the country’s overdependence on oil revenues. In this paper, we examine whether extreme oil dependence for Saudi Arabia is the symptom of Dutch Disease. We first provide a brief review of the literature on Dutch Disease and the natural resource curse. We then discuss the sym...

We introduce the problem of selecting patient-donor pairs in a kidney exchange program to undergo crossmatch test, and we model this selection as two-stage stochastic integer programming problem. The optimal solutions new formulation yield larger expected number realized transplants than previous approaches based on internal recourse or subset recourse. settle computational complexity by showin...

We propose a computationally-tractable optimization-based framework for risk management in midterm process planning under uncertainty. We employ stochastic programming to account for the uncertainty in which a scenario-based approach is used to represent the underlying probability distribution of the uncertain parameters. The problem is formulated as a two-stage stochastic program with recourse...

Firms operate today in a rapidly changing and risky environment, where such factors as market and technology are inevitably shrouded in uncertainties. They must make design and operating decisions to satisfy several conflicting goals such as maximizing expected profit, minimizing risk, and sustaining long-term viability and competitiveness. Proper formulation is both essential and critical for ...

Algorithmic recourse recommendations inform stakeholders of how to act revert unfavorable decisions. However, existing methods may recommend actions that lead acceptance (i.e., the model's decision) but do not improvement underlying real-world state). To such is fooling predictor. We introduce a novel method, Improvement-Focused Causal Recourse (ICR), which involves conceptual shift: Firstly, w...