In this paper we develop approximation algorithms for two-stage convex chance constrained problems. Nemirovski and Shapiro [18] formulated this class of problems and proposed an ellipsoid-like iterative algorithm for the special case where the impact function f(x,h) is bi-affine. We show that this algorithm extends to bi-convex f(x,h) in a fairly straightforward fashion. The complexity of the s...

Abstract Project selection for a portfolio is pivotal decision in the pharmaceutical industry. In this paper, we study optimization problem companies considering uncertainty of cost each phase drug development and specific value annual budget. The presented model suitable to make investment decisions multi-phase projects stochastic approach applied handle model. Post-optimality analysis budget ...

Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given that probability distribution uncertain problem parameters affecting condition(s) is only known belong some ambiguity set. We study three popular approximation schemes for distributionally over Wasserstein balls, whe...

Two-echelon distribution systems are often considered in city logistics to maintain economies of scale and satisfy the emission zone requirements cities. In this work, we formulate two-echelon vehicle routing problem with stochastic demands as a chance-constrained optimization problem, where total demand customers each second-echelon route should fit within capacity high probability. We propose...

High penetration of renewable generation poses challenges to power system operation due its uncertain nature. In droop-controlled microgrids, the voltage volatility induced by uncertainties is aggravated high droop gains. This paper proposes a chance-constrained optimal flow (CC-OPF) problem with routers (PFRs) better regulate profile in microgrids. PFR refers general type network-side controll...