Now showing items 1-6 of 6
Mitigating Uncertainty via Compromise Decisions in Two-stage Stochastic Linear Programming
Stochastic Programming (SP) has long been considered as a well-justified yet computationally challenging paradigm for practical applications. Computational studies in the literature often involve approximating a large ...
Multi-Objective Probabilistically Constrained Programming with Variable Risk: New Models and Applications
We consider a class of multi-objective probabilistically constrained problems MOPCP with a joint chance constraint, a multi-row random technology matrix, and a risk parameter (i.e., the reliability level) defined as a ...
Quasi-Monte Carlo methods for linear two-stage stochastic programming problems
Quasi-Monte Carlo algorithms are studied for generating scenarios to solve two-stage linear stochastic programming problems. Their integrands are piecewise linear-quadratic, but do not belong to the function spaces ...
Distribution shaping and scenario bundling for stochastic programs with endogenous uncertainty
Stochastic programs are usually formulated with probability distributions that are exogenously given. Modeling and solving problems withendogenous uncertainty, where decisions can influence the probabilities, has remained ...
On Distributionally Robust Multiperiod Stochastic Optimization
This paper considers model uncertainty for multistage stochastic programs. The data and information structure of the baseline model is a tree, on which the decision problem is defined. We consider ambiguity neighborhoods ...