Fully PCA-based approach to optimization of multiresponse-multistage problems with stochastic considerations

Time-consistent approximations of risk-averse multistage stochastic optimization problems

OF THE DISSERTATION TIME-CONSISTENT APPROXIMATIONS OF RISK-AVERSE MULTISTAGE STOCHASTIC OPTIMIZATION PROBLEMS by Tsvetan Asamov Dissertation Director: Andrzej Ruszczyński In this work we study the concept of time consistency as it relates to multistage risk-averse stochastic optimization problems on finite scenario trees. We use dynamic time-consistent formulations to approximate problems havin...

Stochastic Optimization Problems in the Service Industry with Customer Considerations

This dissertation studies the effect of customer considerations in stochastic optimization problems in the service industry. The first part looks at problems in the service industry geared towards providing efficient service, i.e., minimizing the total cost of providing service while ensuring that certain service level targets are met. In particular, we study applications in the call center ind...

Stochastic Optimization of PCA with Capped MSG

We study PCA as a stochastic optimization problem and propose a novel stochastic approximation algorithm which we refer to as “Matrix Stochastic Gradient” (MSG), as well as a practical variant, Capped MSG. We study the method both theoretically and empirically.

Stochastic Programming Approach to Multiobjective Optimization Problems with Random Element

Ordinal Optimization Approach to Stochastic Simulation Optimization Problems and Applications

In this paper, we propose an ordinal optimization approach to solve for a good enough solution of the stochastic simulation optimization problem with huge decision-variable space. We apply the proposed ordinal optimization algorithm to G/G/1/K polling systems to solve for a good enough number-limited service discipline to minimize the weighting average waiting time. We have compared our results...