In standard stochastic dynamic programming, the transition probability distributions of the underlying Markov Chains are assumed to be known with certainty. We focus on the case where the transition probabilities or other input data are uncertain. Robust dynamic programming addresses this problem by defining a min-max game between nature and the controller. Considering examples from inventory a...

In this paper, the problems of robust stabilization and$H_{infty}$ control for uncertain stochastic systems withmultiple time delays represented by the Takagi-Sugeno (T-S) fuzzymodel have been studied. By constructing a new Lyapunov-Krasovskiifunctional (LKF) and using the bounding techniques, sufficientconditions for the delay-dependent robust stabilization and $H_{infty}$ control scheme are p...

With the high accident rate of civil aviation, medical resource inventory becomes more important for emergency management at the airport. Meanwhile, medical products usually are time-sensitive and short lifetime. Moreover, we find that the optimal medical resource inventory depends on multiple factors such as different risk preferences, the material shelf life and so on. Thus, it becomes very c...

In this paper we present the problem of combining optimal control with efficient information gathering in an uncertain environment. We assume that the decision maker has the ability to choose among a discrete set of sources of information, where the outcome of each source is stochastic. Different sources and outcomes determine a reduction of uncertainty, expressed in terms of constraints on sys...

In this paper, we consider a variety of models for dealing with demand uncertainty for a joint dynamic pricing and inventory control problem in a make-to-stock manufacturing system. We consider a multi-product capacitated, dynamic setting, where demand depends linearly on the price. Our goal is to address demand uncertainty using various robust and stochastic optimization approaches. For each o...

This paper proposes an integrated methodology and its solution for supplier selection problem. A twostage stochastic programming model and fuzzy TOPSIS methods are consolidated in this methodology. After pre-research phase, in the second part of the methodology, fuzzy TOPSIS is used for ranking potential suppliers considering qualitative data under fuzzy environment. In the third part of the me...

Stochastic Shortest Path problems (SSPs), a subclass of Markov Decision Problems (MDPs), can be efficiently dealt with using Real-Time Dynamic Programming (RTDP). Yet, MDP models are often uncertain (obtained through statistics or guessing). The usual approach is robust planning: searching for the best policy under the worst model. This paper shows how RTDP can be made robust in the common case...

We present a multistage stochastic programming model for mean-risk optimization of electricity portfolios containing physical components and energy derivative products. We consider a medium-term time horizon of up to one year. Stochasticity enters the model via the uncertain (time-dependent) prices, electricity demand, and heat demand. The objective is to maximize the expected overall revenue a...

• Electric spring model is developed for demand-side management. Flexibility of EVs used to increase wind energy. and electric are modeled as flexibility tools transfer The paper deals with day-ahead operation smart microgrids. Hybrid stochastic/robust optimization proposed handle uncertainty. (ES) a novel concept in power electronics has been the purpose dealing In this paper, conquer challeng...