Many industries use revenue management to balance uncertain, stochastic demand and
inflexible capacity. Popular examples include airlines, hotels, car rentals, retailing, and …

The paper provides an overview of the theory and applications of risk-sensitive Markov
decision processes. The term'risk-sensitive'refers here to the use of the Optimized Certainty …

This article develops a safetyanalysis method for stochastic systems that is sensitive to the
possibility and severity of rare harmful outcomes. We define risk-sensitive safe sets as …

Effective bidding on multiple electricity products under uncertainty would allow a more
profitable market participation for hybrid power plants with variable energy resources and …

We consider a risk-averse multi-stage stochastic program using conditional value at risk as
the risk measure. The underlying random process is assumed to be stage-wise …

We present a new methodology to support an energy trading company (ETC) to devise
contracting strategies under an optimal risk-averse renewable portfolio. The uncertainty in …

In this paper, we consider risk-sensitive Markov Decision Processes (MDPs) with Borel state
and action spaces and unbounded cost. We treat both finite and infinite planning horizons …

The paper presents and compares approaches for controlling forest companies' risk
associated with advance planning under variable future timber prices and demand …

Multistage stochastic programming is a powerful tool allowing decision-makers to revise
their decisions at each stage based on the realized uncertainty. However, organizations are …

A preparedness resource allocation model and an algorithmic approach are presented for a
three-stage stochastic problem for managing natural disaster mitigation. That preparedness …