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We propose a stochastic programming model as a solution for optimizing the problem of locating and allocating medical supplies used in disaster management. To prepare for natural disasters, we developed a stochastic optimization approach to select the storage location of medical supplies and determine their inventory levels and to allocate each type of medical supply. Our model also captures disaster elaborations and possible effects of disasters by using a new classification for major earthquake scenarios. We present a case study for our model for the preparedness phase. As a case study, we applied our model to earthquake planning in Adana, Turkey. The experimental evaluations showed that the model is robust and effective. (Disaster Med Public Health Preparedness. 2017;11:747–755)
In this paper we study asymptotic consistency of law invariant convex risk measures and the corresponding risk averse stochastic programming problems for independent, identically distributed data. Under mild regularity conditions, we prove a law of large numbers and epiconvergence of the corresponding statistical estimators. This can be applied in a straightforward way to establish convergence with probability 1 of sample-based estimators of risk averse stochastic programming problems.
Motivated by the observation
that the gain-loss criterion, while offering economically meaningful prices of contingent claims,
is sensitive to the reference measure governing the underlying stock price process (a situation
referred to as ambiguity of measure), we propose a gain-loss pricing model robust to shifts in the reference measure.
Using a dual representation property of polyhedral risk measures
we obtain a one-step, gain-loss criterion based theorem of
asset pricing under ambiguity of measure, and illustrate its use.
Vector autoregressive processes of the first order are considered which are non-negative and optimize a linear objective function. These processes may be used in stochastic linear programming with a dynamic structure. By using Tweedie's results from the theory of Markov chains, conditions for geometric rates of convergence to stationarity (i.e. so-called geometric ergodicity) and for existence and geometric convergence of moments of these processes are obtained.
This paper analyzes the effects of uncertain soil loss in farm planning models. A disaggregated approach was used because of an interest in examining the impact of probabilistic soil loss constraints on farm level decisionmaking. A stochastic programming model was used to consider different levels of probability of soil loss. Traditional methods of analysis are shown to consistently overestimate net returns.
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