policies on ecological and economic criteria. Optimization augmented the usefulness of the approach, suggesting that income …

global warming policies. Because there are typically profound uncertainties in these models, a simulation approach is often …>This paper was accepted by Dimitris Bertsimas, optimization.</i> …

portfolios of credit-sensitive assets such as loans and corporate bonds. Monte Carlo simulation is an important tool for … performing computations in these models. This paper develops, analyzes, and evaluates two simulation algorithms for intensity … Zeevi, stochastic models and simulation.</i> …

Sequential sampling problems arise in stochastic simulation and many other applications. Sampling is used to infer the …, as is common in the literature. <i>This paper was accepted by Assaf Zeevi, stochastic models and simulation.</i> …

simulation experiment. We also show how different definitions of the sample space yield different variants of the LR method, some …

the problem of using simulation to estimate the number of transitions it takes the process to enter \scr{A}. Using …

In a discrete-event simulation, the information related to the events scheduled to occur in the future is kept in a …

approach to simulate comparative solutions for specific contracts under different parametric settings. Through a simulation …

stochastic constrained optimization model is developed for a multi-species fishery that sets the seasonal catch by species, by … sources of uncertainty as catch per species, catch per unit of effort, weather, and markets by a computer simulation procedure …