The concepts of risk-aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. Statistical learning community has also …

The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …

Highly Automated Vehicles (HAVs) must undergo strict safety testing before being released
to the public. Mileage-based on-road testing suffers from unaffordable time costs and high …

Despite the addition of a significant amount of new material, our approach remains “to be
concise, precise, and integrated, leaving a lot of room for the instructor to expand on areas of …

Stochastic simulation is an invaluable tool for operations-research practitioners for the
performance evaluation of systems with random behavior and mathematically intractable …

Stochastic simulation requires input probability distributions to model systems with random
dynamic behavior. Given the input distributions, random behavior is simulated using Monte …

When input distributions to a simulation model are estimated from real-world data, they
naturally have estimation error causing input uncertainty in the simulation output. If an …

The bootstrap is a versatile inference method that has proven powerful in many statistical
problems. However, when applied to modern large-scale models, it could face substantial …

Traditionally, simulation has been used as a tool of design to estimate, compare and
optimize the performance of different system designs. It is rarely used in making real-time …

When a simulation model contains input distributions that need to be calibrated from
external data, proper simulation output analysis needs to account for not only the noises …