A value-at-risk, or quantile, is widely used as an appropriate investment selection measure for risk-conscious decision makers. We present two quantile-based sequential procedures—with and without consideration of equivalency between alternatives—for selecting the best alternative from a set of simulated alternatives. These procedures asymptotically guarantee a user-defined target probability of correct selection within a prespecified indifference zone. Experimental results demonstrate the trade-off between the indifference-zone size and the number of simulation iterations needed to render a correct selection while satisfying a desired probability of correct selection.

We propose two sequential, indifference-zone procedures for the comparison of simulated systems. Comparisons and selection of the best system are based on the mean and variance of a performance metric estimated by simulation. The mean represents the central tendency while the variance is the surrogate for the system’s inherent systematic risk. The first procedure identifies the system(s) with the largest expected value and smallest variance. The second procedure uses the variance of a reference system as a risk threshold, and selects the system with the largest mean from among those with an acceptable level of risk not above the threshold. Numerical experiments demonstrate the validity and efficacy of the proposed procedures. The online appendix is available at https://doi.org/10.1287/ijoc.2018.0808 .

In this work, two access point (AP) selection mechanisms called STOOP and MD-STOOP are presented for enterprise wireless local area networks (WLANs). STOOP utilizes first- order stochastic dominance (FSD) tools to develop a runtime algorithm that probes the network to compare the quality of service from multiple APs in a stochastic manner. MD-STOOP utilizes the concept of mean dominance (MD) to select APs, which provides good average case performance with less overhead than STOOP. Performance of the two algorithms is evaluated through extensive simulations in comparison to the state-of-the-art. It is shown that STOOP and MD-STOOP outperform existing solutions and directions for further improvements are discussed.

Current TV whitespace regulations mainly benefit rural areas where large amounts of TV whitespace exist. Thus, the spectrum scarcity problem is yet to be addressed in urban locations, where it is most experienced. To further improve the spectrum efficiency, a new framework for cognitive radio network operation is presented, which can coexist with current broadcast TV networks. Through geographical evaluations based on distribution of TV towers and population dynamics, it is shown that by leveraging the TV viewership statistics, 5.6-7.7-fold increase in available channels can be provided to mobile users in populated areas such as New York City. Furthermore, daily dynamics of TV viewership can be exploited to provide up to 96 MHz additional bandwidth during prime time and 162-228 MHz additional bandwidth during non-peak hours. The additional TV spectrum can provide additional channel capacities in both rural and urban areas. To the best of our knowledge, this is the first work that analyzes TV whitespace availability based on TV viewership statistics in space and time.