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Accurate estimation of activity durations is a critical project scheduling step. If abundant data are available for activity durations, then distribution fitting is straightforward. However, if sample sizes are small, then choosing an appropriate theoretical distribution for activity duration is challenging. In this study, we propose a methodology for experimenting with the impact of sample size on the accuracy of the estimated duration of a stochastic project network. We synthesized a total of 3,264 stochastic project networks and estimated the true project duration distribution for each network using large-sample Monte Carlo simulation. We varied the sample size per activity and investigated how the choice of theoretical distribution (normal, beta, triangular, and lognormal) for estimated activity durations impacted the accuracy of the estimated project duration distribution. We find that the best choice for a theoretical activity duration distribution highly depends on the sample size. For sample sizes below 50, we find that not much is gained from the common practice of putting extensive effort into selecting best-fit theoretical distributions compared to simply using normal distributions. [ABSTRACT FROM AUTHOR]

Preface to the Special Issue on Behavioral Queueing Science: The Need for a Multidisciplinary Approach Modern service systems are economically important but operationally complex. In "Preface to the Special Issue on Behavioral Queueing Science: The Need for a Multidisciplinary Approach," Ingolfsson, Mandelbaum, Schultz, and Yom-Tov discuss how this special issue advances the scientific study of queues in services systems by acknowledging the central role of human behavior. Behavioral queueing science requires a multidisciplinary approach, using tools that include mathematical modelling, lab experiments and field studies. Each discipline has strengths and weaknesses, but together they have complementing goals, and jointly they give rise to a scientific paradigm for behavioral queues. Cross-disciplinary work is challenging but necessary. The eleven papers in our special issue show how this can be done successfully while setting an example for future work. History: This paper has been accepted for the Operations Research Special Issue on Behavioral Queueing Science. [ABSTRACT FROM AUTHOR]

We present an efficient, reliable, and easy-to-implement algorithm to compute steady-state probabilities for birth-death processes whose upper-tail probabilities decay geometrically or faster. The algorithm can provide any required accuracy and [...]

This article categorizes existing maximum coverage optimization models for locating ambulances based on whether the models incorporate uncertainty about (1) ambulance availability and (2) response times. Data from Edmonton, Alberta, [...]

We compare the performance of seven methods in computing or approximating service levels for nonstationary M(t)/M/s(t) queueing systems: an exact method (a Runge-Kutta ordinary-differential-equation solver), the randomization method, a closure […]

Development of methods for workforce scheduling, based on modelling of structure of sets of permissible shifts alongwith stochastic and time-varying demand processes, is discussed.

The Run by Run Controller provides a framework for controlling a process which is subject to disturbances such as shifts and drifts as a normal part of its operation. The Run by Run Controller combines the advantages of both Statistical Process Control (SPC) and Feedback Control. It has three components: Rapid Mode, Gradual Mode, and Generalized SPC. Rapid mode adapts to sudden shifts in the process such as those caused by maintenance operations. Gradual mode adapts to gradual drifts in the process such as those caused by build-up of deposition inside a reactor. The choice between the two modes is determined by the outcome from Generalized SPC which allows SPC to be applied to a process while it is being tuned. The Run by Run Controller has been applied to the control of a silicon epitaxy process in a barrel reactor. Rapid mode recovered the process within 3 runs after a disturbance. Gradual mode reduced the variation of the process by a factor of 2.7 as compared to historical data.