Finite-sample performance of absolute precision stopping rules

The article of record as published may be found at http://dx.doi.org/10.1287/ijoc.1110.0471Absolute precision stopping rules are often used to determine the length of sequential experiments to estimate confidence intervals for simulated performance measures. Much is known about the asymptotic behavior of such procedures. In this paper, we introduce coverage contours to quantify the trade-offs in interval coverage, stopping times, and precision for finite-sample experiments using absolute precision rules. We use these contours to evaluate the coverage of a basic absolute precision stopping rule, and we show that this rule will lead to a bias in coverage even if all of the assumptions supporting the procedure are true. We define optimal stopping rules that deliver nominal coverage with the smallest expected number of observations. Contrary to previous asymptotic results that suggest decreasing the precision of the rule to approach nominal coverage in the limit, we find that it is optimal to increase the confidence coefficient used in the stopping rule, thus obtaining nominal coverage in a finite-sample experiment. If the simulation data are independent and identically normally distributed, we can calculate coverage contours analytically and find a stopping rule that is insensitive to the variance of the data while delivering at least nominal coverage for any precision value

Simulating multivariate time series using flocking

Refereed Conference PaperNotions from agent based modeling (ABM) can be used to simulate multivariate time series. An example is given using the ABM concept of flocking, which models the behaviors of birds (called boids) in a flock. A multivariate time series is mapped into the coordinates of a bounded orthotope. This represents the flight path of a boid. Other boids are generated that flock around this data boid. The coordinates of these new boids are mapped back to simulate replicates of the original time series. The flock size determines the number of replicates. The similarity of the replicates to the original time series can be controlled by flocking parameters to reflect the strength of the belief that the future will mimic the past. It is potentially possible to replicate general non-stationary, dependent, high-dimensional time series in this manner

Data-driven simulation of complex multidimensional time series

The article of record as published may be found at: http//:dx.doi.org/10.1145.2553082This article introduces a new framework for resampling general time series data. The approach, inspired by computer agent flocking algorithms, can be used to generate inputs to complex simulation models or for generating pseudo-replications of expensive simulation outputs. The method has the flexibility to enable replicated sensitivity analysis for trace-driven simulation, which is critical for risk assessment. The article includes two simple implementations to illustrate the approach. These implementations are applied to nonstationary and state-dependent multivariate time series. Examples using emergency department data are presented

Simulation Optimization Using Simultaneous Replications and Event Time Dilation

A new approach to simulation response optimization is presented that takes advantage of the ability to run simultaneous replications of different experimental factor settings in a single run. It is also possible to use different time scales for the events corresponding to different design points. In this manner, the run can focus on factor settings that are likely to be optimal and feasible. An example is presented using a penalty function to dilate event times to find the cycle-time constrained capacity of a queue.

APPLICATIONS Fast Simulations of Large-Scale Highly Congested Systems

On behalf of

Golf Course Revenue Management: A Study Of Tee Time Intervals

Golf courses have two strategic levers, round duration control and demand-based pricing that they can deploy in a revenue management programme. Before embarking on a revenue management programme, golf courses must first clearly define their capacity. This study uses simulation to study the most controllable factor of capacity: the tee time interval. Intuitively, reducing the interval between parties will lead to an increase in revenue; however, this paper shows that interval reductions may actually lead to decreased revenue. Research in revenue management (RM) has previously addressed the theoretical and practical problems facing airlines and hotels, among other industries, but has given little attention to the golf course industry (Kimes, 1989; Weatherford and Bodily, 1992). The golf course business is similar enough to hotel and airline operations that golf courses should be able to apply RM principles (Kimes, 2000). Industries using RM generally measure their performance by calculating their revenue (or contribution) per available time-based inventory unit. For example, hotels calculate their revenue per available room-night (RevPAR), airlines determine their revenue per available seat-mile (RevPAS), and restaurants rely on revenue per available seat-hour (RevPASH). Based on this logic, golf courses should measure their revenue per available tee-time (RevPATT), but the definition of availability is not as precise as in other industries. The number of available tee times is affected by both controllable and uncontrollable factors. Controllable factors include the length of a round of golf, the dispatching rule used, maintenance and the tee time interval. Uncontrollable factors include the number of hours of daylight and weather. Unless golf course operators have a clear definition of their capacity, they will not be able to measure the performance of their RM systems. This paper focuses on the most easily controllable factor affecting course capacity: the tee time interval. A dynamic simulation model was developed, which can be used to quantify the trade-offs in determining an appropriate tee time interval. Intuitively, reducing the time interval between parties might lead to an increase in throughput and revenue; however, tee time interval reductions may amplify the effects of the variations in pace of play and result in a reduced RevPATT

Planning and scheduling in Japanese semiconductor manufacturing John W. Fowler, Izak Duenyas, and Lee Schruben.

http://deepblue.lib.umich.edu/bitstream/2027.42/4945/5/bbm0209.0001.001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/4945/4/bbm0209.0001.001.tx