Jennifer M. Bekki

Indirect cycle time quantile estimation using the Cornish-Fisher expansion

This paper proposes a technique for estimating steady-state quantiles from discrete-event simulation models, with particular attention paid to cycle time quantiles of manufacturing systems. The technique is based on the Cornish–Fisher expansion, justified through an extensive empirical study, and is supported with mathematical analysis. It is shown that the technique provides precise and accurate estimates for the most commonly estimated quantiles with minimal data storage and low computational requirements.

Monitoring Temporal Homogeneity in Attributed Network Streams

Network modeling and analysis has become a fundamental tool for studying various complex systems. This paper proposes an extension of statistical monitoring to network streams, which is crucial for effective decision-making in various applications. To this end, a model for the probability of edge existence as a function of vertex attributes is constructed and a likelihood method is developed to monitor the underlying network model. The method is flexible to detect any form of anomaly that arises from different network edge-formation mechanisms. Experiments on simulated and real network streams depict the properties and benefits of the method compared with existing methods in the literature.

Simulation in production planning: An overview with emphasis on recent developments in cycle time estimation

In today’s business environment, cycle time is a critical performance measure for manufacturing, and accurate estimation of cycle time–throughput (CT–TH) relationships plays an important role in successful production planning. To efficiently generate such comprehensive performance profiles, we propose a metamodeling approach, which integrates discrete-event simulation, adaptive statistical methods, and analytical queueing analysis. The resulting metamodels are mathematical equations quantifying the CT–TH relationships, and they have the “what-if” capability of tractable queueing models as well as the high fidelity of detailed simulation. In this chapter, methods for metamodeling CT–TH profiles are described for both single and multiproduct environments assuming that the manufacturing system is operated in steady state. As a future direction, the investigation of transient CT–TH behavior is also briefly discussed.

Simulation-based cycle-time quantile estimation in manufacturing settings employing non-FIFO dispatching policies

Previous work shows that a combination of the Cornish–Fisher Expansion (CFE) with discrete-event simulation produces accurate and precise estimates of cycle-time quantiles with very little data storage, provided all workstations in the model are operating under the first-in-first-out (FIFO) dispatching rule. The accuracy of the approach degrades, however, as non-FIFO dispatching policies are employed in at least one workstation. This paper proposes the use of a power transformation for use in combination with the CFE to combat these accuracy problems. The suggested approach is detailed, and three methods for selecting the λ parameter of the power transformation are given. The results of a thorough empirical evaluation of each of the three approaches are given, and the advantages and drawbacks of each approach are discussed. Results show that the combination of the CFE with a power transformation generates cycle-time quantile estimates with high accuracy even for non-FIFO systems.

Using quantiles in ranking and selection procedures

A useful performance measure on which to compare manufacturing systems is a quantile of the cycle time distribution. Unfortunately, aside from order statistic estimates, which can require significant data storage, the distribution of quantile estimates has not been shown to be normally distributed, violating a common assumption amongst ranking-and-selection (R&S) procedures. To address this, we provide empirical evidence supporting an approach using the mean of a group of quantile estimates as the comparison measure. The approach is detailed and illustrated through experimentation on four M/M/l queues in which the 0.9 cycle-time quantile is the performance measure. Results in terms of simulation effort and accuracy are reported and compared to results obtained using the macro-replications approach for inducing normality as well as to results obtained by applying R&S procedures to quantile estimates directly. The suggested procedure is shown to provide significant savings in simulation effort while sacrificing very little in accuracy.

Indirect cycle-time quantile estimation for non-FIFO dispatching policies

Previous work has shown that the Cornish-Fisher expansion (CFE) can be used successfully in conjunction with discrete event simulation models of manufacturing systems to estimate cycle-time quantiles. However, the accuracy of the approach degrades when non-FIFO dispatching rules are employed for at least one workstation. This paper suggests a modification to the CFE-only approach which utilizes a power data transformation in conjunction with the CFE. An overview of the suggested approach is given, and results of the implemented approach are presented for a model of a non-volatile memory factory. Cycle-time quantiles for this system are estimated using the CFE with and without the data transformation, and results show a significant accuracy improvement in cycle-time quantile estimation when the transformation is used. Additionally, the technique is shown to be easy to implement, to require very low data storage, and to allow easy estimation of the entire cycle-time cumulative distribution function

Measuring the Advising Alliance for Female Graduate Students in Science and Engineering An Emerging Structure

Faculty advisors play an important role in the development of graduate students. One group for which the advising relationship has been shown to be especially crucial is women in science, technology, engineering, and mathematics (STEM). We designed two studies to assess the advising alliance for women in STEM graduate programs using the student version of the Advisor Working Alliance Inventory (AWAI) along with additional content developed by our team. Study 1 (N = 76) was developed to assess item performance and the initial structure with a pilot sample of undergraduate and graduate students in science and engineering. Study 2 (N = 293) was designed to assess the advising alliance exclusively for women in STEM graduate programs. Our results indicated that an alternative alliance structure may exist for women in STEM and in Study 2 two factors emerged, which indicated that instrumental support and psychosocial support are two salient factors for women in STEM.

Steady-state quantile parameter estimation: an empirical comparison of stochastic kriging and quantile regression

The time required to execute simulation models of modern production systems remains high even with todays computing power, particularly when what-if analyses need to be performed to investigate the impact of controllable system input variables on an output performance measure. Compared to mean and variance which are frequently used in practice, quantiles provide a more complete picture of the performance of the underlying system. Nevertheless, quantiles are more difficult to estimate efficiently through stochastic simulation. Stochastic kriging (SK) and quantile regression (QR) are two promising metamodeling tools for addressing this challenge. Both approximate the functional relationship between the quantile parameter of a random output (e.g., cycle time) and multiple input variables (e.g., start rate, unloading times). In this paper, we compare performances of SK and QR on steady-state quantile parameter estimation. Results are presented from simulations of an M/M/1 queue and a more realistic model of a semiconductor manufacturing system.

A mastery-based learning approach for undergraduate engineering programs

We report the results of an action research study in which a modified mastery-based learning approach was implemented in three undergraduate engineering courses: engineering statistics, LabVIEW programming, and environmental engineering. In this paper, we describe both the action research and the modified mastery-based learning approach that was implemented. Findings from the analysis of data on student performance, and student and faculty perceptions of the approach are presented. In addition, we discuss our recommendations for modifications to the approach that could be used in future implementations.

Development of the science technology engineering and mathematics — Active listening skills assessment (STEM-ALSA)

The purpose of this investigation was to develop the STEM Active Listening Skills Assessment (STEM-ALSA), a conceptually grounded instrument designed to measure four components of active listening, a key element of communication in an academic setting. The STEM-ALSA is comprised of three unique scales that measure a persons knowledge (12 items), ability to apply (25 items), and self-efficacy (5 items) with respect to active listening. Two pilot studies were conducted with N = 99 upper level undergraduate students enrolled in STEM disciplines to develop and evaluate the instrument. Results of an exploratory factor analysis identified both a unidimensional factor structure for each of the three scales and total scores with adequate internal consistency reliability estimates. The STEM-ALSA provides a mechanism for measuring active listening skills among students in STEM.