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Techniques are presented for modeling, fitting, and generating many of the univariate probabilistic input processes that drive discrete-event simulation experiments. Emphasis is given to the generalized beta distribution family, the Johnson translation system of distributions, and the Be??zier distribution family because of the flexibility of these families to model a wide range of distributional...
As sustainability related issues, such as energy consumption and environmental impact issues are becoming a more integrated part of operational and long-term planning decisions, simulation modeling and analysis tools are needed to aid in the decision-making process. In this paper we introduce the concept of a simulation-based sustainability toolkit and present a prototype of one portion of the toolkit...
A dynamic simulation-based crashing method is introduced in this research to evaluate project networks and determine the optimum crashing configuration that minimizes the average project cost due to lateness penalties and crashing costs. This dynamic approach will let the user evaluate the project network to determine a crashing strategy at the beginning of the project and also during the life of...
Techniques are presented for modeling and generating the univariate probabilistic input processes that drive many simulation experiments. Emphasis is on the generalized beta distribution family, the Johnson translation system of distributions, and the Bezier distribution family. Also discussed are nonparametric techniques for modeling and simulating time-dependent arrival streams using nonhomogeneous...
We develop and evaluate a semiparametric method to estimate the mean-value function of a nonhomogeneous Poisson process (NHPP) using one or more process realizations observed over a fixed time interval. To approximate the mean-value function, the method exploits a specially formulated polynomial that is constrained in least-squares estimation to be nondecreasing so the corresponding rate function...
Techniques are presented for modeling and generating the univariate probabilistic input processes that drive many simulation experiments. Emphasis is on the generalized beta distribution family, the Johnson translation system of distributions, and the Bezier distribution family. Also discussed are nonparametric techniques for modeling and simulating time-dependent arrival streams using nonhomogeneous...
Cyber security methods are continually being developed. To test these methods many organizations utilize both virtual and physical networks which can be costly and time consuming. As an alternative, in this paper, we present a simulation modeling approach to represent computer networks and intrusion detection systems (IDS) to efficiently simulate cyber attack scenarios. The outcome of the simulation...
A critical aspect of semiconductor manufacturing is the design and analysis of material handling and production control policies to optimize fab performance. This research utilizes two simulations of SEMATECH fab data of actual production fabs. The hypothesis of this study is that both vehicle and machine dispatching rules and their interaction have significant impact on fab performance. To test this...
A semiconductor company must bring technology to the market as soon as its application is deemed feasible to be a leader in the industry. The goal of this paper is to investigate production control methods in semiconductor R&D fabs to minimize the time to market for the aforementioned technology. Simulation models of a representative R&D fab are run with different levels of bottleneck utilization,...
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