Martha A. Centeno

Reducing time in an emergency room via a fast-track

Queues at emergency rooms (ER) are prioritized based on the patients sickness level. As a consequence, patients with low priority frequently have to wait excessively long. At Mercy Hospital, we began to study means by which such time would be reduced, without putting in jeopardy the life of critical patients. As an initial effort, we studied the flow of patients at the Emergency Department (ED) and decided to model it with and without a fast track lane. A simulation study revealed that indeed a fast track lane reduces by almost 25% the time in the system for patients with low priority without negatively affecting the times of patients with higher priority.

So you have your model: what to do next. A tutorial on simulation output analysis

Simulation models are built with the intent of studying the behavior of the real system represented by the model. However, a simulation model generates random outputs; thus, the data generated by it can only be used to estimate the true measure of performance. In this tutorial, we introduce several concepts and techniques to analyze such output. Additional examples will be given during the presentation of the tutorial.

An introduction to simulation modeling

This paper offers an introductory overview of discrete simulation, with emphasis on the simulation modeling process rather than on any specific simulation package. Examples are used to demonstrate the application of the methodology at each step of the process. An extensive list of additional readings is given for the reader who wishes to deepen his/her knowledge in a specific area.

Intelligent scheduling and control of rail-guided vehicles and load/unload operations in a flexible manufacturing system

This paper presents a framework of intelligent manufacturing scheduling and control with specific applications to operations of rail-guided vehicle systems (RGVS). A RGVS control architecture is discussed with a focus on a simulated experiment in operations of the load/unload area of a real industrial flexible manufacturing system (FMS). In the operation stage of a material handling system (MHS), all shop floor data are subject to change as time goes. These data can be collected using a data acquisition device and stored in a dynamic database. The RGVS simulator used in this experimental study is designed to incorporate some possible situations representing existing material handling scenarios in order to evaluate alternative control policies. At the development stage of the controller, all possible combinations of most commonly encountered scenarios such as RGV failures, production schedule changes, machine breakdowns, and rush orders are to be simulated and corresponding results collected. The data are then structured into training data pairs to properly train an artificial neural network. The neural network, trained by using input/output data sets obtained from a number of simulation runs, will then provide control strategy recommendations. At the application stage, whenever an abnormal scenario occurs, a pre-processor will be activated to pre-screen and prepare an input vector for the trained neural network. If such an abnormal scenario falls outside the existing domain of data sets employed to train the neural network, as judged by the MHS supervisory controller, an off-line training module will be activated to eventually update the neural network. The recommended control strategies will be transmitted to the MHS control for real-time execution. If there is no further abnormal event detected, the dynamic data base (DDB) module simply continues to monitor the MHS activities. The proposed MHS control system combines the features of example based neural network technology and simulation modeling for true intelligent, on-line, pseudo real-time control. Not only will the system assure that feasible material handling control actions be taken, but also it will implement better control decisions through continuous learning from experiences captured as the operation time of the MHS accumulates.

Enhancing simulation models for emergency rooms using VBA

Increased pressure to control costs and increased competition has prompted health care managers to look for tools to effectively operate their institutions. Managers are increasingly using simulation modeling to understand their processes. A simulation model of an ER has been enhanced with VBA routines, so that it can use real world data. The simulation model has been complemented with a series of decision-making routines. These routines use a hierarchical approach to organize the various scenarios under which the model may run and to partially reconfigure the ARENA model at run time.

Modeling manufacturing systems: an information-based approach

The authors describe an integrated simulation modeling environment (ISME) which utilizes an information-based approach for the description of manufacturing systems. Manufacturing systems are described as a higher level aggregation of the objects in the system. Such a description is stored in a relational database. Models are later synthesized from the description of the system. Experimental conditions are input to guide the analysis. >

Introducing simulation across the disciplines

How to introduce simulation is a fundamental educational issue in a variety of disciplines including industrial engineering and operations management as well as product design and manufacturing. This panel will discuss, compare, and contrast various perspectives and experiences concerning introducing simulation to undergraduate and graduate students. Topics considered by the panel include the fundamental purposes of a first simulation course, modeling and analysis assignments that are given, examination topics, laboratory content, and term project experiences.

A simulation study to increase throughput in an endoscopy center

We present a simulation study to increase the throughput at an Endoscopy center. The center has the capacity to process up to 80 patients a day. However, they were only processing about 50 patients/day, on average. We have built a simulation model to better understand the causes of this low throughput and to determine the worthiness of some proposed changes the centers current operational policies. We examined a fraction of a factorial design of seven factors at two or three levels each. Results helped determine that two perceived causes for the formation of bottlenecks actually did not have much impact, whereas the policy to assign rooms, which was never suspected, has a lot to do with it. We were also able to understand that a major part of the problem is rooted in customers arriving too early for their procedures. We also have identified new venues for future research.

S.U.C.C.E.S.S.F.U.L.: a framework for designing discrete event simulation courses

Teaching a discrete event simulation (DES) course to undergraduates represents a challenge for instructors, particularly for the novice ones. There are many issues to address in designing the course, among which are the scope of the course, focus area of the course, technical depth of the course, and the role of a simulation packages in the course. How to introduce simulation has been a fundamental educational issue for a while. New teaching paradigms and the explosion tools available offers opportunities to design a course that is holistic. This paper presents a framework that enables a holistic design of a DES course such that students acquire a strong knowledge base regarding the use of DES in the real world. This paper also includes an example of how to use this framework.

Simulation-based decision support for emergency rooms systems

Increased pressure to control costs and increased competition has prompted health care managers to look for tools to effectively operate their institutions. This article discusses a framework for a Simulation-Based Decision Support System (SBDSS) to evaluate operating policies in emergency rooms (ER) systems. A prototype of this SBDSS is presented. ER decisions have been categorised and, for each one, an implementation plan is discussed. The prototype revealed that simulation can truly be used in this environment in a timely fashion because the simulation model has been complemented with a series of decision-making routines. These routines use a hierarchical approach to organise the various scenarios under which the model may run and to partially reconfigure the simulation model at run time.