We present empirical results from a new approach, Aesthetic Computing to customizing model structures for designing models for systems found in mathematics and computer simulation. At the University of Florida, we have taught the methodology of Aesthetic Computing as a separate class, and within the context of a Simulation class. Students in the simulation class were taught the method and then subsequently allowed to construct their own interactive 3D representations of typical simulation model structures such as Petri nets and finite state automata. While using the method, natural issues arise, questioning where aesthetic computing can provide benefit in model representation. To help answer such questions, a student needs to be presented with a body of knowledge that represents the “aesthetic computing technique, ” and then the student applies this knowledge to create the modeling artifacts. We present recent empirical studies from two classes, Aesthetic Computing and Computer Simulation, where aesthetic “methods/techniques” were employed. From the studies, we determined that there were several key results associated with Aesthetic Computing: 1) the ability for the student to be more creative in building their own custom models, and 2) the ability to improve perceived communication of technical topics (e.g., associated with the models) to non-experts.

A simulation model is successful if it leads to policy action, i.e., if it is implemented. Studies show that for a model to be implemented, it must have good correspondence with the mental model of the system held by the user of the model. The user must feel confident that the simulation model corresponds to this mental model. An understanding of how the model works is required. Simulation models for implementation must be developed step by step, starting with a simple model, the simulation prototype. After this has been explained to the user, a more detailed model can be developed on the basis of feedback from the user. Software for simulation prototyping is discussed, e.g., with regard to the ease with which models and output can be explained and the speed with which small models can be written.

The model used in this report focuses on the analysis of ship waiting statistics and stock fluctuations under different arrival processes. However, the basic outline is the same: central to both models are a jetty and accompanying tankfarm facilities belonging to a new chemical plant in the Port of Rotterdam. Both the supply of raw materials and the export of finished products occur through ships loading and unloading at the jetty. Since disruptions in the plants production process are very expensive, buffer stock is needed to allow for variations in ship arrivals and overseas exports through large ships. Ports provide jetty facilities for ships to load and unload their cargo. Since ship delays are costly, terminal operators attempt to minimize their number and duration. Here, simulation has proved to be a very suitable tool. However, in port simulation models, the impact of the arrival process of ships on the model outcomes tends to be underestimated. This article considers three arrival processes: stock-controlled, equidistant per ship type, and Poisson. We assess how their deployment in a port simulation model, based on data from a real case study, affects the efficiency of the loading and unloading process. Poisson, which is the chosen arrival process in many client-oriented simulations, actually performs worst in terms of both ship delays and required storage capacity. Stock-controlled arrivals perform best with regard to ship delays and required storage capacity. In the case study two types of arrival processes were considered. The first type are the so-called stock-controlled arrivals, i.e., ship arrivals are scheduled in such a way, that a base stock level is maintained in the tanks. Given a base stock level of a raw material or ...

This paper reports on the progress made toward the emergence of standards to support the integration of heterogeneous discrete-event simulations (DESs) created in specialist support tools called commercial-off-the-shelf (COTS) discrete-event simulation packages (CSPs). The general standard for heterogeneous integration in this area has been developed from research in distributed simulation and is the IEEE 1516 standard The High Level Architecture (HLA). However, the specific needs of heterogeneous CSP integration require that the HLA is augmented by additional complementary standards. These are the suite of CSP interoperability (CSPI) standards being developed under the Simulation Interoperability Standards Organization (SISO---http://www.sisostds.org) by the CSPI Product Development Group (CSPI-PDG). The suite consists of several interoperability reference models (IRMs) that outline different integration needs of CSPI, interoperability frameworks (IFs) that define the HLA-based solution to each IRM, appropriate data exchange representations to specify the data exchanged in an IF, and benchmarks termed CSP emulators (CSPEs). This paper contributes to the development of the Type I IF that is intended to represent the HLA-based solution to the problem outlined by the Type I IRM (asynchronous entity passing) by developing the entity transfer specification (ETS) data exchange representation. The use of the ETS in an illustrative case study implemented using a prototype CSPE is shown. This case study also allows us to highlight the importance of event granularity and lookahead in the performance and development of the Type I IF, and to discuss possible methods to automate the capture of appropriate values of lookahead.

This paper discusses the application of simulation to analyze the value proposition and construction of an incentive program in an Operating Room (OR) environment. The model was further used to evaluate operational changes including scheduling processes within the OR and utilization rates in areas such as Post Anesthesia Care Unit (PACU) and the Ambulatory Surgery Department (ASD). Lessons learned are presented on developing multiple simulation models from one application as well as issues regarding model transition to a client.

Hospitals today are investing time and money to expand and improve their Emergency Departments (ED). Using simulation to test their many improvement ideas can necessitate running numerous scenarios. Model changes such as the number of ED beds, inpatient beds and process improvements will yield an exponentially growing list of permutations in alternative ED designs. This paper uses recent project experience to describes where to begin and which steps to take to go from an As-Is ED configuration to the best To-Be configuration.

This project is the continuation of a previous simulation study based on a trial and error approach that pretended to find a better system. This new phase pursued a scientific approach for the simulation study in order to identify the best alternative: sensitivity analysis, design of experiments, regression analysis for metamodeling purposes, and optimization. Typical simulation optimization methods were not of practical value for this application. An optimization tool based on mathematical programming was developed using Microsoft’s Excel Solver. The tool was validated in terms of the metamodels accuracy and the capacity to find a local optimum within the search region. It was concluded that additional experimental designs were needed in order to find the global optimum. Nevertheless, the tool was valid for the practical application of this project. Finally, it was also concluded that the scientific approach rendered better results than the trial and error approach. ii RESUMEN Este proyecto es la continuación de un estudio de simulación que pretendía resolver un problema de aplicación práctica siguiendo una metodología de prueba y error.

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