8310590
Modeling leadership effects and recruit type in an Army recruiting station
Army recruiters have an uphill battle recruiting well-qualified volunteers into military service. With prospering economy, there are many alternatives to joining the military, and all services are having difficulty recruiting young people. United States Army Recruiting Command (USAREC) sponsored our research in simulating the workings of an Army recruiting station in an effort to help understand more about the recruiting process. Specifically, USAREC wanted a management tool to examine the effects of changing a variety of controllable factors on the way an individual recruiting station performs. We focused this study on the effect of recruiting station commander leadership on recruiting productivity, and the differences in processing times and success rates for different types of applicants (potential recruits). This study added capability to a previous simulation model developed at the Air Force Institute of Technology (AFIT) (J.D. Cordeiro and M.A. Friend, 1998). Cordeiro and Friend's model depicted an Army recruiting station with three types of recruiters, and a single (average) recruiter type. For the current work, we needed to gather pertinent data on the effects of leadership and on the differences between different applicant types. In addition, we needed to incorporate the new data into the simulation model. The remainder of the paper includes an overview of the recruiting process, considerations for modeling leadership effects and applicant types, discussion of incorporating these features in the simulation, a brief look at some simulation output, and some future research interests.
7380263
Getting more from the data in a multinomial selection problem
We consider the problem of determining which of k simulated systems is most likely to be the best per former based on some objective performance measure. The standard experiment is to generate v in dependent vector observations (replications) across the k- systems. A classical multinomial selection pro cedure, BEM (Bechhofer, Elmaghraby, and Morse), prescribes a minimum number of replications so that the probability of correctly selecting the true best system meets or exceeds a prespecified probability. Assuming that larger is better, BEM selects as best the system having the largest value of the performance measure in more replications than any other. We propose using these same v replications across k systems to form vk pseudoreplications (no longer in dependent) that contain one observation from each system, and again select as best the system having the largest value of the performance measure in more pseudoreplications than any other. We expect that this new procedure, AVC (all vector comparisons), dominates BEM in the sense that AVC will never require more independent replications than DEM to meet a prespecified probability of correct selection. We present analytical and simulation results to show how AVC fares versus BEM for different underly ing distribution families, different numbers of populations and various values of v. We also present results for the closely related problem of estimating the probability that a specific system is the best.
3221324
How common random numbers affect multinomial selection
We consider the effect of common random numbers (CRNs) among simulated systems in two different multinomial selection procedures: the classical procedure BEM (Bechhofer, Elmaghraby, and Morse) and procedure AVC (all vector comparisons). CVe examine a simple two population scenario and demonstrate analytically how CRNs affect the probability of correct selection (PCS). In addition, various levels of positive correlation are induced to approximate the effect of using CRNs with complex simulated systems. We present analytical and simulation results to show the effect correlation has on the probability of correct selection (PCS) for each solution procedure. This paper responds to Jim Wilson’s question raised at WSC’96 about how the use of CRNs affects multinomial selection procedures.
8365397
Input modeling when simple models fail
A simulation model is composed of inputs and logic; the inputs represent the uncertainty or randomness in the system, while the logic determines how the system reacts to the uncertain elements. Simple input models, consisting of independent and identically distributed sequences of random variates from standard probability distributions, are included in every commercial simulation language. Software to fit these distributions to data is also available. In this tutorial we describe input models that are useful when simple models are not.
468883
Research and commercial opportunities in Web-Based Simulation
Abstract In a few short years, Web-Based Simulation has exhibited explosive growth in the simulation research community. This paper which grew out of a panel discussion at the 2000 WEBSIM Conference, briefly reviews the development of this research area and considers future opportunities, both on the research and commercial sides. On the research side, the area is maturing, but still quite active. Efforts to expanded Web-Based Simulation to include new capabilities beyond those found in conventional simulation technology or provide interoperation with other information processing technology are particularly promising. On the commercial side, a critical mass of research knowledge is now available. However, some catalyst is needed to produce any substantial movement of simulation vendors toward Web-Based Simulation. This could happen quickly under a couple of scenarios: A small simulation vendor focused on Web-Based Simulation could begin to claim significant market share. Alternatively, the development of a “killer-app” to demonstrate a clear advantage to Web-Based Simulation could make this shift happen very quickly.
5811489
Java-based query driven simulation environment
The concept of Web-based simulation can be finally realized using Java. We have used Java to create a powerful simulation modeling library which is based on the process interaction paradigm. Java threads make it easy for us to implement each process (or active entity) as a thread. Our library, JSIM, supports both simulation and animation thus rendering the model developer's job easier. An important component of our approach is that we integrate our Java Simulator with a Database Management System (DBMS). The conceptual basis for this integration is an environment based on Query Driven Simulation, which allows simulation analysts to study the performance of systems simply by querying a database. Simulation inputs and outputs are stored in databases, and simulation models can be launched as a part of query processing. This integration also resolves a security restriction problem which Java imposes. Most browsers (e.g., Netscape Navigator) restrict Java applets from writing into a file which may be necessary to make simulation results available to the user. Our approach bypasses this problem by writing into a database. Databases are much less of a security concern since they already have substantial authorization mechanisms.
1650301
The spreadsheet interface: A basis for end user programming
10095666
Simulation modeling within workflow technology
This paper presents an approach for integrating simulation modeling and analysis capabilities within the workflow management system (WFMS) being developed in the Large Scale Distributed Information Systems (LSDIS) Lab at the University of Georgia. Simulation modeling can be used for studying the efficiency of workflow designs as well as studying the general performance and reliability of WFMSs. We also discuss the importance of using sophisticated monitoring and animation capabilities, and the use of workflow management technology to advance simulation technology itself. Finally, we demonstrate a sample simulation where tasks and task managers are simulated.
13902265
Simulation and the semantic Web
One of the missions of the semantic Web is to put more knowledge on the Web in an organized fashion and link it to other information and data sources. Three successively more capable languages are (or will soon be) provided for this: RDF, OWL, and SWRL. This paper makes a case for using all three for the domain of modeling and simulation. Based on experience developing the discrete-event modeling ontology (DeMO) some observations on the issues and challenges involved in creating such ontologies are presented. An approach for decomposing models into behavioral and observable parts, a la hidden Markov models, which can make ontologies smaller and easier to understand, is also discussed