Arnold H. Buss

Web-based simulation: revolution or evolution?

The nature of the emerging field of web-based simulation is examined in terms of its relationship to the fundamental aspects of simulation research and practice. The presentation, assuming a form of debate, is based on a panel session held at the first International Conference on Web-Based Modeling and Simulation, which was sponsored by the Society for Computer Simulation during 11-14 January 1998 in San Diego, California. While no clear “winner” is evident in this debate, the issues raised here certainly merit ongoing attention and contemplation.

Discrete-event simulation on the World Wide Web using Java

This paper introduces Simkit, a small set of Java classes for creating discrete event simulation models. Simkit may be used to either implement stand-alone models or Web page applets. Exploiting network capabilities of Java, the lingua franca of the World Wide Web (WWW), Simkit models can easily be implemented as applets and executed in a Web browser. Javas graphical capabilities enable the rapid development of intuitive user interfaces. Javas use of in terpreted bytecodes, while imposing a performance penalty, enable development of platform-independent models. The languages inherent internet-awareness make other possibilities, such as distributed simulation, much easier to implement.

Economic Analysis of Production Bottlenecks

The management of bottlenecks has become a central topic in the planning and control of production systems. In this paper, we critically analyze bottlenecks from an economic perspective. Using a queueing network model, we demonstrate that bottlenecks are inevitable when there are differences in job arrival rates, processing rates, or costs of productive resources. These differences naturally lead to the creation of bottlenecks both for facilities design and demand planning problems. To evaluate bottlenecks from an economic perspective, we develop the notion of an “economic bottleneck,” which defines resources as bottlenecks based on economic, rather than physical, characteristics.

Driving frequency selection for frequency domain simulation experiments

Frequency domain methodology has been applied to discrete-event simulations to identify terms in a polynomial model of the simulation output. In this paper, the problem of optimally selecting input frequencies is studied. A fast algorithm is presented that closely approximates the optimal solution. The results obtained from the algorithm are compared to known optimal solutions. Tables of input frequencies for various experiments are presented in an appendix.

Activity Delay in Stochastic Project Networks

In this paper we deal with a stochastic project network and consider the impact of activity delay to maximize the expected present value of a project. It is shown that in certain situations, delaying the onset of an activity from its earliest start time can indeed increase the present value of a project due to the postponing of associated negative cash flows. Furthermore, a project that could otherwise be rejected negative expected present value may become profitable positive expected present value due to delay. We demonstrate that even activities on the critical path, as determined by each activitys expected duration, may be profitably delayed. Optimal and approximate procedures are developed to determine the amount of delay of the various activities.

Component based simulation modeling with Simkit

This paper demonstrates how to use Simkit to create discrete event simulation models using a component framework. The component framework is based on a listener design pattern especially useful for simulation models. The objects created are called Listener Event Graph Objects, so the component framework is called LEGO. Simkit is an open source package written in Java.

Distributed simulation modeling: a comparison of HLA, CORBA, and RMI

The execution of distributed simulations has become increasingly important to the Department of Defense (DOD). This paper compares three architectures for supporting distributed computing, HLA, CORBA, and RMI. While the fundamental structure of each is similar, there are differences that can profoundly impact an application developer or the administrator of a distributed simulation exercise.

Cross-paradigm simulation modeling: challenges and successes

This paper addresses the broad topic area of cross-paradigm simulation modeling with a focus on the discrete-event, system dynamics and agent-based paradigms. It incorporates contributions from four panel members with diverse perspectives and areas of expertise. First, each paradigm is described and definitions are presented. The difference between the process-oriented worldview and the event-oriented worldview within discrete-event simulation modeling, and the importance of this difference for cross-paradigm modeling, are discussed. Following the definitions, discussion of cross-paradigm modeling is given for each pair of these paradigms, highlighting current challenges and early successes in these areas. The basic time-advance mechanisms used in simulation modeling are also discussed, and the implications of these mechanisms for each paradigm is explored.

VOLUME AND CAPACITY INTERACTION IN FACILITY DESIGN

Abstract This paper addresses the joint facilities design problem of determining both demand and capacity with stochastic demand arrivals and stochastic processing throughput. Using a simple M/M/1 queueing model of a profit maximizing firm, we link marketing and production decision variables by recognizing appropriate congestion costs, and show that coordinated decision-making provides results superior to making demand and capacity decisions sequentially. Sensitivity analysis indicates that the model is robust with respect to its assumptions and parameters. An example illustrates the approach and demonstrates the application of the model.

Simple movement and detection in discrete event simulation

Many scenarios involving simulation require modeling movement and sensing. Traditionally, this has been done in a time-stepped manner, often because of a mistaken belief that using a pure discrete event approach is infeasible. This paper discusses how simple motion (linear, uniform, two-dimensional) and simple sensing can be modeled with a pure discrete event approach. We demonstrate that this approach is not only feasible, it is often more desirable from several standpoints.