Claude Dennis Pegden

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This paper describes the modeling system --- Simio#8482; --- that is designed to simplify model building by promoting a modeling paradigm shift from the process orientation to an object orientation. Simio is a <b>&lt;u&gt;<i>sim</i>&lt;/u&gt;</b>ulation modeling framework based on <b>&lt;u&gt;<i>i</i>&lt;/u&gt;</b>ntelligent(More)
Simulation models are built using one or more "world views" that provide the underlying framework for defining the system of interest. This tutorial presents an overview and brief history of the alternative modeling world views for discrete event simulation. In specific this tutorial discusses the event, process, and object worldviews, and highlights the(More)
This paper discusses the concepts and methods for simulating manufacturing systems using the SIMAN simulation language. SIMAN is a general purpose simulation language which incorporates special purpose features for modeling manufacturing systems. These special purpose features greatly simplify and enhance the modeling of the material handling component of a(More)
In conventional discrete event simulation svstems, the flow of simulated time is controlled by-a data structure that is variously called the event set, the pending event set, the future-event chain, or the sequencing-set. At any instant, this structure contains records of those events, processes or activities that are to be simulated at some future time as(More)
Responses to the featured paper are provided by four authors who represent different elements of the simulation research community: industry, private research laboratory, and university. As is evident from the reactions given, these perspective provide both shared and distinct observations on model composability as an opportunity for research investment.
Over the 50 year history of discrete event simulation the growth in applications has been facilitated by some key advances in modeling that have simplified the process of building, running, analyzing, and viewing models. Three important advances have been: the modeling paradigm shift from an event to a process orientation, the shift from programming to(More)
&#x201C;Input uncertainty&#x201D; refers to the (often unmeasured) effect of not knowing the true, correct distributions of the basic stochastic processes that drive the simulation. These include, for instance, interarrival-time and service-time distributions in queueing models; bed-occupancy distributions in health care models; distributions for the values(More)
This paper briefly describes Simio<sup>#8482;</sup> simulation software, a <b><i>&lt;u&gt;sim&lt;/u&gt;</i></b>ulation modeling framework based on <b><i>&lt;u&gt;i&lt;/u&gt;</i></b>ntelligent <b><i>&lt;u&gt;o&lt;/u&gt;</i></b>bjects. It then describes a few of the many recent enhancements and innovations including SMORE charts that allow unprecedented(More)