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Discrete Event Simulation

In my studies in Industrial Engineering/Operation Research, I became very interested in Discrete Event simulation (actually I became interested in simulation back in my undergraduate work, but that is another story). I was preparing a research proposal on this area, when for several reasons I decide not to continue at this time (hence the Masters of Engineering). So that my work would not go to waste, I have created this site and attached articles to make the work available to others.

What is Discrete Event Simulation?

In classical thinking there are three types of simulation; discrete event, continuous, and MonteCarlo. They were articulated by Nance (1993) as:

    Discrete event simulation utilizes a mathematical/logical model of a physical system that portrays state changes at precise points in simulated time. Both the nature of the state change and the time at which the change occurs mandate precise description. Customers waiting for service, the management of parts inventory or military combat are typical domains of discrete event simulation.

    Continuous simulation uses equational models, often of physical systems, which do not portray precise time and state relationships that result in discontinuities. The objective of studies using such models do not require the explicit representation of state and time relationships. Examples of such systems are found in ecological modeling, ballistic reentry, or large scale economic models.

    Monte Carlo simulation, the name given by John van Neumann and Stanislaw M. Ulam to reflect its gambling similarity, utilizes models of uncertainty where representation of time is unnecessary. The term originally attributed to "a situation in which a difficult non-probabilistic problem is solved through the invention of a stochastic process that satisfies the relations of the deterministic problem". A more recent characterization is that Monte Carlo is "the method of repetitive trials. Typical of Monte Carlo simulation is the approximation of a definite integral by circumscribing the region with a known geometric shape, then generating random points to estimate the area of the region through the proportion of points falling within the region boundaries.

In current thinking and work these lines are becoming less distinct, but for this site my emphasis is Nance’s discrete event simulation (DES). This site will try to present an overview of DES as a tool. Particular emphasis will be on the use of DES in engineering decision support.

The following topics are covered in "Introduction to DES" (

    Selecting a Simulation and Modeling Language
    Evaluating a Simulation and Modeling Package
    Using Discrete Event Simulation in Decision Support
    What is Hybrid Simulation

I have also include an Annotated bibliography  ( and a list of acronyms ( that was used in preparing this site.

I have also included the draft of my research proposal, entitled: “Decision Support in Specialty Chemical Operations: a hybrid simulation based multi-criteria multi-objective optimization system.” (

My goal is to eventual complete this research, and I am currently looking for a doctoral program to pursue it under (advisors/sponsors sought).



MIke Albrecht, P.E.

o   40+ years’ experience in the mining industry with strong mineral processing experience in precious metals, copper, industrial minerals, coal, and phosphate

o   Operational experience in precious metals, coal, and phosphate plus in petrochemicals.

o   Extensive experience performing studies and determining feasibility in the US and international (United States, Canada, Mexico, Ecuador, Columbia, Venezuela, Chile, China, India, Indonesia, and Greece).

o    E-mail: