Mike Albrecht, P.E.(AZ)

We welcome your problems with enthusiasm!

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.

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 the
attached pdf "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).

· Registered Professional Engineer Arizona (Mining)

· Licensed General Engineering Contractor California

· ME – Colorado State University, Ft. Collins, CO

· MBA
- California State University, Hayward, California

· BS Engineering - Michigan Technological University, Houghton, Michigan