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Discrete Event Process Models and Museum Curation. Louis G. Zachos Ann Molineux Non-vertebrate Paleontology Laboratory Texas Natural Science Center The University of Texas at Austin. Discrete Event Simulation. What is DES?

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Discrete event process models and museum curation

Discrete EventProcess ModelsandMuseum Curation

Louis G. Zachos

Ann Molineux

Non-vertebrate Paleontology Laboratory

Texas Natural Science Center

The University of Texas at Austin

Discrete event simulation
Discrete Event Simulation

  • What is DES?

  • Many processes can be represented as a series of discrete events or activities.

Discrete event simulation1
Discrete Event Simulation

  • Events occur at an instant in time, persist for some period of time, and mark a change of state in the process – they are the individual – discrete - steps in the staircase of a process.

  • DES is a computational (i.e., computer) model of a system of real-life processes modeled as multiple series of discrete events

Functionality of des modeling environment
Functionality of DESModeling Environment

  • In practical terms, a DES is comprised of a model and the environment in which it is executed

  • It is possible to design a DES as a single computer program – but there is software to create a modeling environment for a DES

Des modeling environment components house keeping functions
DES Modeling EnvironmentComponents(House-Keeping Functions)

  • Clock

  • Random Number Generators for a Variety of Probability Density Functions

  • Statistics Collation and Graphing Capability

  • Events, Resources, Stores Lists Handling

  • Conditions and System State Handling

Simpy sim ulation in py thon
SimPySimulation in Python

  • An Open Source object-oriented discrete-event simulation language based on

  • “Many users claim that SimPy is one of the cleanest, easiest to use discrete event simulation packages!” (from http://simpy.sourceforge.net/)


Process object model
Process Object Model

  • DES in SimPy is based on the definition of ObjectClasses

  • There are 3 classes:

  • Process class – the object that “does something”

  • Resource class – objects required to “do something”

  • Monitor class – an object to record information

Model design
Model Design

  • A system can be decomposed in a top-down, hierarchical manner

  • Start with the most general

Model design1
Model Design

  • Break each process into sub-processes


  • Resources are things like people, cameras, computer workstations, etc. – required to perform processing.


  • The entities being processed – museum specimens – are represented as stores

  • Stores act like queuing bins -

Npl model
NPL Model

  • Photography of type specimens

  • Scan labels

  • Prepare and scan

  • Photograph specimens

  • Prepare and photograph

  • Convert raw imagery

  • Process multi-focus imagery with Helicon

  • Cleanup and standardize imagery in Photoshop

Npl model1
NPL Model

  • Resources

  • People

  • Cameras

  • Computer workstations

  • Stores – fossil specimens and labels

  • Simplest case – individual resources are alike

  • Variability is modeled stochastically

Modeling results
Modeling Results

Can capture various aspects of a process and realistically model throughput and variability

Modeling results1
Modeling Results

Bottlenecks in the process become readily apparent – in this example the process waits on human resources – just adding another camera would not improve throughput


  • Model results must be validated against actual system throughput

  • Actual process is timed and variability modeled


  • Once a working model has been validated:

  • Bottlenecks can be quantified

  • The effects of varying resources or changing order of processes can be evaluated

  • Reliable estimates of time to completion for entire projects can be made


  • Discrete event simulations can be a useful tool for evaluating long-term projects in the museum environment

  • The methodology makes the results easier to justify for budget or grant applications

  • The development of a model aids in understanding the underlying processes