Discrete EventProcess Models

andMuseum Curation

Louis G. ZachosAnn Molineux

Non-vertebrate Paleontology LaboratoryTexas Natural Science Center

The University of Texas at Austin

Discrete Event Simulation

• What is DES?• Many processes can be represented as a

series of discrete events or activities.

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 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 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

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/)

http://simpy.sourceforge.net/

Process Object Model• DES in SimPy is based on the definition of

Object Classes• 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

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

• Start with the most general

Model Design

• Break each process into sub-processes

Resources

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

Stores• The entities being processed – museum

specimens – are represented as stores• Stores act like queuing bins -

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 Model

• Resources• People• Cameras• Computer workstations• Stores – fossil specimens and labels• Simplest case – individual resources are alike• Variability is modeled stochastically

Modeling Results

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

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

Validation

• Model results must be validated against actual system throughput

• Actual process is timed and variability modeled

Extrapolation

• 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

Conclusion

• 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