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Discrete Event Process Models and Museum Curation

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

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  1. Discrete EventProcess ModelsandMuseum Curation Louis G. Zachos Ann Molineux Non-vertebrate Paleontology Laboratory Texas Natural Science Center The University of Texas at Austin

  2. Discrete Event Simulation • What is DES? • Many processes can be represented as a series of discrete events or activities.

  3. 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

  4. 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

  5. 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

  6. 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/

  7. 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

  8. Model Design • A system can be decomposed in a top-down, hierarchical manner • Start with the most general

  9. Model Design • Break each process into sub-processes

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

  11. Stores • The entities being processed – museum specimens – are represented as stores • Stores act like queuing bins -

  12. 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

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

  14. Modeling Results Can capture various aspects of a process and realistically model throughput and variability

  15. 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

  16. Validation • Model results must be validated against actual system throughput • Actual process is timed and variability modeled

  17. 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

  18. 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

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