“Draft:Micro Saint Sharp”的意思、由来-开放百科全书

Micro Saint Sharp is a general purpose discrete-event simulation and human performance modeling software tool developed by Alion Science and Technology).^[1] It is developed using C# and the .NET framework. Micro Saint Sharp allows users to create discrete-event simulations as visual task networks with logic defined using the C# programming language.^[2]

Micro Saint Sharp uses a task network, a series of functions which decompose into tasks, to create stochastic models. Micro Saint Sharp has been used in the healthcare^[3]^[4], manufacturing^[5], supply chains^[6], business process re-engineering^[7], workstation design^[8], call centers^[9], transportation^[10], and command and control^[11] areas. It has also been used extensively to simulate human performance and workload.^[12]^[13]

History

Micro SAINT began as a "micro" computer implementation of the SAINT (Systems Analysis of Integrated Networks of Tasks) language in the 1980's.^[14] SAINT was a FORTRAN based discrete event simulation language developed in the 1970's^[15]. SAINT evolved from 1) task analysis and 2) Monte Carlo simulation of operator performance under workload stress as represented by Siegel and Wolf.^[16] Micro SAINT was originally a DOS application developed by Micro Analysis and Design, Inc. (MA&D) and was first released in 1985. In 2003 Micro SAINT was completely redesigned by MA&D to take advantage of the C# programming language and Microsoft .NET environment. This allowed it to be faster and more modular. The tool was renamed Micro Saint Sharp^[17].

Discrete event simulation in Micro Saint Sharp

Micro Saint Sharp models contain a task network called a Network Diagram. The network diagram contains a series of tasks connected by paths which determine control flow. System objects called entities flow through the system to create a simulation. Micro Saint Sharp also includes more low level features such as global variables and subroutines called Functions.

Tasks

The task node is the primary element driving the simulation’s outcome. Task nodes simulate system behavior by allowing programmer specified effects, task duration, and sequences. Task Effects are programmer specified C# expressions where programmers can manipulate variables and data structures when a task is invoked. Task duration can be specified by the programmer as a specific value, through use of a built-in probability distribution, or using a C# expression. Control flow and sequencing can also be specified by the programmer.

Entities

Entities are dynamic objects which arrive into the system and move through the task network. Entities flow from one task to the next based on the task’s path logic. When an entity enters a task, the task’s effects are triggered. When the task concludes, the entity moves to the next task. One entity is generated by default at the beginning of the simulation. More entities can be generated at any point in the simulation based on programmer specified logic. When all entities reach the end node or are destroyed, the simulation concludes.

Events are occurrences that happen in an instant of simulated time within Micro Saint Sharp that can change the global state of the system. This can be the arrival or departure of an entity, the completion of a task, or some other occurrence. The events are stored in a master event log which captures every event that will happen and the simulated time that the event occurred. Due to the stochastic nature of discrete-event simulation, an event will often trigger the generation of a random variate to determine the next time that same event will occur. Thus, as events occur, in the simulation, the event log is altered.

Flow Sequencing

Once a task concludes, the invoking entity moves to another node which is directly connected to the current node in the task network. Nodes can connect to any number of other tasks, so Micro Saint Sharp provides a number of sequencing options to determine the task to which the entity moves.

Variables and Functions

Micro Saint Sharp has a number of global variables used by the system throughout a simulation. Micro Saint Sharp provides the public global variable Clock which tracks the simulation’s current time. Micro Saint Sharp also has private variables. Micro Saint Sharp allows the modeler to create custom global variables which can be accessed and modified in any task node. Variables can be of any type native to C#, but the software provides a list of suggested variable types including C# primitive data types and basic data structures. Micro Saint Sharp also provides the programmer with the functionality to create globally accessible subroutines called functions. Functions work as C# functions and can specify parameters, manipulate data, and return data.

2D and 3D Animation

Micro Saint Sharp has three different visual representations of the model that are available to users. The first representation of the process is a flow diagram, or Task Network – users can visually track entities as they flow through the system, and can immediately identify where bottlenecks and queues are building up. The second visual representation available is a two dimensional animation (Animator) which can show moving objects over floor plans, maps, photos or other 2D images. Animator also provides the capability for charts, graphs, and text to all be displayed while the model is executing on one screen. The third visual representation that can be developed is a three dimensional animation (Animator3D). This gives a realistic picture of the modeled process with moving images. Animator3D includes features such as zooming in/out, rotating, panning around, and saving camera views. Users can decide the level of detail they want to include. Micro Saint Sharp also allows user to view individual pieces of a model with the use of dynamic real-time charting. Over 20 different charts can be saved as files or printed out for analysis.

Optimization

OptTek's OptQuest^[18] optimization software is included in the Gold version of Micro Saint Sharp. OptQuest, through a sophisticated optimization technique, finds the right combination of variables that produces the best results possible. OptQuest works with Micro Saint Sharp by sharing variables. Users can directly choose the variables that will have an effect on the Micro Saint Sharp model results. This includes every aspect of an optimization model including constraints, decision variables, and the optimization problem itself.

Tools Based on the Micro Saint Sharp Engine

Micro Saint Sharp is a general purpose simulation tool. However, several customized tools have been developed that use Micro Saint Sharp as the underlying simulation engine. Each tool was designed for a specific purpose. The table below lists some of the Micro Saint Sharp based tools that have been developed.

References

1. ^Plott,B., Pearson, J., Shaw, C. (2017) Micro Saint Sharp User Manual v3_8. Lulu.com. https://www.amazon.com/Micro-Saint-Sharp-User-Manual/dp/1387180347/ref=sr_1_sc_1?ie=UTF8&qid=1549643512&sr=8-1-spell&keywords=Miro+Saint+Sharp
2. ^Schunk, D. 2000. “Micro Saint: Modeling with the Micro Saint simulation package”. In J. A. Joines, R. R. Barton, K. Kang, & P. A. Fishwick (Eds.), Proceedings of the 32nd WSC conference on Winter Simulation, 274–279. San Diego, CA: WSC. https://dl.acm.org/citation.cfm?id=510425
3. ^Gunal, M. Health Service efficiency mapped with simulation tools. Lancaster University. http://www.adeptscience.co.uk/media-room/press_room/health-service-efficiency-mapped-with-simulation-tools.html
4. ^Kanagarajah A.K., Lindsay P., Miller A., Parker D. (2010) An Exploration into the Uses of Agent-Based Modeling to Improve Quality of Healthcare. In: Minai A., Braha D., Bar-Yam Y. (eds) Unifying Themes in Complex Systems. Springer, Berlin, Heidelberg.https://doi.org/10.1007/978-3-540-85081-6_58
5. ^Harshell, J. and Dahl, S., Simulation Model Developed to Convert Production to Cellular Manufacturing Layout, Industrial Engineering, 20, 12 (1988) 40-45.
6. ^Daniel Schunk , Beth Plott, Using simulation to analyze supply chains, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida. https://www.computer.org/csdl/proceedings/wsc/2000/6579/02/65791095.pdf
7. ^Laughery, R., Plott, B., & Scott‐Nash, S. (2007). Simulation of Service Systems. 10.1002/9780470172445.ch18.
8. ^ Hoffman, M. S., & Cramer, M. L. (1981). Workstation Design Optimization Through a Simulation Model. Proceedings of the Human Factors Society Annual Meeting, 25(1), 374–378. https://doi.org/10.1177/107118138102500199
9. ^Keller, J., Plott, B. Staff Resource Simulation of Phone Company Switch Support Service. In Proceedings of the 1999 Winter Simulation Conference.
10. ^Sweeney, K., Campbell, J., Sweeney II, D. Impact of Shippers’ Choice on Transportation System Congestion and Performance: Integrating Random Utility with Simulation. Transportation Journal. Spring 2014. Vol. 53. No. 2.
11. ^Plott,B., Wojciechowski,J., Kilduff, P. Command and Control Human Performance Modeling. CSERIAC Gateway. Volume X: Number 1 (1999).
12. ^Laughery, R. (1999). Using discrete-event simulation to model human performance in complex systems. In Proceedings of the 31st conference on Winter simulation Simulation---a bridge to the future - WSC ’99 (Vol. 1, pp. 815–820). New York, New York, USA: ACM Press. http://doi.org/10.1145/324138.324506
13. ^Pop, V.L. (2015) Using Task Network Modeling to Predict Human Error. Dissertation to Georgia Institute of Technology, School of Psychology.https://pdfs.semanticscholar.org/c53c/56023cc909bddb5bba6bdaf86dce8da0d533.pdf
14. ^Drews, C. (1986) Anybody Can Simulate! A Discussion of Micro SAINT.
15. ^Seifert. D.J., Chubb, G.P. (1978) SAINT: A Combined Simulation Language for Modeling Large, Complex Systems. Aerospace Medical Research Laboratory Technical Report. AMRL-TR-78-48.
16. ^Siegel, A. and Wolf, J. (1967) Man-Machine Simulation Models: Performance and Psychological Interaction. New York, John Wiley & Sons, Inc.
17. ^Bloechle, W., Schunk, D. (2003) Micro Saint Sharp Simulation Software. In Proceedings of the 2003 Winter Simulation Conference.
18. ^www.opttek.com/products/optquest
19. ^Rusnock, C. F., & Geiger, C. D. (2013). Using Discrete-Event Simulation for Cognitive Workload Modeling and System Evaluation. Proceedings of the 2013 Industrial and Systems Engineering Research Conference, 2485–2494. Retrieved from http://search.proquest.com/openview/b77033807ade34134e81d078a4513631/1?pq-origsite=gscholar
20. ^Plott, B., Quesada, S. Using an Information-Driven Decision-Making Human Performance Tool to Assess U.S. Army Command, Control, and Communication Issues. In Proceedings of the 2004 Human Factors and Ergonomics Society Annual Meeting.
21. ^Dahn, D., Laughery, R. The Integrated Performance Modeling Environment - Simulating Human-System Performance. In Proceedings of the 1997 Winter Simulation Conference. https://www.informs-sim.org/wsc97papers/1141.PDF
22. ^Scofield, T., Brown, A. Manning and Automation Model for Naval Ship Analysis and Optimization. Virginia Polytechnic Institute and State University Technical Paper.http://www.dept.aoe.vt.edu/~brown/VTShipDesign/ASNEManningPaperRev2.pdf
23. ^Plott, B. (2006) Evaluating Control Displays with the Engineering Control Analysis Tool (ECAT). In Proceedings of the 5. International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology. https://inis.iaea.org/search/search.aspx?orig_q=RN:43118843
24. ^Gore, B. Minimizing Human Risk: Human Performance Models in the Space Human Factors and Habitability and Behavioral Health and Performance Elements. In Proceedings of the 2016 Applied Human Factors and Ergonomics Meeting.
25. ^Walters. B., Bzostek, J., Li, J. (2005) Integrating Human Performance and Anthropometric Modeling in the Crew Station Design Tool. SAE Transactions Journal. Vol. 114, Section 7. pp. 730-734.