Adventures in Modelling & SimulationAn REF 2014 Impact Case Study

Dr Simon J E Taylor(A top 100 computer science/simulation researcher worldwide

(top 8 UK) – MAS)

Overview

• Modelling & Simulation• REF2014

– Impact case study: High Performance Simulation• Background• High Performance Simulation

– Grid Computing– Distributed Simulation

• Conclusions

Modelling & Simulation (M&S)

• Modelling involves the creation of an abstract representation of reality – the model

• Simulation involves experimentation with the model to better understand reality– Models can be used to predict, compare or optimize

process performance

• Typically models are created and simulated using special software– e.g. Simul8 (and many others)

3

Common Simulation Study Steps

• Define problem• Create conceptual model• Create computer model• Verify• Validate• Experiment• Report• Implement Results

Slide 4

Iterative process

Many simulation lifecycles (Law, Robinson, Balci, etc.)

Simulation allows you to…

• Study and experiment with a system• See how results change as the system changes• Understand the relationship between key areas• Investigate before implementation• Train people on the system• BUT REMEMBER – IT’S NOT THE ACTUAL SYSTEM

– SINSFIT (Simulation Is Not Substitute for Intelligent Thinking!!!)

Slide 5

Simulation Areas of Use

• Business and commerce• Manufacturing• Military• Logistics, Supply Chain, Distribution• Transportation & Traffic• Health Care• Construction & Project Management

Slide 6

Screenshot of Simul8 (http://www.simul8.com/)

Example of Simul8

MAP-Guide Project: Prostate Cancer Clinical Pathway v7 in Simul8

Screenshots from AnyLogic (http://www.xjtek.com/)

Container Terminal

Trauma CentreAircraft Fleet Planning

Beer Distribution GameWholesale Warehouse

Customer Support Centre

PADS 2011, 14-17th June, Nice, France

Screenshots from Flexsim courtesy of Saker Solutions (http://www.sakersolutions.com/)

Processes suitable for Simulation: Production, Automation, People Movement and Flow, Warehousing

Simulation: Advantages

• Control• Time compression• Experimentation

– Sensitivity analysis– What-if experimentation

• Training• Non invasive

Slide 12

Simulation: Disadvantages

• Time and money to develop models• Time and money to simulate/experiment

– Some models can take hours to run once• Costly expertise• Results difficult to interpret• Poor understanding of statistical methods

Slide 13

Overview

• Modelling & Simulation• REF2014

– Impact case study: High Performance Simulation• Background• High Performance Simulation

– Grid Computing– Distributed Simulation

• Conclusions

Research Excellence Framework(REF 2014)

• Publications (65%)• Environment (15%)• (Non-academic) Impact (20%)

– REF 2014 Impact Case Study

High Performance Simulation techniques to reduce industrial production and logistics costs through better

management

Impact Value (Examples)• Ford (Dunton) leads simulation activities with Ford’s engine manufacturing

facilities across the world (UK, USA, Mexico, Spain, South Africa and Brazil) – Now uses significantly more simulation in this area and has saved an

estimated £150,000 in consultancy cost (£15K per year) and a significant confidential sum due to process improvements as a result of that extra experimentation.

• Saker Solutions work with many UK manufacturing and service companies to study a wide range of their problems with simulation. – The development of SAKERGRID has enabled them to significantly improve the

quality of their projects and is saving around £20,000 per year.– SAKERGRID is now deployed within the Sellafield Ltd Operational Research

Group where the tool has enabled approximately £200,000+ savings per year. • Whole Systems Partnership is a small consultancy that develops

healthcare decision support solutions. – Collaborative reusable systems dynamics has enabled a £100,000 per year

revenue stream.

Value to UK economy: £1 Million every three years (Disclosed MINIMUM)

Overview

• Modelling & Simulation• REF2014

– Impact case study: High Performance Simulation• Background• High Performance Simulation

– Grid Computing– Distributed Simulation

• Conclusions

Background

• Research assistant post in investigating how to speed up manufacturing systems simulation

• Led to PhD Parallel Discrete Event Simulation of Manufacturing Systems (with Transputers!) (1993)– Ahead of it’s time… (See later!)

• Became generally interested in new computing technologies and M&S– World wide web had just happened and was

revolutionising everything– …and along came Web-based simulation!

Web-based Simulation

• Huge promise– E.g. plug and play models simulated through a web

browser– Major cost savings through reusable models

• Academic studies with no real world stakeholders or commercial buy in

• Totally failed to have any real impact at all

Robinson, S., Nance, R., Paul, R.J., Pidd, M. and Taylor S.J.E. (2004) Simulation Model Reuse: Definitions, Benefits and Obstacles. Simulation Modelling Practice and Theory. 12, 479-494Miller J, Fishwick P.A., Taylor S.J.E., Benjamin, B. and Szymanski, B. (2001). Research and Commercial Opportunities in Web-Based Simulation. Simulation: Practice and Theory. 9 (1-2), 55-72.

EPSRC GROUPSIM Network (2000-4)

• Formed to study the failure of web-based simulation and to determine how distributed computing could have a real world impact on M&S

• Academic and industrial group• Focussed on High Performance Simulation

– (Groupware)– Grid Computing– Distributed Simulation

Groupware and the Simulation Consultant

• Identified early on that M&S practitioners were not particularly IT savvy

• “Roadshow” to demonstrate benefits of using Groupware– Cost savings by sharing and using M&S in real-time– Demonstrations were with real simulation packages and NETMEETING– Ford demo – Brentwood/Detroit

• Adopted by several companies• Great way of establishing credability• Today – still not commonplaceTaylor, S.J.E. (2001). NETMEETING: A Tool for Collaborative Simulation Modelling. International Journal of Simulation: Systems, Science & Technology, 1 (1-2), 59-68.Taylor, S.J.E., Robinson, S. and Ladbrook, J. (2005) An Investigation into the Use of Net-Conferencing Groupware in Simulation Modelling. Journal of Computing and Information Technology, 13 (1), 1-10. Taylor S.J.E. (2000) Groupware and the Simulation Consultant. In Proceedings of the 2000 Winter Simulation Conference. Association for Computing Machinery Press, New York, NY. pp. 83-89.

Overview

• Modelling & Simulation• REF2014

– Impact case study: High Performance Simulation• Background• High Performance Simulation

– Grid Computing– Distributed Simulation

• Conclusions

Grid Computing

Speed up computations by distributing work across many computers

Simple idea, many different technologies, can be VERY difficult to implement

InputModel and Parameters

Master Process- Create jobs

- Distribute jobs

Worker 2- Process job

Worker 1- Process job

Worker 3- Process job

Worker n- Process job

Master Process- Receive Results- Collate Results

OutputCollated Results

Grid Computing and M&SSpecific requirements (e.g.)

– Need specialised manager– Simulation software is typically “installed”– Model may crash but simulation software appears

to be running fine Simulation Experimentation

Manager

Simulation Software & Model

Grid Interface

WorkerWorker

Simulation Software & Model

Grid Interface

WorkerWorker

Simulation Software & Model

Grid Interface

WorkerWorker

Model/Data Repositories

Grid Computing and M&S• WINGRID

– Ford (Witness)– ING Bank (Excel/Monte Carlo)– Initial demo at Saker Solutions (Flexsim)

• e-Infrastructures– Different talk!

• SAKERGRID– Commercial Desktop Grid developed at Saker– Now running in Sellafield PLC

Mustafee, N. and Taylor, S.J.E. (2009) Speeding up simulation applications using WinGrid. Concurrency and Computation: Practice and Experience, 21 (11), 1504-1523.Kite, S., Wood, C., Mustafee and Taylor S.J.E. (2011) SAKERGRID: Simulation Experimentation using Grid Enabled Simulation Software. In Proceedings of the 2011 Winter Simulation Conference. Association for Computing Machinery Press, New York, N.Y. 2283-2293.Mustafee, N. and Taylor, S.J.E. (2010). Speeding-up the Execution of Credit Risk Simulations Using Desktop Grid Computing: A Case Study. In Proceedings of the 2010 Simulation Workshop, UK Operational Research Society, Birmingham, UK, 176-183.Mustafee, N., Alstad, A., Larsen, B., Taylor, S.J.E. And Ladbrook, J. (2006). Grid-enabling FIRST: Speeding Up Simulation Applications Using WINGRID. In Proceedings Of The Tenth IEEE International Symposium On Distributed Simulation And Real-time Applications. IEEE Computer Society. Pp. 157-164. Short Listed For Best Paper Award.

Cloud Computing and M&S

www.cloudsme.eu

DistributedSimulation• Link together

(interoperate) existing models over a network

• Time management issues

• IEEE 1516-2010 High Level Architecture

• Lots in defence, little elsewhere

Distributed Simulation

• Despite HLA being a standard, it is used in all sorts of different (incompatible) ways

• Started a standardisation initiative– HLA CSPIF– SISO CSPI PDG

HLA Middleware

Computer Network

Simulation SoftwareCamshaft

Manufacturing Line Model

CSP Adaptor

Simulation Software

Assembly Line Model

CSP Adaptor

Simulation Software

Engine Block Line Model

CSP Adaptor

Logical model Interactions

Timestamped messages passed via network between models

Simulation Interoperability Standards Organization (SISO)

• HLA CSPIF (2002-5)• CSPI PDG (2005-2012)• CSPI PSG (2012+)

SISO-STD-006-2010

Standard for COTS Simulation Package Interoperability Reference Models. Simulation Interoperability

Standards Organization, Orlando, FL.

Distributed Simulation Implementations

• Ford demonstrator• Distributed Blood Supply Chain• Infineon Semiconductor Manufacturing• John Deere• SellafieldTaylor, S.J.E., Sudra, R., Janahan, T., Tan, G., and Ladbrook, J. (2002). GRIDS-SCF: An Infrastructure for Distributed Supply Chain Simulation. SIMULATION: Transactions of the Society of Modeling and Simulation International.. 78 (5), 312-320. Taylor, S.J.E., Wang, X., Turner, S.J. and Low, M.Y.H. (2006) Integrating Heterogeneous Distributed COTS Discrete-Event Simulation Packages: An Emerging Standards-based Approach. IEEE Transactions on Systems, Man and Cybernetics: Part A, 36 (1), 109-122.Mustafee, N., Taylor, S.J.E., Katsaliaki, K. and Brailsford, S. (2009). Facilitating the Analysis of a UK National Blood Service Supply Chain using Distributed Simulation. SIMULATION: Transactions of the Society of Modeling and Simulation International. 85 (2), 113-128Taylor, S.J.E., Turner, S.J., Strassburger, S. and Mustafee, N. (2012). Bridging The Gap: A Standards-Based Approach to OR/MS Distributed Simulation. ACM Transactions on Modeling and Computer Simulation. 22(4): Article 18.

Moonbase IKB1 2013Simulation Smackdown 2013 Prize Winner

(Most original approach)

Moonbase IKB1 2014SEE 2014 (exploresimulation.com)

Web-based Simulation

System Dynamics

Web-based Simulation

• Whole Systems Partnership• isee systems (iThink)• Modified the web-based version of iThink to

enable results sharing• Significantly improved the potential of Web-

based System Dynamics

Domdouzis, K., Lacey, P., Lodge, D. and Taylor S.J.E. (2013) System Dynamics in Healthcare: A Web Based Approach. In Proceedings of the 2013 European Simulation Multiconference, EUROSIS, Ghent, 309-319.

Conclusions

• New computing technologies and M&S• Learning the industrial context• Translating complex technology into key pinch

points• Sticking with it – can be tough!• Really worth it as it has a major real world

impact