Simulation for Decision Support

Simulation for Decision Support within the

Intelligent Modelling an Analysis Research Group

Version 17/02/2016

Peer-Olaf Siebers

UoN CompSci

IMA: Intelligent Modelling and Analysis

• IMA is part of the School of Computer Science – 8 academic staff; 6 research fellows; 32 PhD students

– £5m as Principal Investigators + £25m as Co-Investigators

• Mission – Intelligently analyse and model complex data

– Creating new techniques (e.g. in data mining)

– Novel methods of addressing real-world problems

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• Strong links to the Advanced Data Analysis Centre – Linking IMA research outputs to real-world applications

• Strong links to newly appointed data science professors – Thomas Gärtner

– Natasa Milic Frayling

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• Quantitative research methods – AI-based Data Mining

– Evolutionary and other Bio-Inspired Algorithms

– Computational Modelling of Complex Systems

– Discrete and Agent-Based Simulation

– Multi-Criteria Decision Analysis

– Fuzzy Methodologies

– Medical Image Analysis

– Multi-Sensor Data Fusion

• Qualitative research methods – Structured Interviews

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Simulation Modelling Framework

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It's all about Agents and Agent-Based Modelling

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Research Interests

• Technical Aspects – From archetypes to multi-agent systems

– Engineering agent-based social simulations

• Using UML to define agents and their interactions

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From Archetypes …

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…

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… to Multi-Agent Systems

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Research Interest

• Applications – My Mission: Applying ABM to as many fields as possible

• Business studies (Risk Assessment; CBA; MCDA)

• Economics (Game Theory; Agent Based Computational Economics)

• Social Sciences (Political Science; Social Simulation)

• Engineering (Manufacturing; Urban Modelling; Energy; Transportation)

• Computer Science (Robotics; Game Development)

• Systems Biology

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Research Funding

• COI in "Future Energy Decision Making for Cities: Can Complexity Science Rise to the Challenge?"; EPSRC EP/G05956X/1 (£263,879); related to EPSRC EP/G059780/1

• PI in Test Driven Object Oriented Simulation Modelling; funded internally (£1,500)

• COI in Sustaining Urban Habitats: An Interdisciplinary Approach; Leverhulme RP2013-SL-015 (£1,750,000 x 2)

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Research Funding

• COI in Agent-Based Modelling for Simulating Peacebuilding: A Feasibility Study; funded internally by CompSci (£5,100)

• PI in Creating an Artificial Hotspot Laboratory Prototype for Investigating HGV Hotspot Incidences; funded internally by D^3 RPA Discipline Bridging Fund + ADAC (£11,200)

For more details see http://www.cs.nott.ac.uk/~pszps/research.html

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http://www.cs.nott.ac.uk/~pszps/research.html

http://www.cs.nott.ac.uk/~pszps/research.html

My Research Projects

• The Impact of Human Performance Variation on the Accuracy of Manufacturing System Simulation Models

• A Multi-Agent Simulation of Retail Management Practices

• Modelling and Analysing the Cargo Screening Process

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My Research Students

• Main supervisor – Sudhir Venkatesan: Comparative study of different analytical

paradigms for the evidence on antiviral treatment effectiveness for A(H1N1) pandemic influenza

– Olusola Theopilus: Exploring the usefulness of ABM/S to simulate and stimulate modal shift from road to rail

– Tuong Vu: A Software Engineering Approach for Agent-Based Modelling of Public Goods Game

– Mazlina Abdul Majid: Human Behaviour Modelling: An Investigation Using Traditional Discrete Event And Combined Discrete Event and Agent-Based Simulation

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My Research Students

• Additional supervisor – Tim Whiteley: Integrated whole system modelling and optimisation of

city resource flows

– Kunpeng Wang: Multi-scale model integration for the large-scale analysis of complex urban energy system

– James Burnett: What can user data relating to proximity and orientation tell us about real-world vs. simulation for interactive content delivery

– Felix Osebor: Sustainable urban mobility: A modelling framework for cities in rapidly developing countries

– Xia Li: Port operation evaluation with simulation and fuzzy based multi-stakeholder multi-criteria decision analysis

– Jacob Chapman: Multi-agent stochastic simulation of occupants' comfort and behaviour

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Outstanding Dissertation Students

• BSc and MSc students – Lim Zhi En: Using a Hybrid Approach on Climate Assessment

Modelling: Development of the HCAM Decision Support Tool

– Kukuh Nasrul Wicaksono: Study on Human Oriented System Simulation: Comparison of Different Methods to Represent Human Behaviour

– Adam Perkins: Modelling and Simulation of Rail Passengers to Evaluate Methods to Reduce Dwell Times

– Leanne May: Using Simulation to Assist Recruitment in Seasonally Dependant Contact Centers

– Olusola Faboya: On the Search for Novel Simulation Applications to Support Airport Operations Management

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Case Studies

Department Store Customer Service

• Case study sector – Retail (department store operations)

• Developing some tools for understanding the impact of management practices on company performance – Operational management practices are well researched

– People management practices are often neglected

• Problem: – How can we model proactive customer service behaviour?

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Cargo Screening Processes at Calais Ferry Port

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Sustaining Urban Habitats

• For more information see: http://www.cs.nott.ac.uk/~pos/research.html

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http://www.cs.nott.ac.uk/~pos/research.html

Thanks to Anthony Beck (LUCAS) for the poster! 24

Simulating Rail Passengers

• The rail network in the UK is fast approaching maximum capacity and passenger numbers are growing 6-7% per year

• One relatively simple (and therefore cheap) way to increase capacity of the rail network is to reduce loading/unloading times (dwell time)

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• The "social force model" (Helbing and Molnar 1995) assumes that the acceleration, deceleration and directional changes of pedestrians can be approximated by a sum of different forces, each capturing a different desire or interaction effect.

http://futurict.blogspot.it/2014/12/social-forces-revealing-causes-of.html

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Modal Shift: From Road to Rail (with HF)

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SimPB: Simulating Peace Building

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SimPB: Simulating Peace Building

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Using a Hybrid Approach on Climate Assessment Modelling

• Global warming has been a profound indicator of human-induced climate change since the mid-20th century.

• At present, the integrated assessment models used by scientists and policy makers are mostly built using a SD approach, which views a system at an aggregate level.

• In our research we developed a Hybrid Climate Assessment Model (HCAM) , a fully integrated climate policy assessment tool which contains a System Dynamics climate-economy model and an agent-based population model.

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• Sector Boundary Map

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• Representation of the system

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• Representation of people

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Using Simulation to Assist Recruitment in Seasonally Dependant Contact Centers

• The weather is unpredictable and can have a large impact on the profitability of seasonal businesses, particularly if staffing requirements are highly temperature-dependent

• An example for such a business is a company that provides boiler maintenance and repair services – In particular their Call Centre (CC) staffing level requirements depend

very much on the severity of the winter

– The likelihood of boilers breaking down during winter is correlated to the severity of the winter

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Using Simulation to Assist Recruitment in Seasonally Dependant Contact Centers

• Challenge – If recruitment starts too early then staff will have increased idle time

– If recruitment starts too late and the work increases faster than staff can cope, there will be lots of complaints and lost customers

• Aim – To develop a novel simulation tool that helps managers to make better

informed decisions about their CC recruitment needs (of permanent an temporary staff)

• Timing for hiring new staff

• Deciding about the optimal length of temporary contracts

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Documents

Simulation for Decision Support