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Optimisation technologies have become key tools in making important business decisions that increase competitive advantage. Optimisation, through the use of advanced mathematics and computer science techniques, is used to assist organisations with solving their complex business problems in areas such as manufacturing, distribution, finance and scheduling. The success of optimisation projects depends on many different factors such as which modelling tools are used, integration with corporate data and the selection of the most efficient solution algorithms available for the problem. The purpose of this optimisation workshop is to provide participants with an insightful overview and give step-by-step instructions for successfully building optimisation applications.
In this workshop, our instructors, who all have years of experience in this field, will take you through all the steps of an optimisation project using powerful optimisation tools such as AMPL/AMPL Studio Modelling System, CPLEX and FortMP. The purpose of the workshop is to show how optimisation models, relational data and optimisation algorithms can be brought together in one cohesive business application.
This workshop is an advanced course designed to allow individuals with various levels of optimisation knowledge to attend. Some previous exposure to optimisation is helpful.
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Day 1
TIME |
TOPIC |
Presenter |
9.00 |
REGISTRATION AND COFFEE |
|
9.15 |
Ice Breaker session |
|
9.30 |
Introduction and Overview |
|
9.40 |
Introduction to LP Terminology, model representation and mathematical models |
Gautam Mitra |
10.30 |
COFFEE BREAK |
|
11.00 |
An Introduction to Modelling via AMPL Studio
Participants will learn how to use various functionalities of AMPL Studio |
Christian Valente |
11.30 |
An Introduction to AMPL Syntax
A formal presentation of basic AMPL modelling constructs |
Christian Valente |
12.00 |
Efficient/Structured Modelling
A process to create an efficient model starting from the problem is presented |
Gautam Mitra |
12.30 |
Goal programming/Elastic Constraints
Presentation of an introductory financial model that includes goal programming |
Katharina Schwaiger |
13.15 |
LUNCH |
|
14.15 |
Using EXCEL as data source for AMPL
How to connect an AMPL model to Excel |
Christian Valente |
14.45 |
Workshop (I) Financial Model
Participants investigate, formulate and solve an introductory financial model using AMPL |
Cormac Lucas,
Katharina Schwaiger, Christian Valente |
15.15 |
TEA BREAK |
|
15.30 |
Hands-on models partial description: bond stripping, portfolio, ALM, supply chain
The models for the hands-on sessions will be described and hints for their implementation will be given |
Cormac Lucas,
Katharina Schwaiger, Christian Valente |
16.00 |
Hands-On Session
The attendees should form groups and implement one of the models presented in the previous session |
Cormac Lucas,
Katharina Schwaiger, Christian Valente |
17.00 |
Discussion and Feedback |
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Day 2
TIME |
TOPIC |
Presenter |
9.00 |
COFFEE |
|
9.30 |
Mixed Integer Programming Problems
Integer problems involving binary variables, semi-continuous variables and special ordered set variables are introduced. A few discrete programming problems are explained |
Gautam Mitra |
10.30 |
COFFEE BREAK |
|
11.00 |
Case study: IP with buying threshold
An IP model with semi-continuous variables is introduced |
Cormac Lucas |
11.45 |
An Introduction to AMPL scripting functionalities
Introduction to AMPL’s powerful scripting functionalities |
Christian Valente |
12.15 |
Continuation of Hands-On Session
The groups should continue the implementation of the chosen models and prepare brief presentations of their results |
Cormac Lucas,
Katharina Schwaiger, Christian Valente |
13.15 |
LUNCH |
|
14.15 |
Introducing AMPL-COM
How to embed optimisation models in applications |
Christian Valente |
15.00 |
TEA BREAK |
|
15.30 |
Part I: Heuristic for solving Integer Programs using AMPL Script
Different kind of heuristics to speed up solution of problems are here proposed and prototyped using AMPL scripting functionalities |
Cormac Lucas |
16.00 |
Part II: AMPL-COM implementation of AMPL script procedures
Examples of integration of models and scripts into applications |
Christian Valente |
16.30 |
Attendees’ Presentations and feedback
The groups have ten minutes each to present the model they implemented and their results |
|
Day 3
TIME |
TOPIC |
Presenter |
9.00 |
COFFEE |
|
9.30 |
Stochastic Programming: optimum decision making under uncertainty: an overview
A theoretical background to decision making under uncertainty will be given, with a particular focus on Stochastic Programming |
Gautam Mitra |
10.30 |
Stochastic Programming and Risk Measures |
Gautam Mitra |
11.00 |
COFFEE BREAK |
|
11.30 |
Two Stage SP: Expected value, scenario analysis and deterministic equivalent approaches
Different approaches to SP using modelling languages |
Katharina Schwaiger, Christian Valente |
12.30 |
LUNCH |
|
13.30 |
Hands-on: Expected Value, Wait and See and Deterministic Equivalent: an ALM model
Various models will be described and attendees will be helped with their implementation in AMPL |
Katharina Schwaiger, Christian Valente,
Victor Zverovich |
14.30 |
Stochastic Extensions to AMPL: SAMPL and SPInE
AMPL Language extensions to represent SP, Chance Constrained, Integrated Chance Constrained and Robust Optimisation problems are presented |
Christian Valente |
15.15 |
SAMPL Example: an ALM model
An ALM model will be refined by the introduction of uncertainty and expressed using SAMPL syntax |
Christian Valente |
15.45 |
TEA BREAK |
|
16.00 |
Solution Methods for Stochastic Programming |
Victor Zverovich |
16.20 |
Accelerating Benders’ decomposition through regularisation |
Csaba Fabian |
16.40 |
Optimising the Conditional Value at Risk |
Csaba Fabian |
17.00 |
Discussion and Feedback |
|
Day 4
TIME |
TOPIC |
Presenter |
9.00 |
COFFEE |
|
9.30 |
Stochastic Programming and Scenario Generation:
A modelling perspective
The role of scenario generation in SP will be illustrated |
Gautam Mitra |
10.00 |
Scenario Generation: overview and desirable properties |
Gautam Mitra |
10.30 |
COFFEE BREAK |
|
11.00 |
Scenario Generation library in SPInE
The functionalities of the scenario generation library in SPInE will be presented |
Christian Valente |
11.30 |
Hands-on: formulation of SP models in SAMPL
Various SP models will be described and attendees will be helped in their implementation in SAMPL |
Katharina Schwaiger, Christian Valente,
Victor Zverovich |
12.30 |
LUNCH |
|
13.30 |
Strategic gas purchase portfolio planning for public utilities under uncertainty |
Achim Koberstein |
14.15 |
Hands-on: formulation of SP models in SAMPL
Chance Constraint and Integrated Chance Constraint formulation of some models in SAMPL |
Katharina Schwaiger, Christian Valente,
Victor Zverovich |
15.15 |
TEA BREAK |
|
15.30 |
Integration of financial hedging instruments in optimization models for strategic production network planning under exchange rate and demand uncertainty in the automotive industry |
Achim Koberstein, Marc Naumann |
16.00 |
Hands-on: formulation of SP models in SAMPL
Various SP models will be described and attendees will be helped in their implementation in SAMPL |
Katharina Schwaiger, Christian Valente,
Victor Zverovich |
17.00 |
Discussion and Feedback |
|
Day 5
TIME |
TOPIC |
Presenter |
9.00 |
REGISTRATION AND COFFEE |
|
9.30 |
Introduction and overview |
Gautam Mitra |
9.45 |
Formulation of Quadratic Programming problems and Mean Variance efficient frontier |
Gautam Mitra |
10.15 |
Hands-on: computation of mean variance efficient frontier |
Gautam Mitra
Diana Roman |
10.45 |
Hands-on: representation of discrete constraints in portfolio planning |
Gautam Mitra |
11.15 |
COFFEE BREAK |
|
11.45 |
Portfolio construction using stochastic dominance and reference distribution |
Diana Roman |
12.30 |
LUNCH |
|
13.30 |
Mean variance and CVAR: a multi-objective model |
Gautam Mitra |
14.15 |
Hands-on: mean variance and CVaR model |
Gautam Mitra
Diana Roman |
14.45 |
Indexation and enhanced indexation models for portfolio planning |
John Beasley |
15.30 |
Discussion and Feedback |
|
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Professor Gautam Mitra is an internationally renowned research scientist in the field of Operational Research in general and computational optimisation and modelling in particular. He has developed a world class research group in his area of specialisation with researchers from Europe, UK & USA. He has published three books and over hundred refereed research articles. He was Head of the Department of Mathematical Sciences, Brunel University between 1990 and 2001. In 2001 he has established CARISMA: The Centre for the Analysis of Risk and Optimisation Modelling Applications. CARISMA specialises in the research of Risk and Optimisation and their combined paradigm in decision modelling. Professor Mitra is a Director of OptiRisk Systems UK and OptiRisk India. Many of the research results of CARISMA are exploited through these companies.
Dr. Cormac Lucas has extensive knowledge of Mathematical programming modelling, Pre-analysis and reduction techniques in linear programs and representation of logical expressions as MIPs. Dr Lucas has a PhD and BSc degree from Brunel University. He has held academic positions at CARISMA, Brunel University, London. Dr Lucas has published extensively in the area of optimisation modelling. He has led a number of industry projects on scheduling and decision support.
Dr. Katharina Schwaiger is a KTP Post-Doc Associate at OptiRisk Systems. She received her PhD in Operational Research on the topic of "Asset and Liability Management under Uncertainty: Models for Decision Making and Evaluation" from CARISMA (Centre for the Analysis of Risk and Optimisation Modelling Applications) in 2009. Prior to this she gained a First Class BSc in Financial Mathematics from Brunel University. Her work experience includes internships in Applied Research, Equities and Asset Management at Metzler Investments and Metzler Asset Management Frankfurt, Germany, sponsored research on the topic Liability Driven Investment at Insight Investments, London and an EPSRC funded research internship at ACE Ltd, London.
Dr. Christian Valente joined OptiRisk in 2005 as software engineer, coming from the field of Artificial Intelligence. He has participated in the development and maintenance of many of the company’s products. Along with Dr Lucas he is main responsible in holding workshops and training sessions, and he is the main technological advisor for external projects. He is the main designer and developer of SPInE, the OptiRisk modeling system for Stochastic Programming.
He has completed his PhD in Mathematics at Brunel University, and his main research interests are Stochastic Programming and parallel computing. He has a first class degree in Computer Science from Politecnico di Milano, Milan, Italy and an MSc equivalent in Artificial Intelligence from the same institution. He holds a ISEB Foundation certificate in software Testing.
Dr Valente speaks fluent Italian and English and has a good knowledge of German.
Mr. Victor Zverovich graduated from Belarusian State University in 2003 with a first class honours degree (diploma with distinction) in Mathematics. He has several years of software development experience in large projects in areas of natural language processing and IT service analysis.
He is also currently completing a PhD at CARISMA, Brunel University. His current research interests include stochastic programming from the perspective of solution algorithms and modelling languages.
His computing skills include:
(i) Optimisation Modelling and Solvers: AMPL, FortMP;
(ii) Programming Languages: C, C++, C#.
Mr Zverovich speaks Russian and English.
Dr. Diana Roman has a PhD in Models for Choice under Risk, from the School of Information Systems, Computing and Mathematics, Brunel University, UK; MSc in Applied Statistics and Optimisation, and BSc in Mathematics, from University of Bucharest, Romania.
Dr Roman is now a faculty member of CARISMA, a lecturer in the school of The School of Information Systems, Computing & Mathematics at Brunel University.
Formerly she was a software developer at OptiRisk Systems (KTP associate in a partnership between OptiRisk systems and Brunel University), tasked with designing a software library of scenario generators to be integrated within the SPInE system.
Her work experience comprises several years as a teaching assistant in the Department of Mathematics, Technical University of Civil Engineering, Bucharest. Her research interests include Risk decisions in finance (portfolio optimisation), financial risk measurement and modelling, scenario generation, stochastic programming. Dr Roman speaks Romanian and English.
Dr. Csaba Fabian has 15 years' experience in optimisation and decision support modelling; in particular he specialises in computational models for decision making under risk.
He has an MSc and PhD degrees from Eotvos Lorand University, Hungary.
He is senior researcher at Kecskemet College, Hungary; and lecturer at the Department of Operational Research, Eotvos Lorand University, Hungary. He supervises PhD researchers at Kecskemet College and is an Advisor to OptiRisk Systems. Dr Fabian speaks Hungarian and English.
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