Multi-objective Decision Making: Introduction to Goal Programming

This course will teach you the skills needed to make multi criteria decisions. It will tackle a number of situations by exploring the main components of Goal Programming (GP), a fundamental topic within multi objective optimisation and will include goals, aspiration levels, achievement functions, deviation variables and priority sequence.

Description

This course will tackle a number of situations by exploring the main components of Goal Programming (GP), a fundamental component of multiobjective optimisation and will include goals, aspiration levels, achievement functions, deviation variables and priority sequence. It will look at a number of situations using multi-criteria decision
analysis (MCDA) methods which support the decision-maker in their unique and personal decision process. MCDA methods provide stepping-stones and techniques for finding a compromising solution.

We will also show how an investment portfolio problem with single and/or multiple goals within a priority level is formulated to then lead to the optimal number of shares purchased. Delegates will identify the case of decomposing the original problem into smaller pieces as an effective solution strategy; to this end, the GP problem will be explored as a series of linear programs. Finally, delegates will be given the chance to work on other advanced optimisation problems with multiple objectives (e.g.,
allocating resources to different educational/research amenities, specifying the optimal number of incoming students to different educational master programmes of
a department) using MS Excel and Python.

2023 Topics

This training seminar covers a fundamental topic within the multi-objective optimisation, named Goal Programming (GP). People make decisions both in their professional and private lives. A manager in a company, for example, may need to evaluate suppliers and develop partnerships with the best ones. Decision problems are often complex as they usually involve several criteria. To build long-term relationships and make sustainable and environmentally friendly decisions, companies consider multiple criteria in their decision process. Most of the time, there is no one, perfect option available to suit all the criteria: an ‘ideal’ option does not usually exist, and therefore a compromise must be found. Multi-criteria decision analysis (MCDA) methods have been developed to support the decision-maker in their unique and personal decision process. MCDA methods provide stepping-stones and techniques for finding a compromising solution. One of its main theoretical streams concerns the multi-objective decision-making models which assume continuous solution spaces, try to determine optimal compromise solutions, and generally assume, that the problem to be solved can be modelled as a mathematical programming model.

 

We will explore the GP technique, by formulating and solving (either graphically or via the use of a popular software) problems with pre-emptive priorities. In particular, we will introduce the main components of GP, including goals, aspiration levels, achievement function, deviation variables, and priority sequence.


We will also show how an investment portfolio problem with single and/or multiple goals within a priority level is formulated to then lead to the optimal number of shares purchased. Delegates will identify the case of decomposing the original problem into smaller pieces as an effective solution strategy; to this end, the GP problem will be explored as a series of linear programs.

 

Finally, delegates will be given the chance to work on other advanced optimisation problems with multiple objectives (e.g., allocating resources to different educational/research amenities, specifying the optimal number of incoming students to different educational master programmes of a department) using MS Excel and Python.

2023 Course goals and outcomes

Identify the main components of a goal programming (mathematical) model;

Formulate a basic goal programming model with a single goal within a priority level;

Formulate a more complex goal programming model with multiple goals within the same priority level;

Solve various goal programming models with 2 decision variables using the graphical procedure;

Solve goal programming models with more than 2 decision variables using an MS Excel environment as well as the powerful Python programming language;

2023 Audience

The course is designed to address the needs of individuals wishing to work in the industry and/or being involved in research related to management science, operational research, and modelling analytics. Potential delegates could be undergraduate and postgraduate students/researchers, as well as early-career modelling analysts.

2023 Online Platform and Software Requirements

Computer with video camera, speakers and microphone
Stable internet connection
Mains power
MS Excel (emphasis on the add-in option tool "Excel Solver") - Google
Colaboratory (Gmail account only required by each of the participants to then start
using this environment and run there the PuLP library)
Zoom - This course can only be accessed via the desktop version of Zoom, so if necessary please submit a request with your IT team to download Zoom in advance. Alternatively, use a personal computer to attend this course.       


2023 Prior knowledge

This training course expects attendees to have prior knowledge of the Linear Optimisation technique. It is also expected that they have previously used (at least to a beginner level) MS Excel and the PuLP library in Python programming language.

2023 Other related courses to continue your development...

 Foundations of OR: Data Envelopment Analysis, Foundations of OR: Statistical Methods in OR: Multivariate Models

2023 Employer benefits

Operations Research Analyst, Modelling Analyst, Management Consultant, Project Manager, Financial Analyst, Supply Chain Manager, Decision Support Analyst

2023 Learning objectives

Improved decision making by effectively analysing complex problems, evaluating trade-offs, and making data-driven decisions.

 

Enhanced operational efficiency by optimising various business processes including production planning, resource allocation, and scheduling.

 

Better resource management by effectively allocating and utilising resources.

 

Improved performance measurement by providing a framework for measuring and evaluating performance against multiple objectives.

 

Enhanced problem-solving skills by equipping employees with a structured problem-solving methodology that considers conflicting objectives and constraints.

Similar courses

This practical course will help you to improve project performance, ensure outcomes are met and enable resources to be used efficiently, using Soft Systems Methodology (SSM), the best-known problem structuring method.

More Information

While many analysts will use Microsoft Excel daily few use VBA (Visual Basic for Applications). This course will provide delegates with the skills to utilise VBA and achieve the efficiency in Excel modelling necessary for professional analytics practice. 

More Information

Develop your understanding of Operational Research (OR), its processes and how they are used to address real world problems. Address the modelling issues you may encounter throughout your career. 

More Information

Develop your understanding of Operational Research (OR), its processes and how they are used to address real world problems. Address the modelling issues you may encounter throughout your career. 

More Information

An introductory course to equip you with a tool bag of core quantitative forecasting techniques that are adaptable to almost any organisational setting. Appreciate the qualitative methods of forecasting by making use of expert opinion, judgement and scenarios.

More Information

This course is for those conducting modelling and analysis using Microsoft Excel and are now looking to expand their knowledge. In this course you will learn tips and shortcuts to aid you in achieving efficient and effective analysis while improving your proficiency in Excel. 

More Information

This course will provide you with a systematic introduction to the nature and advanced functionality of Microsoft Excel. It will arm you with the skills needed for planning, formatting and formulas that will take your proficiency to an improved level. 

More Information

An introductory course to equip you with a tool bag of core quantitative forecasting techniques that are adaptable to almost any organisational setting. Appreciate the qualitative methods of forecasting by making use of expert opinion, judgement and scenarios.

More Information

Gain an understanding of the ideas and concepts of efficiency assessment of organisations and how Data Envelopment Analysis (DEA) models can be formulated and applied using Excel Solver.

More Information

This course offers a set of frameworks and methods based on behavioural science to support Operational Research (OR) Practice. The course can be useful for developing behavioural OR models, understanding behavioural issues with the implementation of OR models and the behavioural aspects of interventions.

More Information

Overview An introduction to Soft OR and the methods for working with strategic and complex problem situations and structuring qualitative data.

More Information

Develop a practical understanding of System Dynamics qualitative and quantitative methods. 

More Information

Develop a practical understanding of System Dynamics qualitative and quantitative methods. 

More Information

A practical course applying sampling and regression analysis to ‘real world’ case study data and interpreting the results. Learn when and how to use these techniques to address ‘real world’ problems such as quality control.

More Information

A practical course applying sampling and regression analysis to ‘real world’ case study data and interpreting the results. Learn when and how to use these techniques to address ‘real world’ problems such as quality control.

More Information

Gain an understanding of the wide spectrum of skills used in Operational Research, including: problem structuring; data collection; analytics; modelling; and simulation. Learn and understand a typical problem cycle

More Information

A practical introduction to the most used methodologies in OR, linear programming and optimisation.

More Information

A practical course using multivariate statistical techniques to gain greater insights from your analysis.

More Information

A practical course using multivariate statistical techniques to gain greater insights from your analysis.

More Information

Explore the operational research methods and models available for supporting strategy. You will be able to develop your skills in a full session in scenario planning. This course better equips you to apply OR principles for strategic planning activities.

More Information

Improve your personal productivity for generating rapid and insightful results from large data sets using R.

More Information

This course is a natural follow on for delegates who have completed the Foundation course in forecasting and now wish to develop their toolbox further with Autoregressive Integrated Moving Average (ARIMA).

More Information

A practical introduction to simulation modelling, allowing you to understand the benefits of simulation and when to use it.

More Information

This two-day workshop will provide the state-of-the-art on the use of big data to improve service operations and will introduce several useful big data and machine learning techniques to help analysts, managers, and other stakeholders enhance operational performance.

More Information

A practical introduction to simulation modelling, allowing you to understand the benefits of simulation and when to use it.

More Information

Gain the theoretical and practical understanding on how to process and model time series data in your analytical and forecasting workflows using R programming language. This course will provide you with essential knowledge to allow wrangling, processing, analysis and forecasting of time series data in the R programming language.

More Information