Optimisation and (Meta-) Heuristics

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

Description

  • Understand the potential and limitations of optimisation methodologies 
  • Become aware of the scope of applications of optimisation 
  • Be able to apply linear programming in your own work 
  • Be able to solve a linear program using Excel Solver or another program 
  • Be able to interpret a solution to a linear program 
  • Become aware of alternative optimisation methodologies and understand when to use which approach 
  • Become aware of heuristic approaches to optimisation and their advantages and limitations

Learning objectives

  • Learn about the potential and limitations of optimisation methodologies 
  • Understand the scope of applications of optimisation 
  • Apply linear programming by solving a problem using Excel Solver 
  • Interpret the solution to a linear program 
  • Build your knowledge of alternative optimisation methodologies and understand when to apply different approaches 
  • Learn about heuristic approaches to optimisation and their advantages and limitations. 

Topics

  • The application of linear programming in different sectors 
  • Decision variables, objective function, and constraints 
  • Graphical illustrations of linear programs 
  • Using Excel Solver to solve and interpret linear programs 
  • Sensitivity analysis 
  • Common types of linear programs, including transportation 
  • Other types of optimisation problems 
  • Introduction to metaheursitic approaches as alternatives to exact methods 

Audience

The course is designed for those with limited or no prior experience in optimisation. The hands-on experience with optimisation provided during the course will enable delegates to recognise potential of optimisation and start applying it in their own work.

Course format

  • PowerPoint presentation to introduce the topics
  • Practical sessions using Minitab software and the Microsoft Excel analysis tool pack
  • Group discussion/work to explore the topics in more detail
  • Bring questions from your own work to embed your learning
  • Supporting resource pack available to use following the course

Related courses

  • Data Envelopment Analysis 
  • Statistical Methods in OR: Multivariate Statistics 
  • Follow on to Forecasting: ARIMA modelling for forecasting 

Want to run this course in-house? Enquire about running this course in-house

Meet the tutor

Victor Podinovski

Victor is Professor of Operational Research at School of Business and Economics of Loughborough University. Victor’s background is in applied mathematics and operational research, and he has previously worked as Professor of Operational Research at Warwick Business School.

Victor’s academic interests are in the areas of optimisation, decision making and efficiency analysis of firms. He has published extensively on these topics in many international journals, including top European and US journals.

Recently Victor was a principal co-investigator of a large EU-funded research project aimed at the development of a range of quantitative tools to help EU decision makers to simulate policy scenarios in the field of common agricultural policy.

He is a co-editor of Journal of Productivity Analysis and co-director of the Centre for Performance and Productivity Analysis at Loughborough University.