Computational Methods for Logistics

Learning Outcomes

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• utilise written communication.
• structure and solve problems
• apply quantitative decision making methods

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• modelling and solving decision problems.
• the appropriate optimisation method to solve a business problem;
• the state-of-the-art optimisation methods;

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• combine and enhance existing methods to achieve better performance;
• critically evaluate business processes for areas of improvement.
• design and improve your own methods;

No prior knowledge on optimisation or programming is required, although recommended. The topics to be

covered include, but are not limited to

• Basics of optimisation: what is a problem, what is an algorithm, introduction to writing computer programs, algorithm analysis and time complexity, introduction to optimisation software;
• Standard problems in logistics and their applications: including knapsack and bin packing problems, cutting stock problems, facility location and clustering problems, inventory and lot sizing problems, network flow problems; -Methods of optimisation: optimal and heuristic approaches, use of optimisation software, programming your own algorithms;
• Case study on vehicle routing algorithms: constructive heuristics, approximation algorithms, local search, metaheuristics and population based heuristics;
• Case studies in organisation of logistics, wider context and issues.

Teaching and learning methods

Learning activities include:

• Lectures
• Use of online materials
• An individual assignment
• In-class case study / problem solving activities
• Private study.

Resources & Reading list

Textbooks

Winston, W (2004). Operations Research: Applications and Algorithms. Brooks/Cole,.

Michalewicz, Z. and Fogel, D. B (2000). How to Solve it: Modern Heuristics. Berlin: Springer Verlag.

Talbi, E., Metaheuristics (2009). From Design to Implementation. New Jersey: Wiley.

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.