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The University of Southampton

MANG6313 Computational Methods for Logistics

Module Overview

The overreaching goal of this module is to develop your quantitative problem solving skills by improving your algorithmic thinking. The module emphasises the versatility of the methods, and encourages you to apply these techniques to diverse areas of business, in order to reorient your thinking processes towards a perspective of continuous improvement of every process.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

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

  • the state-of-the-art optimisation methods;
  • selecting an appropriate optimisation method to solve a business problem;
  • modeling to solve decision problems.
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;
  • design and improve your own methods;
  • critically evaluate business processes for areas of improvement.
Transferable and Generic Skills

Having successfully completed this module you will be able to:

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


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.

Learning and Teaching

Teaching and learning methods

Learning activities include: • Lectures • Use of on-line materials • An individual assignment • In-class case study/problem solving activities • Private study

Independent Study51
Total study time75

Resources & Reading list

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

Michalewicz, Z. and Fogel, D. B (2000). How to Solve It: Modern Heuristics. 

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



Class Test


MethodPercentage contribution
Examination  (2 hours) 100%


MethodPercentage contribution
Examination  (2 hours) 100%


MethodPercentage contribution
Examination  (2 hours) 100%

Repeat Information

Repeat type: Internal & External


Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:


Recommended texts for this module may be available in limited supply in the University Library and students may wish to purchase the mandatory/additional reading text as appropriate.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at

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