Management Science: Optimisation Methods

The details
Essex Business School
Southend Campus
Postgraduate: Level 7
Thursday 03 October 2019
Saturday 14 December 2019
03 October 2019


Requisites for this module



Key module for

MSC N21612 International Logistics and Supply Chain Management

Module description

Management Science is an interdisciplinary field that employs and integrates methods of applied mathematics, economics, and statistics to help decision making in the management context. Together with the ever increasing availability of computational resources, the efficient use of quantitative methods of management science has become a vital instrument to achieve competitive advantage. For instance, efficient scheduling of flights between the many ports spread all over the world is facilitated by a combination of advanced mathematical and computational tools.

In this module, we focus on the methods of mathematical programming that express decision making problems as optimisation problems where quantities of interest, such as profit and cost, are maximised or minimised, satisfying organisational constraints. This module will provide the student with a deep understanding of standard optimisation models and techniques and will show how they can be used to identify and solve problems encountered by managers in practice. It will provide the student with skills to formulate organisational problems in the language of mathematics, select suitable techniques to solve them, and use specialised software programs to carry out these tasks in practice.

Module aims

This module broadly aims to develop the quantitative skills required to model and solve critical managerial problems by the methods of management science. Students will be able to identify relevant approaches that suit a particular problem of interest. The module will illustrate the use of quantitative methods at both strategic and operational levels. This module aims to equip students with a wide range of models and solution procedures of mathematical programming that are tailored to a set of archetypical optimisation problems. These include, but are not limited to: linear and nonlinear programming, integer programming, dynamic programming, and network models. For each problem type, relevant managerial decisions will be identified and discussed with a particular emphasis on policy implications.

The module aims to provide students with the following:

Appreciation of the competitive advantages that methods and tools of management science can provide to organisations, enabling them to make improved decisions.

Familiarity with different classes of problems and methods.

Analytical skills for representing decision making problems in the mathematical programming formulation, solving them, and analysing sesitivity to changes in organisational factors and conditions.

Knowledge of a wide range of problem prototypes and relevant business problems.

Academic background that is essential for solving optimisation problems in the domain of logistics and supply chain management.

Essential software skills for implementing, solving, and analysing optimisation problems.

Module learning outcomes

On successful completion of this module students should be able to:

Gain awareness about the increasing importance of quantitative methods in the competitive business environment.

Establish a critical understanding of different classes of problems in the business domain and a range of associated methods.

Develop analytical skills of mathematical programming, enabling to model, analyse, and solve challenging and complex problems of the modern business world.

Obtain the digital / technical fluency for implementing, solving, and analysing optimisation problems in practice.

Obtain a critical view on the selection of appropriate optimisation techniques for particular problems under consideration in the area of logistics and supply chain management.

Develop the ability to interpret theoretical solutions in a business context, particularly theough sensitivity analysis.

Module information

No additional information available.

Learning and teaching methods

The following learning and teaching methods will inform the pedagogic process of the course: Lectures; Seminars * The lectures will introduce the key theoretical concepts of management science, mathematical programming, and solution approaches. Mathematical foundations for representing, solving, and analysing optimisation problems will be established through the lectures. The lectures will also equip students with the critical understanding of the context and scopes of specific models and techniques, directing them to the most appropriate approach demanded by a given managerial problem. * The seminars will focus on real-world applications of the theoretical approaches taught in the lectures. For each subject, relevant managerial problems will be introduced, modelled, analysed, and discussed. Lab sessions will help students to acquire the software skills for implementing and solving optimisation problems.


  • Vanderbei, Robert J. (no date) Linear programming : foundations and extensions / Robert J. Vanderbei..

The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework   Group Report    50% 
Written Exam  In Class Test 1    25% 
Written Exam  In Class Test 2    25% 
Exam  1440 minutes during Summer (Main Period) (Main) 

Overall assessment

Coursework Exam
40% 60%


Coursework Exam
40% 60%
Module supervisor and teaching staff
Dr Chang-Hun Lee, email:
Chang-Hun Lee



External examiner

Mr Michael Paul Bernon
Cranfield University
Senior Lecturer and Executive Development Director
Available via Moodle
Of 25 hours, 22 (88%) hours available to students:
3 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information
Essex Business School

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