Postgraduate Diploma in Operations Research

Purpose

The purpose of this qualification is to equip qualifying learners with a range of technical and scientific skills in Operations Research to pursue a career in Industry and Commerce.

Upon completion of the qualification, learners will be able to

• Engage in critical and creative thinking and have a comprehensive and systematic knowledge base with a coherent and critical understanding of the principles, theories and research methods of Operations Research.
• Apply appropriate operations research modelling skills and techniques to complex and real world problems and operate effectively in complex but ill-defined contexts.
• Reflect on learning from individual experience and academic disciplines in relation to scientific, technological and economic realities, locally and globally.
• Show efficient and effective information retrieval and processing skills and effectively present and communicate academic work.
• Manage learning tasks autonomously, professionally and ethically and display a high level of intellectual independence.

Rationale

Operational Research (OR) is the use of advanced analytical techniques to improve decision making. Every customer always expects high-quality products and/or services, hence every organisation whether public or private, needs to provide them as effectively and efficiently as possible.

OR deals with decisions involved in planning the efficient allocation of scarce resources such as material, skilled workers, machines, money and time in order to achieve stated goals and objectives under conditions of uncertainty and over a specific time span. Therefore efficient allocation of resources is vital and may involve establishing policies, designing processes or relocating assets.

OR professionals strive to construct models that accurately represent and forecast relationships, they may use statistical analysis. The qualification aims to develop individuals who will be able to collect, synthesise and work with information and use information systems as a source of data and a means of implementing solutions. Learners will acquire skills to find an efficient allocation of resources which may employ optimisation techniques to deal with huge numbers of inter-related matters and constraints to develop solutions.

Recognition of Prior Learning (RPL)

Applicants who wish to be considered for RPL must apply in in accordance with the principles laid down in the institution's RPL Policy.

RPL may be used to grant admission to the qualification to learners who do not meet the minimum requirements for entry. It may also be used to grant advanced standing to learners through the granting of module exemptions.

Entry Requirements

• A relevant Bachelor's Degree, Level 7.
• A relevant Advanced Diploma, Level 7.

The qualification comprises 5 compulsory and one elective module at Level 8 totalling 120 Credits.

Compulsory Modules, Level 8, 96 Credits

• Research Support Tools, 12 Credits.
• Stochastic Modelling, 24 Credits.
• Introduction to Heuristics, 24 Credits.
• Forecasting, 12 Credits.
• Financial Modelling 1, 24 Credits.

Elective Modules, Level 8, 24 Credits (choose one)

• Discrete Optimisation.
• Financial Modelling 2.

• Engage in critical and creative thinking and show a comprehensive and systematic knowledge base, having a coherent and critical understanding of the principles, theories and research methods of Operations Research.
• Apply appropriate operations research modelling skills and techniques to complex and real world problems and operate effectively in complex but ill-defined contexts.
• Reflect on learning from individual experience and academic disciplines in relation to scientific, technological and economic realities, locally and globally.
• Show efficient and effective information retrieval and processing skills and the ability to effectively present and communicate academic work.
• Manage learning tasks professionally, ethically and independently showing a high level of intellectual independence.

The following Associated Assessment Criteria are applied in an integrated way to assess competence against the Exit Level Outcomes:

• Apply technical writing skills to present technical reports correctly and accurately.
• Utilise the theory and techniques of forecasting models to identify the most suited forecasting model to predict a certain outcome in a data set and solve real world problems.
• Demonstrate understanding and knowledge of the introductory theories of stochastic modelling and its applications in Markov chains, counting processes, queues and reliability theory in order to acquire background knowledge necessary for constructing models and designing and analysing algorithms in the fields of Operations Research and Financial Modelling.
• Demonstrate understanding of concepts and intrinsic limitations of meta-heuristical methods for optimisation problems.
• Demonstrate understanding of statistical concepts and the ability to apply the experimental methodology of static and dynamic simulation.
• Demonstrate knowledge and understanding of and the ability to apply Artificial Neural Network (ANN) algorithms.
• Demonstrate knowledge and understanding of and the ability to apply genetic algorithms.
• Demonstrate knowledge and understanding of meta-heuristics inspired by phenomena in other fields, for example swarming (biology) and simulated annealing (physics) including hybrids thereof.
• Demonstrate understanding of the derivative investment environment with regard to the characteristics of derivatives, the modelling and pricing of derivatives and the use of futures and options in managing profits and reducing risks.
• Demonstrate understanding of the mathematical modelling of portfolio risk and financial price processes.
• Demonstrate understanding of the fundamental principles underlying the design of optimisation algorithms in order to construct mathematical programming models and solve them by using appropriate algorithms.
• Demonstrate understanding and technical ability in combinatorial optimisation and its applications in constructing models and in designing and analysing algorithms in the field of Operations Research.

Integrated Assessment

Formative assessment is done by means of activities in study guides, self-assessment questions in study guides and self-assessment assignments as well as written assignments that have to be submitted for assessment. Summative assessment is conducted by means of a three-hour examination per module. Learners are compelled to submit at least two assignments for each module.