Postgraduate Programme and Module Handbook 2023-2024 (archived)
Module MATH30820: Operations Research
Department: Mathematical Sciences
MATH30820: Operations Research
| Type | Tied | Level | 3 | Credits | 20 | Availability | Available in 2023/24 | Module Cap | None. |
|---|
| Tied to | G1K509 |
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Prerequisites
- Probability and Linear Algebra
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To introduce some of the central mathematical models and methods of operations research.
Content
- Introduction to Operations Research.
- Linear programming: simplex algorithm, duality, post-optimal analysis.
- Deterministic and stochastic dynamic programming.
- Optimisation in Markov chains and Markov decision processes.
- Further topics chosen from: network optimisation problems (transportation problem, shortest path problem, maximal flow problem, ect.) reinforcement learning, inventory theory.
Learning Outcomes
Subject-specific Knowledge:
- Ability to solve novel and/or complex problems in Operations Research.
- Systematic and coherent understanding of the theoretical mathematics underlying Operations Research.
- A coherent body of knowledge, demonstrated through one or more of the following topic areas: linear programming and the simplex algorithm; duality and post-optimal analysis; optimsation on netweork models; deterministic and stochastic dynamic programming; Markov decision processes, including policy-improvement algorithms.
Subject-specific Skills:
- In addition students will have specialised mathematical skills in the following areas which can be used with minimal guidance: Modelling, Computation.
Key Skills:
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
- Assignments for self-study develop problem-solving skills and enable students to test and develop their knowledge and understanding.
- Formatively assessed assignments provide practice in the application of logic and high level of rigour as well as feedback for the students and the lecturer on students' progress.
- The end-of-year examination assesses the knowledge acquired and the ability to solve predictable and unpredictable problems.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures | 42 | 2 per week for 20 weeks and 2 in term 3 | 1 Hour | 42 | |
| Problems Classes | 8 | Four in each of terms 1 and 2 | 1 Hour | 8 | |
| Preperation and Reading | 150 | ||||
| Total | 200 |
Summative Assessment
| Component: Examination | Component Weighting: 100% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Written examination | 3 Hours | 100% | |
Formative Assessment:
Eight written or electronic assignments to be assessed and returned. Other assignments are set for self-study and complete solutions are made available to students.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University