This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Undergraduate Handbook 2025-2026 (archived)
Module MATH3141: OPERATIONS RESEARCH III
Department: Mathematical Sciences
MATH3141: OPERATIONS RESEARCH III
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Open | 3 | 20 | Available in 2025/2026 | Durham |
Pre-requisites
- Calculus I (Maths Hons) (MATH1081) or Calculus I (MATH1061) AND Probability I (MATH1597) AND Linear Algebra I (Maths Hons) (MATH1091) or Linear Algebra I (MATH1071).
Corequisites
- None.
Excluded Combinations
- 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 stochasitc dynamic programming.
- Optimisation in Markov chains and Markov decision processes.
- Further topics chosen from: intenetwork optimisation problems (transportation problem, shortest path problem, maximal flow problem, etc.), 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; optimisation on network models; deterministic and stochastic dynamic programming; Markov decision processes, including policy-improvement algorithms.
Subject-specific Skills
- Specialised mathematical skills which can be used with minimal guidance in the following areas: 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 | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures | 42 | 2 per week in Michaelmas and Epiphany; 2 in Easter | 1 Hour | 42 | |
| Problems Classes | 8 | Fortnightly in Michaelmas and Epiphany | 1 Hour | 8 | |
| Preparation and Reading | 150 | ||||
| Total | 200 | ||||
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Examination | 100% | Written examination | 3 Hours | 100% |
Formative Assessment
Eight assignments to be submitted.
Students who do not attend monitored activities shown under Teaching Methods
and Learning Hours, or who fail to complete the summative or formative
assessment(s) specified above, may be subject to the Academic Progress
procedures defined in the University's General Regulation V, and may be
required to leave the University.