This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Undergraduate Handbook 2016-2017 (archived)
Module MATH4251: OPERATIONS RESEARCH IV
Department: Mathematical Sciences
MATH4251: OPERATIONS RESEARCH IV
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Open | 4 | 20 | Not available in 2016/2017 | Durham |
Pre-requisites
- Core Mathematics A (MATH 1012) AND a minimum of 40 credits of Mathematics modules at Level 3.
Corequisites
- None.
Excluded Combinations
- Operations Research III (MATH3141)
Aims
- To introduce some of the central mathematical models and methods of operations research.
Content
- Introduction to Operations Research.
- Linear programming: primal/dual simplex algorithm, sensitivity analysis, transportation algorithm.
- Optimisation on networks.
- Introduction to Markov chains.
- Inventory theory.
- Markov decision processes.
- Further topics chosen from: integer programming, iterative non linear programming, dynamic programming.
- Reading material on a topic related to: integer programming, queueing theory.
Learning Outcomes
Subject-specific Knowledge
- By the end of the module students will: be able to solve novel and/or complex problems in Operations Research.
- have a systematic and coherent understanding of theoretical mathematics in the fields Operations Research.
- have acquired coherent body of knowledge of these subjects demonstrated through one or more of the following topic areas: Linear programming and the simplex algorithm.
- Duality and sensitivity analysis for L.P.
- Optimisation on network models.
- Brief treatment of finite state Markov chains.
- Deterministic and probabilistic dynamic programming.
- Markov decision processes, including policy-improvement algorithms.
- Inventory Theorem.
- Knowledge and understanding of a topic in the following areas: integer programming, queueing theory.
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 | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures | 40 | 2 per week for 19 weeks and 2 in term 3 | 1 Hour | 40 | |
| Preparation and Reading | 160 | ||||
| Total | 200 |
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Examination | 100% | three hour written examination | 100% |
Formative Assessment
Four written assignments to be assessed and returned. Other assignments are set for self-study and complete solutions are made available to students.
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.