• Probability and Linear Algebra

• None

• None

• To introduce some of the central mathematical models and methods of operations research.

• 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.

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:

• 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.

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.

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