The aims of the module are to develop an understanding of how mathematical modelling and operational research techniques are applied to real-world problems and to gain an understanding of linear and convex programming, multi-objective problems, inventory control and sensitivity analysis.

(LO1) Accurately use a range of routine methods for optimisation and sensitivity analysis in a variety of mathematical contexts.

(LO2) Explain the practical significance of optimisation and sensitivity analysis in real-world contexts.

(LO3) Analyse the advantages and disadvantages of applying a particular operational research method in a specific situation or in comparison to another method.

(LO4) Adapt, combine and apply operational research methods and their derivations to unfamiliar mathematical or real-world settings.

(S1) Adaptability

(S2) Problem solving skills

(S3) Numeracy

(S4) Self-management readiness to accept responsibility (i.e. leadership), flexibility, resilience, self-starting, initiative, integrity, willingness to take risks, appropriate assertiveness, time management, readiness to improve own performance based on feedback/reflective learning

Operational research methodology.

Linear programming: solvability, simplex method, artificial variables, duality, degeneracy, elementary sensitivity analysis.

Transportation Problem: NWCR, Least-cost method, MODI methods, degeneracy, unbalanced problems.

Convex programming: Lagrange multipliers, duality, numerical techniques (gradient methods, penalty functions, etc.).

Multiobjective problems: dominance, non-inferior set, weighting method, other approaches.

Inventory theory: fixed-order-quantity models, sensitivity analysis, production-inventory systems.