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) To understand the operational research approach.

(LO2) To be able to apply standard methods of operational research to a wide range of real-world problems as well as to problems in other areas of mathematics.

(LO3) To understand the advantages and disadvantages of particular operational research methods.

(LO4) To be able to derive methods and modify them to model real-world problems.

(LO5) To understand and be able to derive and apply the methods of sensitivity analysis.

(LO6) To understand the importance of sensitivity analysis.

(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.