-Linear Programming: Decision problems. Formulating a decision problem into a linear programme. Solving Linear programming graphically. Infeasibility, unboundedness and redundant constraints.

Duality and Sensitivity Analysis in Linear Programming: Duality. Solving the dual using the solutions of the primal. Sensitivity analysis.

-Network Analysis: Network optimization problems. Representing the problem as a network. Formulating the problem as a linear programming. The maximum-flow problem. Fictitious nodes: solving transportation problems. Maximin objective function.

-Integer Programming and Goal Programming: Formulating an Integer linear programming (ILP). Solving an ILP. Goal programming: Target values and penalties. Formulating the goal programming.

Single Stage Decision Problems: Structuring decision problems. Solving decision problems. Taking account of attitude to risk. Some problems with expected utility theory.

-Multi-Stage Decision Problems: Multi-stage decision problems. The value of perfect information: expected value with perfect information, sensitivity analysis. The value of experimental information: Prior analysis, Revising prior probabilities, Expected value of experimental information, Sensitivity analysis.

-Decision Making Using Sample Information: Decision making with the proportion. Decision making using the normal distribution. Decision theory and traditional statistics.

-Markov process, Simulation.

-Activity Analysis.