BU275 Lecture Notes - Lecture 1: Linear Programming, Integer Programming, Decision Analysis
Document Summary
The problem is identified: design phase. The model is validated and evaluation criteria are set: choice phase. Intelligence phase pitfalls: don"t make type iii error solving the wrong problem. Computer graphics advances: complement math models using more iconic and analog models (visual simulation) Ranked in order of usage: simulation, statistics and forecasting, linear programming, decision analysis, queuing analysis, integer programming, network methods. Characteristics of lps: objective function and constraints are linear functions, constraint types are <=, =, >, variables can assume any fractional value, decision variables are non negative, maximize or minimize a single objective. If there are multiple objectives, turn one of them into a constraint. Methods of solution: graphical, simplex, interior point methods. Step 1 determine the feasible region: plot a constraint line for each constraint, for each constraint line, determine the feasible side. Identify the set of solutions that satisfy all the constraints.
