Ce cours apparaît dans les formations suivantes :
• IÉSEG > IESEG Degree - Programme Grande École > Bachelor 3 > 1,50 ECTS

Pré requis

Strong background in applied mathematics including calculus and linear algebra.
Strong background in probability and statistics.
Knowledge in algorithms will be helpful

Objectifs du cours

At the end of the course, the student should be able to:
- understand basic theoretical principles in optimization;
- understand formulation of optimization models;
- understand solution methods in optimization;
- understand methods of sensitivity analysis and post processing of results
- apply optimization techniques to a wide range of business problems
- implement practical cases, by using EXCEL SOLVER

Contenu du cours

Linear Programming (LP) and allocation of resources
Maximization then Minimization problems
Graphical LP Minimization and Maximization solution
Linear Programming-Simplex Method for Maximizing
Linear Programming-Big-M Method for Optimizing
Sensitivity Analysis: Changes in Objective Function
Duality

Modalités d'enseignement

Organisation du cours

Méthodes pédagogiques

• Interactive class
• Project work

Évaluation

1. Presence/Participation
2. Final Exam
3. Final project (Project 1 = 15% and Project 2 = 25%)

Bibliographie

• Mayne Winston (2004) "Operations Research: Applications and Algorithms", 4th Edition, Brooks/Cole Cengage Learning -

• Moore, J.H., L.R. Weatherford (2001) "Decision Modeling with Microsoft Excel", 6th Edition, Upper Saddle River, Prentice Hall -

• Winston, W.L., S.C. Albright (2001) "Practical Management Science: Spreadsheet Modeling and Applications", 2nd Edition, Pacific Grove, Duxbury Press -

• Taylor Bernard W. (2007) "Introduction to management Sicence", 9th Edition, Pearson Prentice hall -

• Anderson, R.A., Sweeny, D.J. (2006) "Applied Production and Operations Management" -