Learning Outcomes
• The knowledge of the basic mathematical programming (Linear and Non-linear) concepts and methods.
• The ability to model a real-world operational problem by the development of the appropriate mathematical programming model.
• The ability to solve mathematical programming models by employing the appropriate operations research methodologies and algorithms.
• The ability to handle data and solve mathematical programming models using computer software.
• The ability to perform sensitivity/"what-if" analyses on the results of operations research problems.
• The ability to interpret the results of an operations research problem's solution.
Course Content (Syllabus)
Introduction to optimization, mathematical programming models, variables, objective function parameters, constraints. Linear programming theory, graphical solution, Simplex method, revised Simplex method, dual theory, dual Simplex method and sensitivity analysis. Transportation algorithm, assignment algorithm, transshipment algorithm.Linear programming problem solving using computer software.Integer programming.Non-linear programming.Classic methods for solving non-linear models (with or without constraints), Karush-Kuhn-Tucker (KKT) conditions. Non-linear programming applications.
Keywords
Linear Programming, Dual Theory, Integer Programming, Non-linear Programming
Course Bibliography (Eudoxus)
Λουκάκης, Μ., Γραμμικός Προγραμματισμός, Εκδόσεις Σοφία, 3η έκδοση, 2010
Additional bibliography for study
1. Hillier, F. S. and Lieberman, G. J., Introduction to Operations Research, McGraw-Hill, 9th ed., 2010.
2. Taha, H. A., Operations Research: An Introduction, Pearson Education, 9th ed., 2010.