Learning Outcomes
1. Understand how to translate a real-world problem, given in words (verbal description), into a mathematical model/formulation.
2. Identify and use the proper mathematical tools and methods that are needed to solve several classes of optimisation problems.
3. Use mathematical software (modelling/programming language) to solve the problems using operational research techniques.
4. Develop a report that describes the model and the solving technique, analyse the results and propose recommendations in language understandable to the decision-making processes from the totality of stakeholders.
Course Content (Syllabus)
Theory and Methods of Linear Programming (Simplex, Graphical Interpretation, Duality Theory, Polynomial Methods, Special Cases). Karush‐Kuhn‐Tucker Condition. Integer Programming: Formulation and Methods. Fundamental Algorithms for Combinatorial Optimization Problems (Network Flows, Matchings, Special Problems in Graphs). Stochastic Methods. Dynamic Programming: Formulation and Methods. Fundamentals of Nonlinear Programming. Applications of Operational Research Methods in Various Fields.
Keywords
Mathematical Programming, Stochastic Processes, Queuing Theory, Optimization.
Course Bibliography (Eudoxus)
Εισαγωγή στην Επιχειρησιακή Έρευνα. H. Taha.
Εισαγωγή στην Επιχειρησιακή Έρευνα, Ιωάννης Κολέτσος, Δημήτρης Στογιάννης.
Εισαγωγή στην Επιχειρησιακή Έρευνα, Hillier Frederick S., Lieberman Gerald J., (Αλέξανδρος Διαμαντίδης (επιμέλεια)).
Επιχειρησιακή Έρευνα, Παντελής Υψηλάντης.