Applying the most suitable methodologies and respective algorithms to solve particular linear programming cases. Applying appropriate tools for solving linear programming problems. Solving linear and dynamic programming problems Modeling mathematical programming problems. Understanding and problem classification into linear programming, integer programming, etc. Understanding the basic concepts of mathematical modelling. Understanding the basic concepts of mathematical programming. Module Objective (preferably expressed in terms of learning outcomes and competences):.In class teaching, homework exercises, laboratory exercises, external invited speakers. Probability and Statistics with Reliability, Queuing, and Computer Science Applications Practical Introduction to Management Science Tools for Thinking: Modelling in Management ScienceĪ. Model Building in Mathematical ProgrammingĪn Introduction to Management Science: Quantitative Approaches to Decision Makingĭ.R.
Introduction to Operations Research Techniques Operations Research: Principles and Practice Operations Research: Applications and Algorithms Lieberman, Introduction to Operations Research, 8th edition, Mc Graw-Hill International Edition Elements of stochastic modelling.īasic Reference: F.S. Network optimization, graphs and networks, the shortest path, minimum spanning trees, maximum flow problem, minimal cost flow problem. Introduction to operations research, linear programming, modelling, Simplex method, Big M method, duality, sensitivity analysis. A wide range of OR methodologies is approached with emphasis in applications. The objective of this course is to provide students with the appropriate basic tools of Operations Research for the decision support in management and operations systems. Name of lecturer / lecturers : Dr Agapios Platis.