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Course Content (Syllabus)

Managerial Decision Making

The aim of the course is to introduce the undergraduate students to the methodology of decision making, as well as in the major models used today.

Decision making is one of the most important functions of management. Today’s business environment is characterized by high competition, constant changes, extensive globalization, large availability of data and information, and the huge penetration of information and telecommunications technology. In this environment, decision making is increasingly based on the use and analysis of data, through the development of models and the use of user-friendly PC based computer packages.

The three major categories of models are covered: Linear and Integer Programming, and Decision Analysis.

In each unit, the student is exposed to several applications and can apply his/her knowledge to several problems and case studies. In addition to developing models, the student is exposed to several computer packages, most of them based on Excel, to use to solve the problems.

SYLLABUS

The Fundamentals of Operations Research: Introduction to management Science; The methodology of Decision Making; Models in Managerial Decision Making 

Linear Programming (LP): Introduction; Characteristics of LP Problems; Graphical solution of a LP problems; A Maximization Problem; a Minimization Problem; Problems General Formulation and Assumptions of LP problems

Sensitivity analysis in Linear Programming: Dual Prices in LP; Reduced costs in LP; Changes in the Objective Function’s Coefficients; Changes in the Right-Hand Sides (RHS) of the Constraints; Evaluation of a New Activity

Using Solver to Solve Linear Programming Problems: Introducing the model in Excel; Solving the Problem; Understanding and Analyzing the Solution – SOLVER Reports.

Integer Programming (IP): Introduction; Formulating IP Problems with Binary Variables; Formulating IP Problems; Solving IP problems; Solving Integer Programming Problems with SOLVER.

Implementing Management Science in Practice: Marketing and Sales problems; Production and Inventory problems; Networks problems; Logistics and Supply Chain problems; Investments problems; Human Resources problems.

Implementing Management Science in Practice: Transportation, Transshipment, and Assignment problem

Heuristics Methods for hard Combinatorial Optimization Problems: Traveling Salesman Problem, Capacitated Minimum spanning tree problem, Vehicle Routing Problems.

Decision Analysis: Introduction; Criteria for Making Decision under Uncertainly; The Expected Value of Perfect Information; Decision Tree; Calculating the Risk Profile a Strategy; Sensitivity Analysis; Using Precision Tree to Solve Decision Analysis Problems.

Simulation: Introduction; Implementation of Simulation under Conditions of Uncertainty

Using Excel, Extend and @Risk in Simulation: Introduction; Simulation of Queuing Systems; Simulation of an Inventory System; Analysis of Simulation Results.