Managerial Decision Making


Fall Semester
6 ECTS credits, Advanced Level

Course Outline

This course outline describes the course Managerial Decision Making. It has been organized into the following sections:

  1. Basic Information about the Course
  2. Aim of the course
  3. Planned learning activities and teaching methods
  4. Learning Outcomes
  5. Reading List
  6. Syllabus
  7. Course Assessment.

Basic Information about the Course

Prerequisites: None
Teaching Methods: The class meets once a week
Consultation Time/Tutorials: Wednesday 14.00-17.00

Course Objectives

The course introduces the student to the methodology of decision making, as well as to the major models used today. Decision making is one of the most important functions of management. The three major categories of models are covered: Linear and Integer Programming, Decision Analysis, and Simulation. In each unit, the student is exposed to a number of applications, and has the opportunity to apply his/her knowledge to a number of problems such as Transportation, Assignment and Network models. In addition to developing models, the student is exposed to a number of computer packages, most of them based on Excel, to use in order to solve the problems.

Planned Learning Activities and Teaching Methods

We cover the course material in lectures. Attending lectures is compulsory. This is the best way of being introduced to a topic. Self-study is a vital and significant part of studying for the course.

Learning Outcomes

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.

On completion of this course, students should be able to: to understand and formulate decision making problems, and to use the computer technology efficiently in order to make the best decision.

Reading List

Required Textbook
G.P.Prastacos, (2008), Managerial Decision Making Theory and Practice, Tsinghua University Press

Recommended Reading

N.Balakrishnan, B.Render, and R.M.Stair, Jr. (2013), Managerial Decision Modeling with Spreadsheets, Pearson Education Inc.
C.P.Bonini, W.H.Hausman and H.Bierman, (1997), Quantitative Analysis for Management, McGraw-Hill / Irwin
G.L.Nemhauser and L.A.Wolsey, (1999), Integer and Combinatorial Optimization, Wiley Interscience
W.L.Winston and S.C. Albright,(2002), Practical Management Science, South-Western College Pub.


Managerial Decision Making


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 Problems; 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 and Transportation problems; Logistics and Supply Chain problems; Investments problems; Human Resources problems.
Decision Analysis and Precision Tree: 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 and @Risk in Simulation: Introduction; Simulation of Queuing Systems; Simulation of an Inventory System; Analysis of Simulation Results.

Course Assessment

The following notes offer guidance on how you will be assessed for the course. The final grade will be based on homework, classroom participation, an individual essay, case studies and a final exam. The breakdown of the final grade will be approximately as follows:

20%      homework and classroom participation
30%      individual essay and group case studies
50%      final written exam