Course Title  Quantitative Methods of Decision Making  
Lecturer  Karapet Shahinyan  
Institution  The Pskov Branch of S.Petersburg Academy for Engineering and Economics  
Country  Russia 
The purpose
of studying decisionanalysis techniques is to be able to represent
realworld problems using models that can be analyzed by means of
quantitative methods to gain insight and understanding. Decision Analysis Procedures and Modeling (first
semester) Theoretical part 1. Fundamentals of Decision Making
(DM) Introduction. Types of decisions. Types of DM environments.
Decision making: normative model. The six steps in DM. The structure of
“unstructured” decisions. DM under uncertainty. Frameworks for shorter and
longer term DM. Using the computer to solve DM problems. Key concepts.
2. An Introduction to
Models Models. Different types of models and their uses. The
modeling cycle. Model building steps. Modeling of a “realworld” problem.
Data and models. Creating a model. Computers and modeling. Key
concepts. 3.
Introduction to Quantitative Analysis (QA)
Introduction.
What is QA? The QA approach. Possible problems in the QA approach.
Behavioral and management considerations in implementation of QA.
Development of QA within an organization. QA and MIS. The use of
microcomputers in QA. Key concepts. 4. Constrained Optimization
Models Introduction. Mathematical formulation and interpretation.
Decision variables, parameters, constants and data. Applications of
constrained optimization models. Multiple objectives. Constrained versus
unconstrained optimization. Why constraints are imposed. Key concepts.
5. Nonlinearity, Computational Considerations and
Suboptimization Illustrating the general nonlinear model. Unbounded and
unfeasible problems. Problem complexity and computational considerations.
Suboptimization. Key concepts. 6. A Basic Model for Decisions Under
Uncertainty Introduction. The elements of a decision problem.
Quantifying the likelihood of an uncertain event. Criterion for evaluating
alternatives. Risk analysis. Business risk assessment. Risk control. Key
concepts. 7. Diagnosis and System Thinking
Introduction. System concepts. Organizational systems.
Systems thinking. Diagnosis. The nature of problems. Soft systems
analysis. Hard systems analysis. Graphical techniques. The systems
approach to DM. Key concepts. Practical part Exercises:
examining a decision, crisis in the housing market – a soft system
analysis. Formulation of constrained optimization models. Productmix
examples. A Blanding example. Multiperiod inventory examples.
Capitalbudgeting problems. Mediaplanning examples. Stochastic inventory
models. Queuing models. The Methods of Decision Theory (second
semester) Theoretical part 1. Forecasting Types of forecasts. Time Series (TS) models and the
components. TS using trend projection. TS with trend and seasonal
components. Forecasting using smoothing methods. Forecasting using
regression models. Causal forecasting methods. The Delphi method.
Forecasting accuracy. Using a computer to forecast.
2. Linear Programming: Graphical
Methods Introduction. Formulating Linear Programming (LP) problems.
Graphical solution to a LP. Solving minimization problems. A few special
issues in LP. Graphical Sensitivity analysis. 3. LP. The Simplex
Method Introduction. Simplex solution procedures. Surplus and
artificial variables. Solving the minimization problems. The Dual in LP.
Special cases in using the simplex method. Solving LP by
computer. 4. Decision Trees and Utility
Theory Introduction. Decision trees. Utility theory. Three classes
of decision problems. Bayesian analysis. Sensitivity analysis. Utilities
and decisions under risk. Decision trees: incorporating new information.
Sequential decisions. 5. Game Theory Introduction to the language of games. Pure strategy games.
The minimax criterion. Mixed strategy games.
Dominance. 6. Markov Analysis Introduction. States and state probability. The matrix of
transition probabilities. Equilibrium conditions. Solving Markov analysis
problems by computer. 7. Multicriterion Decision
Problems Goal programming: formulation and graphical solution.
Solving more complex problems. The Analytic Hierarchy Process (AHP).
Establishing priorities using the AHP. Using the AHP to develop an overall
priority ranking. Using expert choice to implement the
AHP. 8. Quantitative Methods and Computerbased Information
Systems Decision Support Systems (DSS): an overview. Applications of
DSS. The impact of PC. Expert systems. 9. Simulation Introduction. Advantages and disadvantages of simulation.
Monte Carlo simulation. The role of computers in
simulation. Practical part LP
applications: marketing, finance and transportation. Forecasting sales.
Decision trees and expected monetary value. Markov analysis: A grocery
store example. Multicriteria DA: production scheduling. Readings A.
Mandatory Jennings D. and Wattam S., Decision Making, Prentice Hall,
England, 1998 Curvin G. and Slater R., Quantitative methods for Business
Decisions, Tomson, China, 2000 Robert T. Clemen, Making Hard Decisions. An Introduction to Decision Analysis, 1995
B. Recommended Robson M., Problem Solving in Groups, Gower, England,
1998 Gorden G., Israel P, Sanford C, Quantitative Decision Making
for Business, Prentice hall, 1990 Scott P., The Psychology of Judgment and Decision Making,
McGraw Hill, New York, 1993
