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   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 decision-analysis techniques is to be able to represent real-world 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 “real-world” 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 micro-computers 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. Non-linearity, 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. Product-mix examples. A Blanding example. Multi-period inventory examples. Capital-budgeting problems. Media-planning 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 Computer-based 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  

 

 



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