|Course Title||Quantitative Methods of Decision Making|
|Institution||The Pskov Branch of S.-Petersburg Academy for Engineering and Economics|
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)
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.
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)
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.
Introduction. Advantages and disadvantages of simulation. Monte Carlo simulation. The role of computers in simulation.
LP applications: marketing, finance and transportation. Forecasting sales. Decision trees and expected monetary value. Markov analysis: A grocery store example. Multicriteria DA: production scheduling.
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
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