Syllabus for DSC 321: Quantitative Analysis of Business Problems Course Goals
Course Goals
1.
To present concepts that are useful for problem solving.
2.
To demonstrate the role of mathematical modeling in the problem solving process. This
includes the use of abstraction and simplification to gain a better understanding of a
system or process.
3.
To show the role and limitations of the language of mathematics as a representational
form for a process or system.
4.
To demonstrate that there are a number of different equally valid models that might be
used to represent a particular system and that the model form chosen is influenced by the
needs and perspective of the modeler.
5.
To present the students with the opportunity to work together on group projects to
demonstrate that we learn from each other as well as in the classroom or from the
"expert."
6.
To encourage the student to remember that the purpose of analysis is action and that the
goal is to change systems so that they better meet some defined need.
Topics
Time (Lectures)
Introduction to Decision Making and Management Science
1
An Introduction to Corporate Planning Models (The use
of logically and statistically derived functional relationships
in models of complex systems)
2
The Linear Programming Model
Introduction
Graphical Solution Method
Formulation of Linear Programming Models
Logical and Statistical Modeling Methods
Use of Computer Software to Solve
Linear Programming Models
The Simplex Method for Solving Linear
Programming Models
Sensitivity Analysis for Linear Programming Models
Network Models
The Transportation Problem
The Assignment Problem
1
1
2
1
2
4
3
1
1
An Introduction to Nonlinear Programming Models
Formulation of Nonlinear Models Using Logical and
Statistical Modeling Methods
Use of Computer Software to Solve Nonlinear
Programming Models
Choosing a Linear or Nonlinear Function to
Model a Relationship
2
1
1
Methods for Incorporating Multiple Decision Criteria into
a Model
Goal Programming Models
3
A.
Methods for Decision Analysis Under Conditions of
Uncertainty
Use of Payoff Tables
Use of Decision Trees
Expected Value and Expected Utility as
Decision Criteria
7*
B.
Analytical Queuing Models
Single Server Queues
Multiple Server Queues
Economic Evaluation of Queuing Systems
C.
Analytical Inventory Models
Deterministic Models
Probabilistic Models
* Seven lectures for one or more of topics A, B, and C at the instructor’s discretion.
Simulation Models
Models of Queuing Systems
Models of Inventory Systems
Evaluating Decision Alternatives Using Simulation Models
Using Regression Analysis to Evaluate Alternatives
Combining Modeling Methodologies
The Use of Simulation and Regression Analysis to Build
a Mathematical Programming Model, An Example
TOTAL
3
2
1
1
1
42