# Course

Course Summary
Credit Type:
Course
ACE ID:
STAT-0019
Organization's ID:
#625
Organization:
Location:
Online
Length:
8 weeks (120 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Graduate 3 integer and nonlinear programming and network flow
Description

## Objective:

The course objective is to provide information to specify and implement optimization models that solve network problems (what is the shortest path through a network, what is the least cost way to route material through a network with multiple supply nodes and multiple demand nodes). Students also learn how to solve Integer Programming (IP) problems and Nonlinear Programming (NLP) problems. Spreadsheet-based software is used to specify and implement models.

## Learning Outcomes:

• describe the characteristics of a network flow problem
• Specify an objective function and constraints for a network problem, and model it with software
• Describe the scenario in which an integer programming method is used
• Specify an integer programming model
• Appropriately use rounding and stopping rules, and branch and bound
• Solve the integer programming problem with software
• Accommodate multiple goals in the analysis
• specify a nonlinear programming model
• demonstrate knowledge of how various decision problems can be modeled and solved as network flow problems, and the pros and cons of this modeling framework
• Demonstrate knowledge of how to use general integer and binary variables in mathematical optimization models to create more accurate representations of decision problems, and the computational challenges presented by these integer programming models
• Demonstrate knowledge of the techniques for dealing with multiple objectives in mathematical optimization problems
• demonstrate knowledge of how non-convexity and non-linearity impacts optimization problems, and how artificial intelligence techniques such as genetic/evolutionary algorithms can be applied to hard optimization problems

## General Topics:

• Network Flow Problems
• Integer Linear Programming
• Multiple Goals
• Nonlinear Programming
Instruction & Assessment

## Instructional Strategies:

• Case Studies
• Classroom Exercise
• Coaching/Mentoring
• Computer Based Training
• Discussion
• Practical Exercises
• Project-based Instruction

• Case Studies
• Other
• Quizzes
• Project

## Minimum Passing Score:

80%
Supplemental Materials

(STAT-0038)
(STAT-0021)