OR/ISE 501: Introduction to Operations Research

Introduction

• 1: Course Overview, Modeling, and Installing Julia
• 2: Basic Concepts in Julia
• 3: Monte Carlo Methods
• HW 1: Julia
  

Nonlinear Optimization

• 1: Univariate Optimization
• 2: Multivariate Optimization
• 3: Operations Analysis
• HW 2: Nonlinear Optimization

Linear Programming

• 1: Constrained Optimization
• 2: Modeling and Sensitivity Analysis
• 3: Larger-Scale LP Models
• HW 3: LP

Network Models

• 1: Linear Assignment and Transportation Problems
• 2: Shortest Paths and CPM
• 3: Minimum Cost Network Flows
• HW 4: Network Modeling

Mixed-Integer Programming

• 1: Solvers and Branch-and-Bound
• 2: Modeling Constructs for Internal Decisions
• 3: Set Covering, Bin Packing, and Graph Coloring Problems
• 4: Production-Inventory Planning
• HW 5: Mixed-Integer Programming

Combinatorial Optimization

• 1: Brute-Force Optimization
• 2: Construction and Improvement Heuristics
• 3: Scheduling Problems
  
• HW 6: Combinatorial Optimization

Machine Learning

• 1: Data Wrangling
• 2: Regression 
• 3: Classification - Decision Trees
• HW 7: Machine Learning

Study Guide for the Final Exam (soln)

Michael G. Kay, NC State University, Fall 2021