Optimization with Julia: Mastering Operations Research

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is optimization and why use Julia

Lecture 2: Objective function, variables, parameters and constraints

Lecture 3: How to solve optimization problems

Lecture 4: Examples of what you are gonna learn

Chapter 2: Starting with Julia

Lecture 1: Installing Julia

Lecture 2: Installing VSCode

Lecture 3: Our first code

Lecture 4: If statement

Lecture 5: Functions

Lecture 6: Loops

Lecture 7: Lists, arrays and dicts

Lecture 8: Packages

Lecture 9: Reading Excel Files

Lecture 10: Learning more about Julia

Chapter 3: Linear Programming (LP)

Lecture 1: Introduction: Linear and Nonlinear problems

Lecture 2: Modeling a linear problem

Lecture 3: Solving the first linear problem

Lecture 4: Using CBC

Lecture 5: List of solvers

Lecture 6: Installing and using Gurobi

Lecture 7: Installing and using CPLEX

Lecture 8: Example LP 1: Meal Planning – Modeling

Lecture 9: Example LP 1: Meal Planning – Solving

Lecture 10: Example LP 1 – Working with indexes

Lecture 11: Example LP 2: Financial Investment – Modeling

Lecture 12: Example LP 2: Financial Investment – Solving

Lecture 13: LP Concepts

Chapter 4: Mixed-Integer Linear Programming (MILP)

Lecture 1: Integer and Binary Variables

Lecture 2: Defining Integer Variables in Julia

Lecture 3: MILP Solvers

Lecture 4: Example MILP: JobShop – Modeling

Lecture 5: Example MILP: JobShop – Solving

Lecture 6: MILP Concepts

Chapter 5: Working with Double Summation and Multiple Constraints

Lecture 1: Introduction and formulations

Lecture 2: Multiple Indexes in Julia

Lecture 3: Double Summations in Julia

Lecture 4: Multiple Constraints in Julia

Lecture 5: Multiple Constraints with Summation

Lecture 6: Naming Constraints

Chapter 6: Using external inputs to solve a routing problem (VRP)

Lecture 1: Routing Problem Formulation

Lecture 2: Data Input structure

Lecture 3: Reading Excel

Lecture 4: Reading other sources

Lecture 5: Creating sets and filtering DataFrames

Lecture 6: Solving the routing problem

Lecture 7: Exporting the solution

Chapter 7: Parameters and Progress of the Solver

Lecture 1: Progress of the Solver

Lecture 2: Checking the parameters

Lecture 3: Gap Tolerance

Lecture 4: Time Limit

Chapter 8: Nonlinear Programming (NLP)

Lecture 1: NLP challenges

Lecture 2: NLP Solvers

Lecture 3: Transportation problem using exp() curve – Modelling

Lecture 4: Transportation problem – Solution – Using Ipopt

Lecture 5: Solving problem with cosines and sines – local solutions – using IPOPT

Lecture 6: Using Couenne to find a global solution

Lecture 7: NLP Concepts

Chapter 9: Mixed-Integer Nonlinear Programming (MINLP)

Lecture 1: MINLP Concepts and Solvers

Lecture 2: Solving a MINLP problem with Couenne

Lecture 3: Using Bonmin

Lecture 4: Using SCIP

Lecture 5: Avoiding errors in MINLP

Chapter 10: Expanding Your Knowledge and Exploring Opportunities

Lecture 1: Enhancing Your Knowledge of Mathematical Formulation and Optimization

Lecture 2: Course recommendation to expand your skills: Optimization with Python

Lecture 3: Congratulations

Lecture 4: Thank you!