Optimization With Julia: Mastering Operations Research

Solve optimization problems with Gurobi, CPLEX, GLPK, IPOPT, JuMP... using linear programming, nonlinear, MILP...

What you'll learn
Solve optimization problems using linear programming, mixed-integer linear programming, nonlinear programming, mixed-integer nonlinear programming
Main solvers, including Gurobi, CPLEX, GLPK, CBC, IPOPT, Couenne, SCIP, Bonmin
How to use JuMP to solve optimization problems with Julia
How to solve problems with summations and multiple constraints
How to install and use Julia
How to install and activate each solver

Requirements
Some knowledge in programming logic
What is operations research
It is NOT necessary to know Julia

Description
The increasing complexity of the modern business environment has made operational and long-term planning for companies more challenging than ever. To address this, optimization algorithms are employed to find optimal solutions, and professionals skilled in this field are highly valued in today's market.As an experienced data science team leader and holder of a PhD degree, I am well-equipped to teach you everything you need to solve optimization problems in both practical and academic settings.In this course, you will learn how to problems problems using Mathematical Optimization, covering:Linear Programming (LP)Mixed-Integer Linear Programming (MILP)Nonlinear Programming (NLP)Mixed-Integer Nonlinear Programming (MINLP)Implementing summations and multiple constraintsWorking with solver parametersThe following solvers: CPLEX, Gurobi, GLPK, CBC, IPOPT, Couenne, Bonmin, SCIPThis course is designed to teach you through practical examples, making it easier for you to learn and apply the concepts.If you are new to Julia or programming in general, don't worry! I will guide you through everything you need to get started with optimization, from installing Julia and learning its basics to tackling complex optimization problems.By completing this course, you'll not only enhance your skills but also earn a valuable certification from Udemy.Operations Research | Operational Research | Operation Research | Mathematical OptimizationI look forward to seeing you in the classes and helping you advance your career in operations research!

Overview
Section 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

Section 2: Starting with Julia

Lecture 5 Installing Julia

Lecture 6 Installing VSCode

Lecture 7 Our first code

Lecture 8 If statement

Lecture 9 Functions

Lecture 10 Loops

Lecture 11 Lists, arrays and dicts

Lecture 12 Packages

Lecture 13 Reading Excel Files

Lecture 14 Learning more about Julia

Section 3: Linear Programming (LP)

Lecture 15 Introduction: Linear and Nonlinear problems

Lecture 16 Modeling a linear problem

Lecture 17 Solving the first linear problem

Lecture 18 Using CBC

Lecture 19 List of solvers

Lecture 20 Installing and using Gurobi

Lecture 21 Installing and using CPLEX

Lecture 22 Example LP 1: Meal Planning - Modeling

Lecture 23 Example LP 1: Meal Planning - Solving

Lecture 24 Example LP 1 - Working with indexes

Lecture 25 Example LP 2: Financial Investment - Modeling

Lecture 26 Example LP 2: Financial Investment - Solving

Lecture 27 LP Concepts

Section 4: Mixed-Integer Linear Programming (MILP)

Lecture 28 Integer and Binary Variables

Lecture 29 Defining Integer Variables in Julia

Lecture 30 MILP Solvers

Lecture 31 Example MILP: JobShop - Modeling

Lecture 32 Example MILP: JobShop - Solving

Lecture 33 MILP Concepts

Section 5: Working with Double Summation and Multiple Constraints

Lecture 34 Introduction and formulations

Lecture 35 Multiple Indexes in Julia

Lecture 36 Double Summations in Julia

Lecture 37 Multiple Constraints in Julia

Lecture 38 Multiple Constraints with Summation

Lecture 39 Naming Constraints

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

Lecture 40 Routing Problem Formulation

Lecture 41 Data Input structure

Lecture 42 Reading Excel

Lecture 43 Reading other sources

Lecture 44 Creating sets and filtering DataFrames

Lecture 45 Solving the routing problem

Lecture 46 Exporting the solution

Section 7: Parameters and Progress of the Solver

Lecture 47 Progress of the Solver

Lecture 48 Checking the parameters

Lecture 49 Gap Tolerance

Lecture 50 Time Limit

Section 8: Nonlinear Programming (NLP)

Lecture 51 Release date: March 26th, 2023

Section 9: Mixed-Integer Nonlinear Programming (MINLP)

Lecture 52 Release date: Abril 2nd, 2023

Section 10: Expanding Your Knowledge and Exploring Opportunities

Lecture 53 Enhancing Your Knowledge of Mathematical Formulation and Optimization

Lecture 54 Course recommendation to expand your skills: Optimization with Python

Lecture 55 Congratulations

Undergrad, graduation, master program, and doctorate students,Companies that wish to solve complex problems,People interested in solving complex problems

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