Optimization With Julia Mastering Operations Research – Download Free

Free Download 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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