Operations Research & Optimization Projects With Julia

Operations Research & Optimization Projects with Julia – Real-World Applications, Mathematical Models

What you'll learn

nderstand fundamental optimization techniques, including Linear Programming (LP), Integer Programming (IP), and Nonlinear Programming

Develop practical coding skills by implementing optimization algorithms in Python, Julia, MATLAB, and R to solve complex decision-making problems

Explore and apply metaheuristic optimization methods such as Particle Swarm Optimization (PSO), Simulated Annealing, and Ant Colony Optimization

Integrate optimization techniques with machine learning and stochastic methods to enhance decision-making processes in industries such as finance, logistics

Requirements

A basic understanding of programming concepts will be helpful but is not required.

Familiarity with basic mathematics and linear algebra will make it easier to grasp optimization concepts, but I will explain everything in a way that is accessible to all learners.

No prior knowledge of optimization is necessary—you’ll learn everything step by step.

Description

Operations Research (OR) and Optimization are fundamental in solving real-world problems across industries. From logistics and finance to artificial intelligence and system simulation, these techniques help organizations make better decisions, reduce costs, and improve efficiency.This course is designed to give you practical expertise in OR and optimization, focusing on real-world applications rather than just theory. You’ll start with the fundamentals—what optimization is, how it connects to Operations Research, and its role in industries. Then, we’ll move into more advanced topics, covering Integer Programming, Nonlinear Programming, and Mixed-Integer Nonlinear Programming (MINLP).The course includes hands-on projects where we solve practical problems such as the Traveling Salesman Problem (TSP), Portfolio Optimization, Warehouse Simulation, Job Shop Scheduling, and the Capacitated Vehicle Routing Problem (CVRP). You will learn to implement these solutions in Julia, using mathematical models and optimization techniques that apply to real-world decision-making scenarios.Additionally, we will cover stochastic optimization, prescriptive analytics, and machine learning-based optimization. By the end of this course, you’ll be equipped to tackle large-scale, complex optimization problems using Operations Research techniques.More lessons will be added to expand the scope of this course, covering even more real-world optimization challenges.Enroll now and start solving real-world problems with Operations Research and Optimization!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Guide For This Course

Section 2: Operations Research & Optimization

Lecture 3 What is Optimization?

Lecture 4 What is Operations Research?

Section 3: Software & Tools

Lecture 5 Cplex, Gurobi, Xpress and More

Lecture 6 What's Solver?

Lecture 7 Nextmv

Lecture 8 Timefold.ai

Lecture 9 Hexaly

Lecture 10 Hexaly - Website Tour

Lecture 11 COIN-OR

Lecture 12 OMLT

Section 4: SAP & Optimization

Lecture 13 ERP & OR

Section 5: Optimization For Data Science

Lecture 14 Optimization & Data Science

Section 6: The Interplay between Operations Research and Machine Learning

Lecture 15 Operations Research & Machine Learning

Section 7: Operations Research & Management Science

Lecture 16 OR & MS

Section 8: Operations Research & System Simulation

Lecture 17 OR & Simulation

Section 9: Real Life Application of Math in Operations Research

Lecture 18 OR in Real Life

Section 10: Integer Programming

Lecture 19 Branch and Bound | Intro

Lecture 20 Branch and Bound | Diagram

Lecture 21 Branch and Bound | Knapsack

Lecture 22 Branch and Bound | Production Planning

Section 11: Nonlinear Programming

Lecture 23 Intro

Lecture 24 Karush-Kuhn-Tucker (KKT) Conditions

Section 12: Inventory Routing Problem

Lecture 25 IRP with Julia

Section 13: Capacitated Facility Location Problem (CFLP)

Lecture 26 Project

Section 14: Transportation Problem

Lecture 27 Project

Section 15: Traveling Salesman Problem with Julia

Lecture 28 Simulated Annealing

Section 16: Jop Shop Scheduling

Lecture 29 Optimization

Section 17: Robust Optimization

Lecture 30 Portfolio Management

Section 18: Mixed-Integer Nonlinear Programming (MINLP)

Lecture 31 Multi-period Portfolio Optimization

Section 19: Capacitated Vehicle Routing Problem (CVRP)

Lecture 32 CVRP Optimization

Section 20: Optimization for Machine Learning and Data Analytics

Lecture 33 ADAGrad

Lecture 34 Gradient Descent Optimization

Lecture 35 RMSProp

Section 21: Large-Scale Optimization

Lecture 36 Bender's Decomposition

Section 22: Simulation with Julia

Lecture 37 Warehouse Simulation

Lecture 38 Warehouse Simulation Part 2

Section 23: Sequential Decision Making

Lecture 39 Inventory Management

Section 24: Additional Content

Lecture 40 Prescriptive Analytics

Lecture 41 Stochastic Optimization

Lecture 42 Bayesian Optimization

Lecture 43 Teaching Learning Based Optimization

Lecture 44 Convex Optimization

Lecture 45 Grey Wolf Optimizer

Lecture 46 Adaptive Optimization

Lecture 47 Whale Optimization Algorithm

Lecture 48 Chance Constrained Optimization

Lecture 49 Surrogate Optimization

Section 25: Book List

Lecture 50 Optimization Related Books

This course is designed for engineers, data scientists, researchers, and business analysts who want to apply optimization techniques to real-world problems.