Optimization with Python: Solve Operations Research Problems

Course Curriculum

Chapter 1: Introduction to the course

Lecture 1: Introduction

Lecture 2: What is optimization

Chapter 2: Installing Python

Lecture 1: Installing Python

Lecture 2: Packages

Lecture 3: Important note about Python

Lecture 4: IDE Spyder

Lecture 5: Jupyter NotebookLab

Chapter 3: Starting with Python

Lecture 1: Lists, Tuples, and Dictionary

Lecture 2: If, For, While

Lecture 3: Functions

Lecture 4: Numpy

Lecture 5: Pandas

Lecture 6: Pandas: reading Excel

Lecture 7: Graphs

Lecture 8: PDFs to learn more about Python

Chapter 4: Introduction to mathematical modelling

Lecture 1: What is Mathematical Modelling?

Lecture 2: How do we solve optimization problems?

Lecture 3: Type of Variables

Lecture 4: Objective Function and Constraints

Lecture 5: How to model your problem?

Lecture 6: Our first formulation

Lecture 7: Example 1: investiment

Lecture 8: Example 2: investiment

Lecture 9: Example 3: production cost

Lecture 10: Example 4: route problem

Lecture 11: Example 5: construction assignment

Lecture 12: Example 6: construction assignment

Lecture 13: Example 7: job assignment

Lecture 14: Example 8: job assignment

Lecture 15: How to Learn More?

Lecture 16: Some references for you learn more (problems of VRPTW, TSP, JobShop…)

Chapter 5: Linear Programming (LP)

Lecture 1: LP: Introduction

Lecture 2: Framework and Solvers

Lecture 3: LP: Ortools

Lecture 4: LP: SCIP

Lecture 5: LP: SCIP | errors during installation

Lecture 6: LP: Gurobi, CPLEX, and GLPK (installation)

Lecture 7: Academic License for Gurobi [Updates]

Lecture 8: LP: Pyomo (using Gurobi, CPLEX, and GLPK)

Lecture 9: LP: Pyomo | overcoming errors

Lecture 10: LP: PuLP

Lecture 11: Which solver and frameworks should we choose?

Lecture 12: LP: Exercise, solve it by yourself

Lecture 13: LP: Concepts

Chapter 6: Working with Pyomo

Lecture 1: Pyomo: Using other solvers (CBC)

Lecture 2: Pyomo: Summations

Lecture 3: Pyomo: Double Summations and Variables with 2 or more indexes

Lecture 4: Pyomo: Pprint

Lecture 5: Pyomo: Manual

Chapter 7: Mixed-Integer Linear Programming (MILP)

Lecture 1: MILP: Introduction

Lecture 2: MILP: Pyomo

Lecture 3: MILP: Ortools

Lecture 4: MILP: SCIP

Lecture 5: MILP: Exercise, solve it by yourself

Lecture 6: MILP: Exercise solution

Lecture 7: MILP: Concepts

Chapter 8: Nonlinear Programming (NLP)

Lecture 1: NLP: Introduction

Lecture 2: NLP: Pyomo (IPOPT)

Lecture 3: NLP: SCIP

Lecture 4: NLP: Exercise, solve it by yourself

Lecture 5: NLP: Exercise Solution

Lecture 6: NLP: Concepts

Chapter 9: Mixed-Integer Nonlinear Programming (MINLP)

Lecture 1: MINLP: Introduction

Lecture 2: MINLP: Pyomo (Couenne)

Lecture 3: MINLP: Pyomo (decomposition using mindtpy)

Lecture 4: MINLP: SCIP

Chapter 10: Genetic Algorithm and Particle Swarm

Lecture 1: Genetic Algorithm: Introduction

Lecture 2: Genetic Algorithm: Base Case Example

Lecture 3: Genetic Algorithm: Routing Problem

Lecture 4: Multi-Objective Problems using NSGA-II – An introduction

Lecture 5: Particle Swarm (PSO): Base Case Example

Lecture 6: PSO: Concepts

Chapter 11: Constraint Programming (CP)

Lecture 1: CP: Ortools

Lecture 2: CP: Concepts

Chapter 12: Special Cases

Lecture 1: Introduction

Lecture 2: SCOP: Second-Order Cone Programming

Lecture 3: NonConvex Quadratic Programming

Lecture 4: Vehicle Routing Problems (VRP) with Or-Tools, An introduction

Lecture 5: Linearization: binary*continuos using BigM

Lecture 6: Linearization: binary*binary

Chapter 13: Advanced Features for Pyomo

Lecture 1: Introduction and a new Case Study with double summations

Lecture 2: Check the solver progress

Lecture 3: Define a Gap Limit

Lecture 4: Define a Time Limit

Lecture 5: More parameters for the solvers