Master Vehicle Route Planning Problems In Python

Learn to Solve TSP and CVRP problems with 2-opt, 3-opt, Large Neighbourhood Search, Tabu Search and Simulated Annealing.

What you'll learn

Understand VRP Theory: Learn the theory behind TSP and CVRP and how these problems are tackled in optimization.

Implement Algorithms from Scratch: Code k-opt, Large Neighbourhood Search, Tabu Search, and Simulated Annealing algorithms using basic Python libraries.

Hands-On Practice: Apply algorithms to standard TSP and CVRP problem instances with practical coding exercises.

Visualize Solutions Dynamically: Create animations and visualizations to understand and present solutions step-by-step.

Follow Numerical Examples: Step-by-step numerical examples guide you through the theory and implementation of each algorithm.

Compare Algorithm Performance: Evaluate and compare the results of different optimization algorithms to infer their efficiency and applicability.

Customize and Expand Algorithms: Learn how to adapt and expand these algorithms for other VRP variants and real-world scenarios.

Explore Heuristic Improvements: Implement different algorithm structures and ideas to improve the efficiency of heuristics and metaheuristics.

Requirements

Basic Python Knowledge (Preferred): Familiarity with Python syntax and basic programming concepts is recommended.

No Prior Experience with VRP Needed: All algorithms and concepts will be explained from scratch, so no prior knowledge of vehicle routing is required.

Description

Unlock the power of optimization by mastering Vehicle Routing Problems (VRP) with Python! In this course, you will learn to solve the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP) using a range of powerful algorithms—k-opt, Large Neighborhood Search, Tabu Search, and Simulated Annealing.Designed for researchers, data scientists, and professionals in logistics and scheduling, this course provides both the theoretical foundations and hands-on coding exercises. You will implement each algorithm from scratch using basic Python libraries, enabling a deep understanding of the concepts without relying on external packages.We’ll walk through real-world problem instances, offering step-by-step explanations of both theory and code. You’ll also create dynamic visualizations of algorithmic solutions, helping you visualize how these algorithms work in practice. Beyond coding and theory, this course emphasizes practical application. You’ll learn how to compare algorithm performance, draw meaningful conclusions, and understand when to apply each method based on the problem’s unique requirements. With guided numerical examples and problem-solving strategies, you’ll gain the confidence to tackle various VRP variants and optimize real-world logistics challenges. Whether you're working in research or industry, this course will provide you with a strong foundation to innovate and improve routing solutions efficiently.Whether you're looking to enhance your skills in optimization, develop solutions for industry challenges, or expand your knowledge of heuristic and metaheuristic algorithms, this course equips you with all the tools you need to excel.By the end, you’ll not only understand how to solve VRPs but also how to customize and expand these algorithms for more complex, real-world problems. Join us and take your optimization skills to the next level!

Overview

Section 1: Introduction

Lecture 1 TSP vd VRP

Lecture 2 VRP variants and Shortest Path Problems

Lecture 3 TSP and VRP data for the course: Know the data

Lecture 4 Take the first look at the data

Lecture 5 Function for Reading all the Data

Lecture 6 Visualize TSP Data

Lecture 7 Visualize CVRP Data

Section 2: K-opt Algorithms for TSP and CVRP

Lecture 8 Heuristics vs Metaheuristics

Lecture 9 2-opt Algorithms Theory

Lecture 10 3-opt Algorithm Theory

Lecture 11 Initialize a Random Tour for TSP

Lecture 12 2-opt for TSP: Part 1

Lecture 13 2-opt for TSP: Part 2

Lecture 14 2-opt Results for TSP

Lecture 15 Visualize 2-opt Results for TSP

Lecture 16 Tricks for 3-opt Algorithms for TSP: Part1

Lecture 17 Tricks for 3 Opt Algorithms for TSP: Part 2

Lecture 18 Design 3-opt Function

Lecture 19 3-opt Results for TSP

Lecture 20 2-opt Roadmap for CVRP

Lecture 21 Initial Solution Design for CVRPs

Lecture 22 2-opt Algorithm Design for CVRP

Lecture 23 2-opt Results Function Design for CVRP

Lecture 24 2-opt Results for CVRP

Lecture 25 3-opt Algorithm for CVRP and Results

Section 3: Large Neighbourhood Search for TSP and CVRP

Lecture 26 Large Neighbourhood Search Theory

Lecture 27 Nearest Neighbour Greedy Search Initialization for TSP

Lecture 28 Destroy and Repair Function Design

Lecture 29 Large Neighbourhood Search Function Design

Lecture 30 LNS Results for TSP

Lecture 31 Destroy Function for CVRP

Lecture 32 Repair Function for CVRP

Lecture 33 LNS Function for CVRP

Lecture 34 Animation of CVRP Results for LNS

Section 4: Tabu Search Algorithms

Lecture 35 Tabu Search Theory

Lecture 36 Swap Algorithm Design

Lecture 37 Tabu Search Function Design for TSP

Lecture 38 TSP Results for Tabu Search

Lecture 39 Initialization of Local Search Swap Algorithm for CVRP

Lecture 40 Tabu Search Function for CVRP

Lecture 41 CVRP Results for Tabu Search

Section 5: Simulated Annealing for TSP and CVRP

Lecture 42 Simulated Annealing Theory

Lecture 43 SA Function Design

Lecture 44 TSP Results for SA

Lecture 45 CVRP Results for SA

Section 6: Resources

Lecture 46 Books

Lecture 47 Papers

Lecture 48 Courses

Researchers in Optimization Problems: Ideal for those working on or studying optimization algorithms and techniques.,Data Scientists: Especially those focusing on solving complex logistics, routing, and scheduling problems.,Planners and Schedulers: Professionals in companies managing delivery routes, production schedules, or other resource allocation tasks.,Students and Enthusiasts: Anyone interested in learning how to solve Vehicle Routing Problems (VRP) using Python from scratch.,Developers Seeking Heuristic Insights: Programmers looking to improve their skills in heuristics and metaheuristics for optimization.