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!

Who this course is for
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.

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