Optimizing Travelling Salesman and Vehicle Routing Problems
Optimizing Travelling Salesman and Vehicle Routing Problems, available at $44.99, has an average rating of 3.25, with 33 lectures, 4 quizzes, based on 258 reviews, and has 1231 subscribers.
You will learn about Learn optimization through Simulated Annealing and Tabu Search Learn how to solve the Travelling Salesman Problem (TSP) Lean how to solve the Vehicle Routing Problem (VRP) Learn how to handle optimization constraints Learn how to code metaheuristics in Python programming This course is ideal for individuals who are Anyone who is interested in optimization or Anyone who is interested in knowing metaheuristics including Simulated Annealing and Tabu Search or Anyone who wants to solve the Travelling Salesman Problem (TSP) or Anyone who wants to solve the Vehicle Routing Problem (VRP) or Anyone who wants to learn how to handle optimization constraints or Anyone who wants to learn how to code metaheuristics in Python programming It is particularly useful for Anyone who is interested in optimization or Anyone who is interested in knowing metaheuristics including Simulated Annealing and Tabu Search or Anyone who wants to solve the Travelling Salesman Problem (TSP) or Anyone who wants to solve the Vehicle Routing Problem (VRP) or Anyone who wants to learn how to handle optimization constraints or Anyone who wants to learn how to code metaheuristics in Python programming.
Enroll now: Optimizing Travelling Salesman and Vehicle Routing Problems
Summary
Title: Optimizing Travelling Salesman and Vehicle Routing Problems
Price: $44.99
Average Rating: 3.25
Number of Lectures: 33
Number of Quizzes: 4
Number of Published Lectures: 33
Number of Published Quizzes: 4
Number of Curriculum Items: 37
Number of Published Curriculum Objects: 37
Original Price: $129.99
Quality Status: approved
Status: Live
What You Will Learn
- Learn optimization through Simulated Annealing and Tabu Search
- Learn how to solve the Travelling Salesman Problem (TSP)
- Lean how to solve the Vehicle Routing Problem (VRP)
- Learn how to handle optimization constraints
- Learn how to code metaheuristics in Python programming
Who Should Attend
- Anyone who is interested in optimization
- Anyone who is interested in knowing metaheuristics including Simulated Annealing and Tabu Search
- Anyone who wants to solve the Travelling Salesman Problem (TSP)
- Anyone who wants to solve the Vehicle Routing Problem (VRP)
- Anyone who wants to learn how to handle optimization constraints
- Anyone who wants to learn how to code metaheuristics in Python programming
Target Audiences
- Anyone who is interested in optimization
- Anyone who is interested in knowing metaheuristics including Simulated Annealing and Tabu Search
- Anyone who wants to solve the Travelling Salesman Problem (TSP)
- Anyone who wants to solve the Vehicle Routing Problem (VRP)
- Anyone who wants to learn how to handle optimization constraints
- Anyone who wants to learn how to code metaheuristics in Python programming
In this course, we will solve the Travelling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) through Metaheuristics, namely, Simulated Annealing and Tabu Search. You will also learn how to handle constraints in optimization problems. You will learn how to code the TSP and VRP in Python programming.
Please feel free to ask me any question! Don’t like the course? Ask for a 30-day refund!!
The ideal student should have basic knowledge in Operation Research and basic programming skills.
Real Testaments –>
1) “Great course if you are having trouble to understand the traveling salesman and vehicle routing problems. I would recommend this course to anyone. The instructor is also very helpful if you ask questions. Very quick response. Keep it up Dana. Thanks” — Ryan, 5 star rating
2) “Amazing teaching skill and very well structured course for handling real-life routing problems. After completing this course, you will obtain an ability to apply these techniques in various optimization problems. I would highly recommend this course for anyone who is interested in optimization. Kudos Dana!!” — Brejees, 5 star rating
3) “The course has real-world applications especially in areas of Logistics and supply chain management. I recommended this course to anyone who works on the technical side of transportation and logistics.” — Bhawesh, 5 star rating
Course Curriculum
Chapter 1: Introduction
Lecture 1: Promo Video
Lecture 2: Course Outline
Lecture 3: PLEASE READ
Lecture 4: Optimization
Lecture 5: Correction!!!
Lecture 6: P vs. NP Problems Resource (IMPORTANT!!!)
Lecture 7: Metaheuristics
Lecture 8: Search Techniques #1
Lecture 9: Search Techniques #2
Lecture 10: Search Techniques #3
Chapter 2: Simulated Annealing
Lecture 1: Simulated Annealing #1
Lecture 2: Simulated Annealing #2
Lecture 3: Simulated Annealing #3
Chapter 3: Tabu Search
Lecture 1: Tabu Search #1
Lecture 2: Tabu Search #2
Lecture 3: Tabu Search #3
Chapter 4: Constraint Handling
Lecture 1: Constraint Handling
Chapter 5: Travelling Salesman Problem
Lecture 1: Travelling Salesman Problem
Lecture 2: Travelling Salesman Problem , Coding in Python #1
Lecture 3: Travelling Salesman Problem , Coding in Python #2
Lecture 4: Travelling Salesman Problem , Coding in Python #3
Chapter 6: Vehicle Routing Problem
Lecture 1: Vehicle Routing Problem #1
Lecture 2: Vehicle Routing Problem #2
Lecture 3: Vehicle Routing Problem #2, Coding in Python #1
Lecture 4: Vehicle Routing Problem #2 , Coding in Python #2
Lecture 5: Vehicle Routing Problem #2 , Coding in Python #3
Lecture 6: Vehicle Routing Problem #2 , Coding in Python #4
Lecture 7: Vehicle Routing Problem #2 , Coding in Python #5
Lecture 8: Vehicle Routing Problem #2 , Coding in Python #6
Lecture 9: Vehicle Routing Problem #2 , Coding in Python #7
Lecture 10: Vehicle Routing Problem #2 , Coding in Python #8
Lecture 11: Vehicle Routing Problem #2 , Coding in Python #9
Chapter 7: BONUS OFFER!!
Lecture 1: Bonus Lecture: Discounted Coupons
Instructors
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Curiosity for Data Science
Architect and Industrial Engineer
Rating Distribution
- 1 stars: 13 votes
- 2 stars: 19 votes
- 3 stars: 54 votes
- 4 stars: 83 votes
- 5 stars: 89 votes
Frequently Asked Questions
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