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Avascular Necrosis (AVN) || Early Detection, Better Outcomes || ~xRay Pixy

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Avascular Necrosis (AVN) is a condition where blood flow to the bone is reduced, causing bone cells to die. This leads to pain, joint damage, and difficulty in movement, especially in the hip. Early diagnosis and proper treatment can prevent permanent bone damage and improve quality of life. Video Chapter: AVN 00:00 Introduction 00:45 What is AVN? 01:55 About Bone Tissue 02:49 AVN Causes 03:38 AVN Symptoms 04:11 AVN Diagnosis 04:56 AVN of femoral head 05:33 How AVN Develops 07:28 Conclusions #optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python #optimizationproblem #optimizationalgorithms 

Vehicle Routing Problem (VRP) ~xRay Pixy

Vehicle Routing Problem

Learn Vehicle Routing Problems Step-By-Step using Examples.
Video Chapters: Vehicle Routing Problem (VRP)
00:00 Introduction
00:18 VRP Example
00:52 VRP Variants
02:43 VRP Objective
03:20 VRP Component
06:06 VRP Graph Representation
05:13 VRP Challenge
05:51 TSP vs VRP
07:17 VRP Real-Life Situations
07:38 VRP Solving Rules
08:57 VRP Method
09:16 Metaheuristics for VRP
10:16 VRP Application Areas
10:24 Metaheuristics to solve VRP
11:39 Conclusion

VEHICLE ROUTING PROBLEM is a fundamental combinatorial optimization and integer programming problem in the fields of transportation, logistics, and operations research. It involves determining the most efficient routes for a fleet of vehicles to traverse in order to deliver goods or services to a set of customers, subject to various constraints.

Objective: Minimize the Total Route Cost. While satisfying all Co
  • Capacity Constraint
  • Time Window Constraints

VRP Basic Components

  • Depot: The starting and ending point for all vehicles.
  • Customers: Locations that require service (e.g., delivery or pickup).
  • Fleet: A set of vehicles available for routing, each with specific capacities.
  • Constraints: Limitations such as vehicle capacity, time windows, maximum route length, etc.

Variants of VRP

The basic VRP has numerous extensions and variants to model real-world complexities:

  1. Capacitated VRP (CVRP): Each vehicle has a limited carrying capacity.
  2. VRP with Time Windows (VRPTW): Customers must be serviced within specific time frames.
  3. VRP with Pickup and Delivery (VRPPD): Items must be picked up from specific locations and delivered to others.
  4. Split Delivery VRP (SDVRP): A customer's demand can be split across multiple vehicles.
  5. VRP with Multiple Depots (MDVRP): There are multiple depots from which vehicles can start and end their routes.
  6. Stochastic VRP: Elements like demand or travel times are uncertain and modeled probabilistically.
  7. Electric VRP (E-VRP): Considers electric vehicles with constraints like battery capacity and charging stations.
Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) Comparision:

 Real-World Considerations

Implementing VRP solutions in practice involves addressing several additional complexities:

  • Dynamic Routing: Adjusting routes in real-time in response to traffic conditions, cancellations, or new orders.
  • Driver Regulations: Complying with labor laws and driver working hours.
  • Geographical Constraints: Navigating areas with restricted access or specific traffic rules.
  • Multi-Objective Optimization: Balancing multiple objectives like cost, time, and customer satisfaction.
  • Integration with Other Systems: Synchronizing VRP solutions with inventory management, order processing, and customer relationship systems.
Metaheuristic algorithms like Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) are effective for solving Vehicle Routing Problems (VRPs) because they are designed to handle complex, large-scale optimization problems that have multiple variables and constraints.

Metaheuristics for VRP:

  1. Ant Colony Optimization (ACO): Inspired by how ants find the shortest paths, this method uses "pheromones" to guide the search for good solutions.
  2. Tabu Search: A technique that avoids revisiting previously explored solutions by keeping track of moves that are temporarily forbidden (tabu).
  3. Iterated Local Search: A method that repeatedly applies local search, finding a nearby solution, and then modifies it to explore new areas.
  4. Simulated Annealing: A technique based on how materials cool down slowly to find a state of minimal energy, which helps avoid getting stuck in local solutions.

Why Use Metaheuristics for VRPs?

  1. Scalability: Metaheuristics can solve large-scale problems with many customers and vehicles.
  2. Flexibility: They adapt to different constraints and objectives (e.g., minimizing costs or balancing workload).
  3. Efficiency: Provide near-optimal solutions quickly, even when the problem is complex

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