CVRPTW Algorithm Analysis
| dc.contributor.author | ANKIT BHATTACHARJEE 1NH21AI012 CHO LAQSHYA 1NH21AI025 | |
| dc.date.accessioned | 2025-05-20T04:59:15Z | |
| dc.date.available | 2025-05-20T04:59:15Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | The Vehicle Routing Problem with Time Windows (VRPTW) is a combinatorial optimization problem that extends the classic Vehicle Routing Problem (VRP) by incorporating time constraints for customer deliveries. In this problem, a fleet of vehicles, each with a fixed capacity, must deliver goods to a set of customers while minimizing the total travel distance or cost. Each customer has a specific demand that must be met, and deliveries must occur within predefined time windows, meaning a vehicle cannot arrive earlier than the lower bound or later than the upper bound of the assigned timeframe. The goal is to determine a set of optimal routes that minimize the total distance traveled while ensuring all deliveries adhere to capacity and time constraints. The problem is NP-hard, meaning finding an exact solution for large instances is computationally expensive, requiring heuristic or metaheuristic approaches such as Genetic Algorithms (GA), Simulated Annealing, Ant Colony Optimization, or Tabu Search. VRPTW has many real-world applications, including logistics, last-mile delivery, and supply chain management, where companies strive to optimize fuel consumption, reduce operational costs, and improve customer satisfaction by ensuring on-time deliveries. The challenge lies in balancing multiple conflicting objectives, such as minimizing the number of vehicles used, reducing the overall route length, and meeting customer constraints while handling real-world uncertainties such as traffic conditions, vehicle breakdowns, and delays. Given these complexities, modern AI-based optimization techniques play a crucial role in efficiently solving large-scale VRPTW instances. | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/18991 | |
| dc.language.iso | en | |
| dc.publisher | NHCE | |
| dc.title | CVRPTW Algorithm Analysis | |
| dc.type | Learning Object |