The Vehicle Routing Problem (VRP) is a fundamental challenge in logistics and supply chain management, involving the optimization of routes for a fleet of vehicles to deliver goods to a set of customers. The problem’s complexity increases with the number of vehicles, delivery points, and constraints such as delivery windows, vehicle capacities, and traffic conditions. Recent advancements in deep reinforcement learning (DRL) and metaheuristics offer promising solutions to enhance VRP efficiency and scalability.
Understanding the Vehicle Routing Problem
The VRP can be seen as an extension of the Traveling Salesman Problem (TSP), where multiple vehicles must visit a set of locations and return to a central depot. The goal is to minimize the total travel distance or time while satisfying constraints such as vehicle capacity and delivery windows. The combinatorial nature of VRP makes it computationally challenging, especially as the problem size grows.
