Enhancing Vehicle Routing with Time Windows: A Machine Learning-Driven Ant Colony Optimization Approach

The Vehicle Routing Problem with Time Windows (VRPTW) is a challenging optimization problem in logistics and transportation, where the objective is to efficiently plan routes for vehicles to service a set of customers within specified time windows while minimizing costs. This paper introduces a novel approach to solving VRPTW using a Machine Learning-Driven Ant Colony Optimization (ML-ACO) algorithm, referred to as Machine Learning-Driven Ant Colony Optimization for Vehicle Routing with Time Windows (ML-ACO-VRPTW). The proposed algorithm enhances traditional ACO by integrating a machine learning model, to predict heuristic values that capture the urgency of time windows and vehicle capacity constraints. The algorithm begins with initializing pheromone levels and parameters, followed by constructing routes based on probabilistic path selection influenced by pheromone intensity and machine learning-predicted heuristic values. Key innovations include dynamic pheromone updates that adapt to the algorithm's progress and the incorporation of penalties for time window violations and vehicle capacity exceedances. The pheromones are used to maintain a memory of the paths, and the machine learning model ensures adaptive and accurate heuristic predictions.