Vehicle Routing Problem with Time Window Constrain using KMeans Clustering to Obtain the Closest Customer

In this paper, the problem statement is solving the Vehicle Routing Problem (VRP) with Time Window constraint using the Ant Colony Algorithm with K-Means Clustering. In this problem, the vehicles must start at a common depot, pickup from various ware houses, deliver to the respective nodes within the time window provided by the customer and returns to depot. The objectives defined are to reduction in usage of number of vehicles, the total logistics cost and to reduce carbon emissions. The mathematical model described in this paper has considered multiple pickup and multiple delivery points. The proposed solution of this paper aims to provide better and more efficient solution while minimizing areas of conflict so as to provide the best output on a large scale in Vehicle Routing Problem, K-Means Clustering, Time Window constraint, Ant Colony Algorithm

Metaheuristic link prediction (MLP) using AI based ACO-GA optimization model for solving vehicle routing problem

Delivering goods is crucial to the supply chain industry because it directly affects package delivery, a crucial aspect of real-time vehicle movement on which most e-commerce businesses rely. By improving the vehicle routing process, package delivery speed could be increased, and especially for medical emergency-related items, this will drastically impact the nature of delivery, cost, and time spent on it. This is being done to prepare an efficient routing model for the vehicle route, which will ultimately result in an improved path. As they have a variety of restrictions, the goal of this article is to identify the various parameters that, when used with a multi-objective optimization-based routing model, will satisfy the limit. The routing route may be made more efficient using ant colony optimization (ACO) in conjunction with an upgraded recurrent model of the genetic algorithm (GA). To achieve this, the ACO-GA optimization method known as metaheuristic link prediction (MLP) was used for parameter prediction. This method offers an evaluation of the relationship between the emission of CO2 (carbon dioxide), the trip region, and the other associated parameters. The authors of this study compare the findings of their prior work, which combined ACO and K-means clustering to get better results. Once the results are established, they will become the primary objective function of the optimization algorithm, which will be responsible for choosing the path that is connected to the parameter values. The complete procedure of the suggested method was simulated and evaluated using the publicly accessible data set of Solomon’s benchmark data set with the property pairs, and then it was compared with the ACO-K-means method. In addition to this, the current algorithm is compared with other vehicle routing algorithms to improve the process