Route optimization via improved ant colony algorithm with graph network

Abstract

Route optimization problem using vehicle routing problem (VRP) and time window constraint is explained as finding paths for a finite count of vehicles to provide service to a huge number of customers and hence, optimizing the path in a given duration of the time window. The vehicles in the loop have restricted intake of capacity. This path initiates from the depot, delivers the goods, and stops at the depot. Each customer is to serve exactly once. If the arrival of the vehicle is before the time window “opens” or when the time window “closes,” there will be waiting for cost and late cost. The challenge involved over here is to scheduling visits to customers who are only available during specific time windows. Ant colony optimization (ACO) algorithm is a meta-heuristic algorithm stimulated by the growing behaviour of real ants. In this paper, we combine the ACO algorithm with graph network henceforth increasing the number of vehicles in a particular depot for increasing the efficiency for timely delivery of the goods in a particular time width. This problem is solved by, an efficient technique known as the ACO+graph algorithm.