Optimizing Logistics And Distribution Networks Using Ant Colony Optimization (ACO)

Abstract

To reduce costs, improve delivery times, and enhance overall logistics and distribution network efficiency, it is crucial to optimize the network. The traditional methods may be insufficient when a system includes numerous variables such as traffic volumes and resource availability, and is dynamic, such as when the demand changes over time. Ant Colony Optimization (ACO) is a bio-inspired metaheuristic algorithm that has proven to be a viable option for solving these problems. ACO simulates ants' foraging process and, after several iterations, finds the optimal solution by exchanging information pheromones. The purpose of this paper is to improve logistics and distribution systems using ACO to optimize network efficiency, minimize operational costs and ensure on-time deliveries. This study proposes to introduce the concept of ACO to the logistics optimization problem by simulating an ant visiting a network of distribution centres, warehouses and customers' destinations. The model considers important parameters such as vehicle capacity, time window constraints and the influence of pheromones. The goal function is to minimize overall costs such as travel time, fuel consumption, as well as capacity and delivery time constraints. The total travel time and costs in the ACO application in a synthetic logistics network prove to be significantly reduced compared to the unoptimized routes. The algorithm was highly accurate (92%) with a runtime of 45 minutes and a convergence of 150 iterations. The ACO approach is proven better than traditional approaches, such as the Genetic Algorithms (GA) and Simulated Annealing (SA), in terms of cost, time and convergence rate.