Enhancing sustainability in logistics through stochastic network routing mechanism optimization using ant colony algorithm

Abstract

Stochastic networks are one of the most prevalent types of networks these days. Therefore, many researchers directed to study them and summarize the essential points and challenges they face in developing these types of networks, especially optimal route path selection. In this paper, a solution to this problem was addressed using the evolutionary algorithm ACO (Ant Colony Optimization), where the path with the lowest cost was obtained according to several scenarios studied in the research, which consider the fact that, the traffic information in the network is available either in a static or in a dynamic form in real-time. The proposed method presented contributions for real networks that can be used in many applications. The results are essential in solving the problem of choosing the optimal route. Also, they can be applied to various scenarios of the stochastic networks that exist in real life. Optimization improves logistics efficiency, which contributes to sustainability by minimizing fuel consumption, reducing emissions, and conserving resources.