As part of phase 3 of his dissertation thesis, doctoral candidate Jai Keerthy Chowlur Revanna, of Harrisburg University of Science and Technology, has had an original research paper published in Volume 19 (March 2024) of Software Impacts, a renowned scientific journal. His co-author and faculty advisor was Nushwan Yousif Baithoon Al-Nakash, PhD, a professor from the Harrisburg University Information Systems Engineering and Management program.
The purpose of the research described in “Impact of ACO intelligent vehicle real-time software in finding shortest path” is to propose optimization methods for artificially intelligent pathfinding and routing systems. The researchers leveraged Ant Colony Optimization (ACO) algorithms and Genetic Algorithms (GA) in a method called “ACO-GA” to show how traffic clustering and other data can be used in real-time to enhance routing accuracy and improve vehicle travel efficiency.
“Our Python implementation of the ACO-GA algorithm has demonstrated remarkable efficacy in tackling the complexities of package delivery systems,” the authors write. “By amalgamating Ant Colony Optimization (ACO) with Genetic Algorithm (GA), our software excels in dynamically adjusting routes. Previously, the author experimented with an ACO-K-means clustering mechanism, yielding an accuracy of 0.86. Transitioning to GA has notably improved this accuracy to 0.842, representing a significant reduction in time and enhancement in delivery precision. This method considers various factors such as travel distance, traffic congestion, and real-time data.”
Importantly, this research is backed up by practical demonstrations in the real world:
“The implementation of this software has had a profound impact on supply chain industries, exemplified by a local company in Harrisburg that has adopted our approach, resulting in a remarkable 20 percent reduction in travel costs and consequential improvements in delivery efficiency. Moreover, our software has undergone rigorous testing and validation through simulations, showcasing its prowess in selecting optimal routes and minimizing time consumption, thereby enhancing resource efficiency in package delivery. Real-world validation from industries has further affirmed its efficacy.”
The explosion of e-commerce in recent years has caused a rise in local traffic and “last-mile” deliveries. Vehicular routing problem (VRP) research seeks to solve many of the traffic-related frustrations present in our cities, including congestion. Effective pathfinding software for vehicles could dramatically reduce congestion and help commuters and businesses save time and money.
In 2022, the average US driver lost about 51 hours to congestion. In 2023, JD Power observed a 60 percent increase in gas prices over the previous year and estimated that Americans spend $5,000 per year on gas, or about 2.24 percent of their monthly income.
Moving vehicles, goods, and people from Point A to Point B is an incredibly complex endeavor. Computer systems with the right training and real-time data can cut through the noise to help vehicle navigation systems choose optimal routes that waste less time and gas. The authors conclude that their enhanced ACO-GA method is “not just relevant but also ahead of the curve” thanks to its ability to receive additional refinement over time.
This is a significant step forward for the efficiency of our supply chains and the sustainability of logistics and delivery services. As the world grapples with return-to-office orders, expanding home shopping, and rising demand for reverse logistics (customer returns), technology has a significant role to play in keeping our supply chains lean and efficient, and making our cities a little friendlier for motorists and pedestrians.
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