| چکیده انگلیسی مقاله |
With the rapid growth and development of urban areas, the demand for secure and efficient transportation systems in urban logistics has become increasingly critical. One of the most pressing challenges in this domain is the routing of bank cash-in-transit (CIT) vehicles, which, due to their sensitive and high-risk nature, require precise and intelligent planning. The primary challenges for CIT vehicles include ensuring security, optimizing timing, managing traffic congestion, and selecting the most efficient routes. This paper proposes an innovative method for the intelligent routing of CIT vehicles by mapping the urban routing problem to the Traveling Salesman Problem (TSP). The proposed approach leverages the Ant Colony Optimization (ACO) algorithm, enhanced with real-world constraints such as heavy traffic, low-security areas, and road hazards, which are incorporated as additional weights in the optimization process. The data used in this study includes the urban traffic map of Shiraz, Iran, and the locations of various banks.
The results demonstrate that the proposed method effectively selects routes that avoid high-traffic zones, crime-prone areas, and hazardous roads while optimizing travel time. This approach not only enhances security and operational efficiency but also contributes to reducing operational costs. The core innovation of this research lies in its ability to map the urban routing problem to the TSP, a well-known combinatorial optimization problem, and to utilize the ACO algorithm, which is inspired by the foraging behavior of ants. In nature, ants leave pheromone trails to communicate and find the shortest path between their nest and food sources. Similarly, the ACO algorithm employs artificial ants to explore possible routes, leaving virtual pheromones to guide subsequent ants toward optimal paths. In this study, the ACO algorithm is further enhanced by incorporating heuristic information such as traffic volume, security rates, and unsafe driving conditions, which are treated as critical factors in the routing process.
The implementation of the proposed method utilizes real-world data from the urban traffic map of Shiraz, including the locations of eight major banks and the routes connecting them. The distances between these locations are calculated using the Haversine formula, which accounts for the Earth's curvature to provide accurate geographical distances. The algorithm is tested with various parameters, including different numbers of artificial ants (ranging from 10 to 200), evaporation rates (0.1 to 0.5), and exploration-exploitation trade-offs (alpha and beta values). The results show that the proposed method can effectively identify routes that minimize travel time while avoiding high-traffic areas, crime-prone zones, and hazardous roads.
One of the key contributions of this research is the integration of multiple heuristic factors into the ACO algorithm. Traditional routing algorithms often focus solely on minimizing distance or travel time, neglecting critical real-world constraints. In contrast, the proposed method assigns weights to factors such as traffic volume, security levels, and unsafe driving conditions, allowing the algorithm to prioritize safer and more efficient routes. For example, routes passing through areas with high crime rates or heavy traffic are penalized, reducing their likelihood of being selected. This approach ensures that the final route is not only the shortest but also the safest and most reliable.
Comparative evaluations indicate that the proposed algorithm offers a more realistic and comprehensive solution compared to other models. By balancing multifaceted aspects of routing such as safety, timeliness, and cost, the method proves to be highly effective. The algorithm's ability to avoid routes with heavy traffic, low security, and poor road conditions significantly enhances the safety and time efficiency of CIT vehicles. Furthermore, the proposed method represents a significant step forward in improving the efficiency and security of banking operations by providing an innovative approach to intelligent routing for CIT vehicles. By mapping the urban routing problem to the TSP and utilizing the ACO algorithm with real-world constraints, the method delivers optimal and secure routes.
In comparison to other studies, the proposed method demonstrates superior performance in terms of accuracy, execution time, energy consumption, and security. The use of real-time traffic data and the algorithm's ability to adapt to dynamic changes further enhance the practicality and reliability of the proposed solution. This adaptability makes the method particularly suitable for urban environments with fluctuating traffic patterns and evolving security challenges.
In conclusion, this paper presents a novel and effective approach to intelligent routing for CIT vehicles in urban environments. By combining the strengths of the ACO algorithm with real-world constraints, the proposed method offers a comprehensive solution that balances efficiency, security, and reliability. Future work could explore the integration of machine learning techniques to further enhance the algorithm's predictive capabilities, enabling it to anticipate and respond to emerging traffic and security challenges proactively. This research marks a significant advancement in the field of urban logistics, providing a robust framework for the safe and efficient routing of high-risk transportation systems. |