Dynamic Logistics Path Optimization via Integrated Ant Colony Optimization and Reinforcement Learning
Abstract
Logistics path planning plays a critical role in improving the efficiency and cost-effectiveness of distribution systems, especially under dynamic traffic conditions. This paper proposes a hybrid path optimization model that combines Ant Colony Optimization (ACO) with Deep Reinforcement Learning (RL), specifically a Deep Q-Network (DQN), to address the limitations of traditional static planning algorithms. The model integrates real-time traffic conditions and historical logistics data into a dynamic directed graph structure. ACO is first used to generate high-quality initial paths, which are encoded to initialize the RL environment and guide early exploration. As the vehicle navigates, real-time traffic fluctuations such as congestion and road closures trigger immediate re-optimization via the RL agent and adaptive pheromone updates in ACO. The model is evaluated using a real-world logistics dataset with 30 customer nodes under time window constraints and varying dynamic scenarios. Experimental results demonstrate that the proposed method reduces average delivery route length from 56.9 km to 52.3 km and lowers fuel and operational costs by 27%, while also achieving 100% punctuality. These findings validate the model’s effectiveness, robustness, and potential for deployment in intelligent logistics distribution systems.References
References
Perkumiene D, Osamede A, Andriukaitienė R, et al. The impact of COVID-19 on the transportation and logistics industry[J]. Problems and perspectives in management, 2021, 19(4): 458.
Chung S H. Applications of smart technologies in logistics and transport: A review[J]. Transportation Research Part E: Logistics and Transportation Review, 2021, 153: 102455.
Zheng K, Zhang Z, Song B. E-commerce logistics distribution mode in big-data context: A case analysis of JD. COM[J]. Industrial Marketing Management, 2020, 86(1): 154-162.
Winkelhaus S, Grosse E H. Logistics 4.0: a systematic review towards a new logistics system[J]. International Journal of Production Research, 2020, 58(1): 18-43.
Hu W C, Wu H T, Cho H H, et al. Optimal route planning system for logistics vehicles based on artificial intelligence[J]. Journal of Internet Technology, 2020, 21(3): 757-764.
Teng S. Route planning method for cross-border e-commerce logistics of agricultural products based on recurrent neural network[J]. Soft Computing, 2021, 25(18): 12107-12116.
Brem A, Giones F, Werle M. The AI digital revolution in innovation: A conceptual framework of artificial intelligence technologies for the management of innovation[J]. IEEE Transactions on Engineering Management, 2021, 70(2): 770-776.
Katoch S, Chauhan S S, Kumar V. A review on genetic algorithm: past, present, and future[J]. Multimedia tools and applications, 2021, 80: 8091-8126.
Shami T M, El-Saleh A A, Alswaitti M, et al. Particle swarm optimization: A comprehensive survey[J]. Ieee Access, 2022, 10: 10031-10061.
Kirkpatrick S, Gelatt Jr C D, Vecchi M P. Optimization by simulated annealing[J]. science, 1983, 220(4598): 671-680.
Wu L, Huang X, Cui J, et al. Modified adaptive ant colony optimization algorithm and its application for solving path planning of mobile robot[J]. Expert Systems with Applications, 2023, 215: 119410.
Pan Y, Yang Y, Li W. A deep learning trained by genetic algorithm to improve the efficiency of path planning for data collection with multi-UAV[J]. Ieee Access, 2021, 9: 7994-8005.
Lakshmanan A K, Mohan R E, Ramalingam B, et al. Complete coverage path planning using reinforcement learning for tetromino based cleaning and maintenance robot[J]. Automation in Construction, 2020, 112: 103078.
Shi K, Wu Z, Jiang B, et al. Dynamic path planning of mobile robot based on improved simulated annealing algorithm[J]. Journal of the Franklin Institute, 2023, 360(6): 4378-4398.
Kumar S, Parhi D R, Kashyap A K, et al. Static and dynamic path optimization of multiple mobile robot using hybridized fuzzy logic-whale optimization algorithm[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2021, 235(21): 5718-5735.
Samir M, Assi C, Sharafeddine S, et al. Age of information aware trajectory planning of UAVs in intelligent transportation systems: A deep learning approach[J]. IEEE Transactions on Vehicular Technology, 2020, 69(11): 12382-12395.
Yu Z, Si Z, Li X, et al. A novel hybrid particle swarm optimization algorithm for path planning of UAVs[J]. IEEE Internet of Things Journal, 2022, 9(22): 22547-22558.
Ajeil F H, Ibraheem I K, Sahib M A, et al. Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm[J]. Applied Soft Computing, 2020, 89: 106076.
Wang M, Ma T, Li G, et al. Ant colony optimization with an improved pheromone model for solving MTSP with capacity and time window constraint[J]. IEEE Access, 2020, 8: 106872-106879.
Pan H, You X, Liu S, et al. Pearson correlation coefficient-based pheromone refactoring mechanism for multi-colony ant colony optimization[J]. Applied Intelligence, 2021, 51: 752-774.
Du P, Liu N, Zhang H, et al. An improved ant colony optimization based on an adaptive heuristic factor for the traveling salesman problem[J]. Journal of Advanced Transportation, 2021, 2021(1): 6642009.
Liu C, Wu L, Xiao W, et al. An improved heuristic mechanism ant colony optimization algorithm for solving path planning[J]. Knowledge-based systems, 2023, 271: 110540.
Zhao Y, Wang Y, Tan Y, et al. Dynamic jobshop scheduling algorithm based on deep Q network[J]. Ieee Access, 2021, 9: 122995-123011.
Shi D, Xu H, Wang S, et al. Deep reinforcement learning based adaptive energy management for plug-in hybrid electric vehicle with double deep Q-network[J]. Energy, 2024, 305: 132402.
Wang Y, Liu H, Zheng W, et al. Multi-objective workflow scheduling with deep-Q-network-based multi-agent reinforcement learning[J]. IEEE access, 2019, 7: 39974-39982.
Eschmann J. Reward function design in reinforcement learning[J]. Reinforcement Learning Algorithms: Analysis and Applications, 2021: 25-33.
Chandriah K K, Naraganahalli R V. RNN/LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting[J]. Multimedia Tools and Applications, 2021, 80(17): 26145-26159.
Noor M N, Yahaya A S, Ramli N A, et al. Filling missing data using interpolation methods: Study on the effect of fitting distribution[J]. Key Engineering Materials, 2014, 594: 889-895.
Deng Y, Chen Y, Zhang Y, et al. Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment[J]. Applied Soft Computing, 2012, 12(3): 1231-1237.
Tang G, Tang C, Claramunt C, et al. Geometric A-star algorithm: An improved A-star algorithm for AGV path planning in a port environment[J]. IEEE access, 2021, 9: 59196-59210.
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https://doi.org/10.31449/inf.v49i6.9224Downloads
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