Aug 2026· Advanced Electromagnetics· Vol 15, pp. 788-796· 0 citations
TL;DR
This study investigates logistics path planning cost control through an optimized Ant Colony Algorithm driven by highway enterprise operational data and provides an effective engineering solution for intelligent logistics management, transportation optimization, and data-driven supply chain operations.
Abstract
This study investigates logistics path planning cost control through an optimized Ant Colony Algorithm (ACA) driven by highway enterprise operational data. Real-time transportation information, including traffic flow, vehicle speed, and road condition data, is integrated into pheromone update mechanisms and heuristic factor adjustments to enhance the adaptability of the algorithm in dynamic logistics environments. A data-driven optimization framework is developed to support intelligent route selection under continuously changing traffic conditions. Case-study results demonstrate that the proposed method significantly improves route planning efficiency and logistics cost control performance. Compared with traditional experience-based planning methods and the standard ACA, the optimized approach reduces total logistics distribution costs by 26.45%, transportation costs by 31.2%, and average travel distance by 12.12%. The framework exhibits strong robustness, adaptability, and operational efficiency in large-scale logistics networks. The proposed methodology is particularly applicable to intelligent transportation systems supported by wireless communication infrastructures and antenna-enabled sensing networks, where reliable real-time data acquisition and low-latency information transmission are essential for dynamic route optimization and operational decision-making. This research provides an effective engineering solution for intelligent logistics management, transportation optimization, and data-driven supply chain operations.
With the rapid advancement of agricultural modernization and the increasing demand for agricultural products, inefficient logistics distribution has become a major bottleneck in rural supply chains. This study addresses the capacitated vehicle routing problem (CVRP) in agricultural logistics. A genetic algorithm (GA)-based optimization model was proposed to enhance distribution efficiency. The model integrates critical agricultural characteristics, including multidistribution center networks, seasonal delivery schedules, and regional road infrastructure constraints, to minimize both transportation distance and operational costs. Experimental results show that the GA outperforms traditional metaheuristic methods (e.g., particle swarm optimization and simulated annealing), achieving a >5 km reduction in total delivery distance, an 11% decrease in delivery time, and a 5% reduction in path distance compared to conventional planning approaches. Notably, the hybrid GA-CVRP framework converges faster and achieves higher cost efficiency, with empirical tests validating its ability to optimize route planning under complex rural conditions. This research provides a robust, data-driven solution for agricultural enterprises to enhance supply chain resilience, reduce carbon footprints, and support sustainable rural development. By bridging AI-driven optimization and agricultural logistics practices, the study offers practical insights for deploying intelligent routing systems in global rural contexts.
Ming-Fang Song, Kan Lu· Turkish Journal of Agricultu...· 0 citations
The findings demonstrate that metaheuristic techniques consistently outperform traditional algorithms in complicated, constraint-rich situations and emphasize the need of cost-effective, data-driven metaheuristic optimization in current logistics planning.
K. Khaw, C. Tan· International Journal on Rob...· 0 citations
Simulation experiments demonstrate that SW-MSACO achieves improved Pareto solution quality and search stability compared with existing heuristic optimization approaches, particularly under large-scale and high-load scenarios, confirming the effectiveness of the proposed framework for complex UAV logistics optimization.
Xin-Yi Chen, Lin Shi, Jian-Yu Li· Algorithms· 0 citations
Cloud task scheduling is a critical component of cloud computing systems because it directly affects resource allocation, workload distribution, execution efficiency, and service cost. However, many metaheuristic algorithms suffer from population diversity loss, premature convergence, and an inadequate balance between global exploration and local exploitation when solving complex and large-scale optimization problems. To address these limitations, this study develops an Enhanced Human Urbanization Algorithm (EHUA) for numerical optimization and cloud task scheduling. Inspired by the collaborative resource-allocation behavior of modern logistics networks, three coordinated mechanisms are reformulated within the adventurer–city–citizen structure of the original Human Urbanization Algorithm: a logistics-hub-guided adaptive exploration mechanism, a supply–demand-based dynamic redistribution mechanism, and a cooperative logistics delivery exploitation mechanism. These mechanisms reduce excessive dependence on a single capital, adaptively regulate city search ranges, and strengthen citizen-level solution refinement. The performance of EHUA is evaluated on the CEC2014 and CEC2020 benchmark suites using convergence analysis, box plots, numerical statistics, Wilcoxon signed-rank tests, Friedman rankings, and ablation experiments. EHUA obtains the best mean fitness values on 20 of the 30 CEC2014 functions under both 30- and 50-dimensional settings, on 8 of the 10 CEC2020 functions at 10 dimensions, and on all 10 functions at 20 dimensions, demonstrating strong overall competitiveness and repeatability without implying universal superiority on every problem. EHUA is further applied to cloud task scheduling under workload scales ranging from 100 to 10,000 tasks. Considering comprehensive cost, monetary cost, execution time, and load cost, the proposed method consistently achieves low comprehensive scheduling costs and maintains favorable trade-offs among individual objectives as the workload increases. These results indicate that EHUA provides an effective and scalable optimization framework for complex benchmark problems and cloud task scheduling applications.
This study investigates the integration of UAVs as an alternative transportation mode within industrial environments, focusing on the rapid delivery of critical spare parts in large-scale production facilities, and highlights the role of UAVs as a complementary transportation layer in controlled industrial networks.
Konstantinos Kolonas, S. Ponis, Michalis Fragkoulakis et al.· Future Transportation· 0 citations
To address urban traffic congestion, low vehicle throughput, and high energy consumption, a vehicle cooperative planning method based on global traffic conditions is proposed. Combining the spatiotemporal evolution characteristics of traffic conditions, a multi-objective vehicle cooperative planning model considering traffic flow density, throughput, and energy consumption is constructed. An improved particle swarm optimization algorithm is designed to solve the model, addressing its multi-constraint and multiobjective characteristics, thereby improving optimization efficiency and solution optimality. To verify the effectiveness and innovation of the method, multi-scenario experiments are constructed based on the SUMO traffic simulation platform, and a comprehensive comparison is made with the traditional Dijkstra's path planning method (TPM) and the cooperative planning method based on local traffic information (LCPM). Simulation experiments show that this method significantly improves average travel time, road network throughput, and vehicle energy consumption compared to traditional planning methods, and exhibits stronger adaptability under high congestion conditions. The research results provide a new technical approach for improving the operational efficiency of urban road networks and alleviating traffic congestion.
Shucong Yu, Yibo Zhou· The 2026 International Confe...· 0 citations