Aug 2026· Turkish Journal of Agriculture and Forestry Sciences· 0 citations
Abstract
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.
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
This paper addresses the Capacitated Coverage Path Planning Problem (CPP) arising in agricultural field operations. It aims to determine an efficient sequence of field tracks to be serviced by an agricultural machine subject to limited onboard capacity and refilling constraints, while minimizing non-working distance. The non-working distance is the length traveled by the machinery not performing productive fieldwork. Optimizing how agricultural machinery maneuvers can help reduce operational costs and greenhouse gas (GHG) emissions, while increasing productivity. The problem is formulated as a variant of the constrained Vehicle Routing Problem (VRP), which is NP-hard. A novel exact Integer Linear Programming (ILP) formulation is proposed. We employ a commercial optimizer, Gurobi, to obtain optimal reference solutions for smaller instances. To overcome scalability limitations, a metaheuristic based on "simulated annealing (SA) methodology" is proposed. Our algorithm incorporates customized neighborhood search operators and a capacity-aware route-splitting mechanism to explicitly handle refilling operations. Computational experiments are conducted on real-world field instances. The proposed metaheuristic is evaluated against the exact formulation. The results indicate that SA consistently produces near-optimal solutions with substantially reduced computation times.
Fabliha Zahin, Shahadat Hossain· Annual Conference on Genetic...· 0 citations
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.
J.-G. Zhang, X.-C. Li, S. Zhang et al.· Advanced Electromagnetics· 0 citations
The increasing demand for urban last-mile delivery services has intensified the need for routing approaches that simultaneously address operational efficiency and environmental sustainability. This study introduces the Green Multi-Courier Delivery Routing Problem (GMCDRP), a heterogeneous routing and assignment problem involving pedestrian couriers, electric bicycles, and motorized vehicles under capacity, distance, and service-time constraints. To solve the problem, a state-aware constructive heuristic named Emission-Minimizing Green Routing (EMGRO) is proposed. Unlike conventional metaheuristics that evaluate emissions after route generation, EMGRO integrates emission awareness directly into the assignment process by considering the real-time operational state of each courier and prioritizing the lowest-emission feasible alternative. The proposed method is evaluated using 20 large-scale scenarios derived from the real road network of Adana, Türkiye, each containing up to 1000 delivery requests. Its performance is compared with Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) approaches. Experimental results demonstrate that EMGRO achieves the lowest average emission per delivered package (0.512 g CO2/package), outperforming ACO, PSO, and GA by 47.9%, 44.1%, and 41.9%, respectively, while maintaining identical delivery coverage. Furthermore, EMGRO generates solutions within seconds, providing substantial computational advantages over population-based metaheuristics. The findings indicate that embedding environmental considerations directly into the decision-making process can significantly improve both sustainability and computational efficiency in heterogeneous urban delivery systems.
With the rapid expansion of fresh food e-commerce and urban instant retail, urban fresh logistics has become a core component of modern urban circulation system. Different from general commodity logistics, fresh cold chain distribution has strict requirements on delivery time, transportation temperature and service quality, which also brings problems such as repeated vehicle routes, high energy consumption and excessive carbon emissions in actual operation. Traditional fresh logistics path optimization mostly takes the shortest distance or the lowest transportation cost as the single optimization goal, ignoring the low-carbon development requirements under the dual-carbon policy, and the classic optimization algorithm is prone to local optimal solutions and slow convergence speed in complex urban traffic scenarios, resulting in poor practicability of optimization results. Aiming at the above pain points, this paper constructs a multi-objective optimization model of urban fresh logistics low-carbon distribution, which takes total distribution cost and total carbon emission as dual optimization objectives, and sets multi-dimensional constraints including vehicle load limit, customer time window and driving speed limitation. On this basis, an improved ant colony algorithm is proposed by optimizing the pheromone update mechanism and heuristic function weight, which effectively makes up for the defects of the traditional ant colony algorithm. Finally, simulation example analysis is carried out with urban fresh distribution scene data. The results show that compared with the traditional algorithm optimization scheme, the improved algorithm can reduce the total distribution cost by 8.2% and the total carbon emission by 11.5%, and significantly improve the convergence efficiency and path rationality. This research realizes the coordinated optimization of economic benefit and environmental benefit of fresh logistics distribution, and can provide effective theoretical support and practical reference for the intelligent and low-carbon transformation of urban fresh logistics enterprises.
Fang Qi· Frontiers in Business, Econo...· 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.