Jul 2026· International Journal on Robotics Automation and Sciences· Vol 8, pp. 99· 0 citations· 13 references
TL;DR
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.
Abstract
The study investigates the use of data-driven optimization strategies to increase routing efficiency in large-scale transportation systems with realistic constraints on operation. The objectives are to combine network cost data with advanced optimization techniques, assess routing performance under capacity, time, and cost constraints. It also compares traditional shortest-path methods to heuristic and metaheuristic approaches. A computational framework was created to compare several algorithms on a large-scale transportation dataset while accounting for real-world constraints such as vehicle capacity and travel costs. Performance was evaluated using total routing cost and route efficiency. The findings demonstrate that metaheuristic techniques consistently outperform traditional algorithms in complicated, constraint-rich situations. Genetic Algorithms, in particular, achieved greater cost reduction and continued to perform well as the problem scale rose. Classical algorithms performed well only in simple circumstances. These findings emphasize the need of cost-effective, data-driven metaheuristic optimization in current logistics planning and encourage future study into hybrid and dynamic routing systems.
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.
Mingfang Song, Kan Lu· Turkish Journal of Agricultu...· 0 citations
Efficient transportation planning requires balancing economic performance, operational efficiency, and environmental responsibility. In real-life transportation systems, transportation planner frequently need to include more than one conflicting factors like minimizing transportation cost, minimizing transportation time, and environmental impact. Traditional single-objective models are not sufficient to handle these complex requirements. Therefore, the need for efficient multi-objective optimization techniques has become increasingly significant. The proposed study focuses to solve a "Multi-Objective Transportation Problem (MOTP)” by different optimization techniques namely, Weighted Sum Method and Goal Programming Method. For each method, the mathematical models have been formulated and solved using secondary data in similar scenarios for fair comparison of methods. For each method, the solutions vary based on the prioritization of objectives. The results show that the Weighted Sum Method gives different results when the weights vary, which means it is sensitive to weight selection. Goal Programming proves to be more flexible and consistent by assigning prioritization and targets. A comparative analysis has been performed by focusing on the ability to find compromise solution, effectiveness of each method, efficiency in getting optimal results and practical applicability of the methods. As seen from the analysis, the multi-objective optimization offers more realistic solution as compared to single-objective optimization techniques. This could help decision makers in transportation and logistic planning.
Aditi Upadhya· International Journal of Inn...· 0 citations
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.
J. Zhang, X. C. Li, S. Zhang et al.· Advanced Electromagnetics· 0 citations
This study investigates the dynamic-demand green vehicle routing problem with soft time windows (DDGVRPSTW). A two-stage optimization model is developed to minimize total distribution cost, including vehicle operating cost, fixed dispatch cost, fuel consumption cost, carbon emission cost, and time window penalty cost. To solve the model, a hybrid artificial bee colony state-transition algorithm (HABC-STA) is proposed. In the pre-optimization stage, multiple initial routes are generated and refined to obtain an initial distribution plan. In the dynamic optimization stage, customer information is updated at a specified event time, and four state-transition operators are used to search the neighborhood of the current solution and generate a revised routing plan with lower cost. Computational results on Solomon benchmark instances and a real-world case study show that the proposed method effectively reduces both total cost and environmental cost. The results also indicate that selecting an appropriate distribution scheme can significantly reduce fuel consumption and carbon emissions while improving overall routing efficiency.
Ming He, Kaijun Zhou, Qian Wang et al.· Electronics· 0 citations
The Vehicle Routing Problem (VRP) and its variants represent some of the most practically consequential optimization challenges in modern logistics and urban mobility. In this study, we address a dynamic, online variant combining elements of the VRP and the Orienteering Problem (OP), in which a fleet of vehicles must maximise cumulative reward collected within a fixed time horizon while continuously replanning as new tasks arrive. We propose and evaluate a reward-density heuristic for dynamic multi-vehicle assignment, referred to as the Efficiency heuristic. We evaluate this formulation across two application domains: autonomous drone task allocation and urban taxi dispatch, across multiple fleet sizes and task scales. The proposed method is compared with four classical construction heuristics and three metaheuristic algorithms (Adaptive Large Neighbourhood Search, Genetic Algorithm, and Simulated Annealing), all evaluated under identical conditions. Across all tested configurations, the Efficiency heuristic matches the solution quality of the best metaheuristic algorithms while requiring two to three orders of magnitude less planning time, establishing Pareto dominance over all competing methods on the reward-versus-compute frontier. These findings suggest a practical design principle for real-time allocation and dispatch systems: in dynamic, time-constrained routing environments, carefully designed greedy heuristics can match the output of sophisticated search procedures at a fraction of the computational cost, making them preferable for online deployment.
The growing adoption of electric vehicles in urban logistics has increased the need for routing models that jointly address operational efficiency and environmental impact. This paper studies a Sustainable Electric Vehicle Routing Problem with Time Windows, which extends the classical EVRPTW by integrating mixed time windows with penalties and carbon-emission costs, while restricting each charging station to at most one visit per route and prohibiting depot-to-station and station-to-station movements. To solve this NP-hard problem, two greedy-based hybrid metaheuristics are proposed, Greedy Simulated Annealing (GSSA) and Greedy Variable Neighborhood Search (GSVNS). Computational experiments on 76 benchmark instances show that greedy-based hybridization significantly improves the baseline methods. GSSA achieves an average total-cost reduction of 49.48% over SA, while GSVNS improves VNS by 11.49% in total cost, 16.89% in distance, and 19.52% in fleet size. In addition, GSVNS attains the best total cost on 80.26% of the tested instances, confirming its effectiveness, particularly on large-scale instances.
Mohamed Amine Mourou, Mariem Belhor, N. Hamani· International Conference on...· 0 citations