Greedy-Based Hybrid Metaheuristics for the Sustainable Electric Vehicle Routing Problem

Energy-Constrained Electric Vehicle Fleet Routing with Charging Station Insertion Using Metaheuristic Optimization

The rapid transition toward sustainable electrified transportation has led to the urgent deployment of electric vehicle (EV) fleets in urban logistics, where limited battery capacity and the need for en-route charging introduce new challenges for EV routing optimization. The final route, travel time, and operational cost are directly affected by the inclusion of charging stations in the routing objective. Unlike conventional vehicle routing problems (VRPs), the electric VRP (EVRP) requires energy feasibility constraints to be tightly integrated with routing decisions. In this study, we introduce a scalable, simulation-based EVRP framework that explicitly incorporates charging-station insertion into a metaheuristic optimization solver. A particle swarm optimization (PSO) approach is adopted to address the combinatorial complexity of large-scale instances, while feasibility is enforced through state-of-charge tracking and adaptive charging decisions. The proposed framework is evaluated and compared with a heuristic baseline algorithm (BR-TSP) under multiple simulation scenarios. Results demonstrate that the proposed approach is fully feasible across all tested scenarios, significantly outperforms classical exact solvers in scalability, and highlights the critical role of explicit charging integration in realistic EV fleet operations. The findings reveal practical strategies for energy-aware routing in large-scale electric mobility applications.

Reward-Density Heuristic for Dynamic Multi-Vehicle Routing: Performance and Computational Efficiency

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.

Comparative Optimization and Heuristic Approaches for Electric Vehicle Charging Schedule Management

Towards Carbon-Efficient Urban Logistics: A Constructive Routing Framework for Heterogeneous Courier Fleets

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.

Genetic algorithm for agricultural logistics optimization: solving the capacitated vehicle routing problem in rural distribution

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.

Data-Driven Route Optimization for Large-Scale Transportation Systems

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.