A novel genetic programming algorithm is proposed that is Hybrid Crossover Genetic Programming featuring two distinct crossover operators to tackle the dynamic vehicle routing problem with drone and time window constraints and is highly effective, achieving fast convergence and consistently outperforming the existing methods.
The Two-Echelon Vehicle Routing Problem with Drones (2E VRP-D) model can initiate flights from the truck, complete several deliveries to different customer locations, and then rendezvous with the truck again. In addition to economic benefits, logistics providers must consider the environmental impacts of the order-fulfillment process. A novel multi-objective optimization framework is established in this study to simultaneously minimize the total time required for truck travel while also reducing total carbon emissions. Due to restrictions in payload capacity and battery energy limits, drones need to work alongside trucks to deliver services effectively. A dynamic energy consumption model is applied for the drone, where energy use changes based on the loading rate, enabling a more realistic representation of actual operations. This complex problem is addressed using the Non-Dominated Sorting Genetic Algorithm (NSGA-II) with two approaches: the Giant Chromosome (GC) and K-means methods. Routing plans for both trucks and drones are then constructed using a novel heuristic algorithm. Overall, the K-means method delivers better average objective values, reflecting enhanced exploitation performance. Conversely, the GC method produces a higher Hypervolume (HV), indicating superior convergence and coverage of the Pareto front, supported by a lower spacing value, while K-means achieves a slightly better spread. These outcomes contribute to improving logistics operations and informing government policy decisions.
Santoso Santoso, Nurhadi Siswanto, B. Santosa et al.· Engineering, Technology &...· 0 citations
With the continued electrification and digitalization of urban logistics, electric freight routing increasingly requires the coordinated consideration of customer time windows, vehicle capacity, limited battery range, and en-route charging. This study formulates an electric vehicle routing problem with time windows (EVRPTW) for smart-city electric freight and develops a multi-strategy improved ant colony optimization algorithm (IACO). The proposed model integrates customer service, route continuity, time windows, vehicle capacity, battery-energy propagation, and en-route charging. IACO combines a route–charging-state representation with feasibility-guided sweep-insertion initialization, max–min pheromone control, multi-representative guidance, reachable charging-station insertion, greedy feasibility repair, and 2-opt local search, forming a multi-stage search process that integrates global exploration, feasibility restoration, and local intensification. Computational experiments on an R-C benchmark scenario with 51 customers and 9 charging stations compare IACO with ACO, GA, TS, LNS, SA, PSO, and WOA over 100 independent runs under a common 300-iteration limit. Under the current experimental protocol, IACO records a representative generalized cost of 570.88, with reductions of 7.75–44.65% relative to the seven comparison methods, while its median CPU time is 28.42 s. These results demonstrate a clear solution-quality–computation trade-off and indicate the potential of IACO for plan-level electric freight routing and en-route charging coordination.
Li-Ping Gao, Zhao-Lei He, Cong Lin et al.· Energies· 0 citations
The rise in the usage of Electric Vehicles (EVs) in logistics has introduced several routing challenges, which mainly include constraints related to battery capacity, vehicle load capacity, and the availability of recharging infrastructure for delivering the goods by the electric vehicle. These factors can make the Electric Vehicle Routing Problem (EVRP) significantly much more complex than classical vehicle routing problems. Most of the conventional evolutionary algorithms often include fixed-weight factors to impose penalties for violating constraints. However, this approach may lead to poor feasibility and slower convergence. This study proposes the usage of various adaptive forms of penalty weight adjustment strategies for solving the Electric Vehicle Routing Problem using Priority-based Travelling Salesman Problem (TSP)-style evolutionary search. This proposed method dynamically adjusts penalty weights based on constraint violation patterns within the population, to improve balance between solution feasibility and constraint satisfaction. The results obtained on the Standard Goeke benchmark instances demonstrate that the proposed adaptive methods can achieve better improvement in the hypervolume results, faster convergence, as well as the feasibility of the solutions obtained.
Mahankali Prathyusha Lahari, Jeyakumar G· International Conference Com...· 0 citations
Truck–drone collaborative delivery combines the carrying capacity and operational reliability of a ground vehicle with the point-to-point mobility of an unmanned aerial vehicle. This paper studies an adjacent-sortie variant of the single-truck single-drone routing problem on Euclidean instances, where the objective is to minimize the synchronized completion time. For a fixed customer permutation, feasible drone insertions are selected by a dynamic-programming decoder under endurance and non-conflict constraints. On this basis, a Memetic Adaptive Genetic Algorithm with Drone Insertion, denoted by MAGA-DI, is developed. The algorithm uses randomized nearest-neighbor initialization, adaptive mutation, and decoder-aware local search. In the local search phase, swap, insertion, and segment-reversal moves are evaluated by the same drone insertion decoder used for final solution evaluation, so local improvement is guided by synchronized truck–drone makespan rather than by truck-only distance. On five instances per size and ten independent runs per stochastic method, MAGA-DI reduces the mean makespan of the adaptive GA baseline AGA-DI by 3.35 percent, 5.11 percent, and 4.86 percent for 20-, 50-, and 100-customer instances, respectively. The added local search increases runtime, but the average runtime remains below three seconds for 100-customer instances in the tested setting.
Si-Ping Huang, Zeng-Xin Chen, Peng-Bo Jiao et al.· 2026 12th International Conf...· 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
Truck–UAV collaborative delivery can improve last-mile logistics efficiency, but fixed-node rendezvous often causes waiting loss and service delay. To address this problem, this paper proposes a route optimization method integrating en route synchronization, pseudo-node insertion, and GAT-PPO. Pseudo-nodes are generated along truck travel arcs to provide flexible UAV recovery points, and a time-recursive simulation model is developed to evaluate makespan and total tardiness under soft time windows. In the proposed framework, GAT is used to capture spatial–temporal relationships among nodes, while PPO supports sequential routing decisions and UAV dispatch coordination. Experiments on Solomon VRPTW instances with clustered, random, and mixed customer distributions show that GAT-PPO achieves the shortest total travel distance, the lowest total tardiness, and the shortest completion time among Random, NN, NN+2-opt, MLP-PPO, ALNS, GA, and VNS. Ablation results further confirm the contributions of GAT, PPO, pseudo-node insertion, en route synchronization, and UAV collaboration. The results indicate that the proposed framework can effectively reduce synchronization waiting loss and improve the temporal efficiency of truck–UAV collaborative delivery.
Shu-Kang Zheng, Genhua Ma, Hanpei Yang et al.· Applied Sciences· 0 citations
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