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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>ICRT: Intent-Driven Cross-Domain Routing Design for Task-Oriented Heterogeneous FANETs</title>
      <link>https://trid.trb.org/View/2781039</link>
      <description><![CDATA[Task-oriented heterogeneous Flying Ad Hoc Networks (FANETs) are emerging as network paradigms to meet increasingly complex task requirements. However, conventional cross-domain routing protocols face challenges such as heavy address matching, high routing overhead, and a lack of task-adaptive capabilities. We propose an intent-driven cross-domain routing framework, termed ICRT. The ICRT framework explores and exploits typical capabilities of intent-driven networks and named data networks to decouple the task control plane from the network forwarding plane to achieve routing flexibility. Furthermore, we propose a novel ICRT protocol to assign appropriate cross-domain forwarding routes to different services, ensuring that the transmission links of the services are adapted to the business requirements. Finally, we construct an emulation platform to validate the feasibility and efficiency of the presented ICRT framework and protocol. Simulation results show that the ICRT protocol outperforms the conventional FANET routing protocol, achieving a 350 ms reduction in end-to-end delay, an 11.8% increase in packet delivery ratio, a 1.5 Mbps improvement in network throughput, and a 10% reduction in routing overhead. It also ensures that the differences between all service requirements and link metrics remain within the order of 10-4.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2781039</guid>
    </item>
    <item>
      <title>A Dynamic Unmanned Aerial Vehicle Routing Framework for Urban Traffic Monitoring</title>
      <link>https://trid.trb.org/View/2780538</link>
      <description><![CDATA[Unmanned Aerial Vehicles (UAVs) have great potential in urban traffic monitoring. However, their limited flight duration presents critical challenges in sustainable recharging strategies and efficient route planning in long-term monitoring tasks. Additionally, existing approaches for long-term monitoring often neglect the dynamic and evolving nature of urban traffic networks. In this study, we introduce a novel dynamic UAV routing framework for long-term, network-wide urban traffic monitoring, leveraging existing ground vehicles (GVs), such as public transit buses, as mobile charging stations without disrupting their operations. To address the complexity of long-term monitoring scenarios involving multiple flights, we decompose the problem into manageable single-flight tasks, in which each flight is modeled as a Team Arc Orienteering Problem with Decreasing Profits (TAOP-DP) with the objective to collectively maximize the spatiotemporal network coverage. Between flights, we adaptively update the edge weights to incorporate real-time traffic changes and revisit intervals. We validate our framework through extensive microscopic simulations in a modified Sioux Falls network under various scenarios, including static and changing demand levels and varying historical information availability. Comparative results demonstrate that our model outperforms three baseline approaches, especially when historical information is incomplete or absent. The scalability and generalizability of the framework are further validated through a portion of the Manhattan network with real world probe vehicle data. Moreover, we show that the proposed monitoring framework can capture network-wide traffic trends and construct accurate Macroscopic Fundamental Diagrams (MFDs), which highlights its potential in network-wide traffic control and management applications.]]></description>
      <pubDate>Fri, 25 Sep 2026 10:51:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780538</guid>
    </item>
    <item>
      <title>Dynamic switching models for truck-only delivery and drone-assisted truck delivery under demand uncertainty</title>
      <link>https://trid.trb.org/View/2717965</link>
      <description><![CDATA[Integrating drones into truck delivery systems holds the potential for transformative improvements in customer accessibility, operational cost reduction, and delivery efficiency. However, this integration comes with costs, including drone procurement, maintenance, and energy consumption. The decision on whether and when to incorporate drones into truck delivery systems is heavily contingent on the demand for these services. In areas where demand is low and dispersed, deploying drone-assisted trucks may lead to the underutilization of resources and financial challenges, primarily due to the substantial upfront costs of drone dep loyment. Accurately predicting future demand density is a complex task, compounded by uncertainties stemming from unforeseen events or infrastructure disruptions. To tackle this challenge, a market entry and exit real option approach has been used to optimize the switching timing between different delivery services (i.e., trucks with or without drones) while considering the stochastic nature of demand density. The results of this study highlight that drone-assisted truck delivery, particularly when multiple drones are deployed per truck, can offer significant economic advantages in regions with high demand for delivery services. Utilizing the proposed dynamic switching model, the deterministic and stochastic approaches reduce costs by 17.4% and 31.3%, respectively, compared to the immediate cost-saving switching model. Furthermore, the stochastic parameters within the real option framework asymmetrically influence the entry and exit timings, as revealed through sensitivity analysis. Then, a stochastic multiple-options model is developed to dynamically switch between truck-only delivery and drone-assisted truck delivery with varying numbers of drones. These proposed stochastic models are applied in the Miami-Dade County region to evaluate the cost of dynamic switching services for three major logistics operators in a real-world scenario. This study illuminates the potential benefits of dynamic switching between different delivery services and provides decision-makers in the logistics industry with valuable insights for optimizing their delivery systems.]]></description>
      <pubDate>Wed, 16 Sep 2026 11:21:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717965</guid>
    </item>
    <item>
      <title>An Exact Algorithm to Solve Vehicle Routing Problem with Drones for Delivery and Surveillance Tasks after Disasters</title>
      <link>https://trid.trb.org/View/2686234</link>
      <description><![CDATA[Disasters cause severe economic and human losses, and delays in rescue efforts can lead to more deaths due to damaged infrastructure. To enhance post-disaster response efficiency, this study proposes a collaborative truck-and-drone system that leverages the high capacity of trucks and the speed and flexibility of drones to overcome road accessibility challenges. Although truck-drone collaborations have been studied in commercial logistics, their application in disaster relief remains understudied, particularly for simultaneous delivery and surveillance tasks, which are critical for saving lives and assessing damage. Unlike cost- or time-focused commercial logistics, humanitarian operations require prioritizing urgency, where tasks with higher mortality risks should be prioritized. This study addresses the truck-and-drone routing problem with flexible collaboration strategies for delivery and surveillance tasks to minimize the priority and penalty costs in the disaster response. We model the problem as a mixed integer linear programming model and develop an exact method based on the branch-and-bound algorithm and the Benders decomposition approach to solve the problem. Several valid inequalities are derived based on problem properties to improve the computational efficiency. Numerical experiments demonstrate that the proposed algorithm outperforms the state-of-the-art solver Gurobi in terms of objective function value, optimality gap, and computational time. The proposed flexible truck-and-drone collaboration can significantly enhance rescue efficiency in disaster response.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:03:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686234</guid>
    </item>
    <item>
      <title>Privacy-Aware Routing for Drone Delivery as a Service</title>
      <link>https://trid.trb.org/View/2672770</link>
      <description><![CDATA[Uncrewed aerial vehicles (UAVs), or drones, are increasingly being used to deliver goods from vendors to customers. In an emerging business model, a drone operator partners with multiple businesses to offer drone delivery as a service. However, this business model poses a privacy risk due to regulations requiring drones to broadcast location information. Third-party observers may leverage broadcast trajectories to link customers to vendors, resulting in potential privacy issues. A probabilistic definition of privacy risk is proposed based on the likelihood that a third-party observer can correctly infer which vendor a customer ordered from. Next, these risks are quantified, and the impacts of order count, drone capacity, decoy stops, and delivery time requirements on privacy are evaluated. Privacy risk mitigation is then formulated as an optimization problem and integrated into the vehicle routing problem. Doing so allows optimization of efficiency while satisfying a privacy constraint. A comparison of the tradeoffs between privacy and efficiency based on the geographical arrangement of vendors and customers is presented. Finally, limitations, including complexity, assumptions, and areas for future work, are discussed.]]></description>
      <pubDate>Wed, 24 Jun 2026 11:29:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672770</guid>
    </item>
    <item>
      <title>Multi-objective optimization of truck-drone cooperative routing problem based on customer classification and fuzzy time windows</title>
      <link>https://trid.trb.org/View/2592386</link>
      <description><![CDATA[With the growing demand for personalized logistics services, the combined use of drones and trucks as collaborative delivery services has become increasingly crucial for improving service levels. A key challenge lies in the rational allocation of limited logistics resources to enhance customer satisfaction. To address this challenge, this study proposes a novel multi-objective optimization model for truck-drone collaborative routing, utilizing customer value classification and fuzzy time window management. First, considering the Pareto principle (also known as the 80/20 rule) of customer profitability, customers are classified into three levels: high, medium, and low based on their current purchase value (CPV) and potential purchase value (PPV). This classification allows for a differentiated delivery strategy: high-level customers receive door-to-door delivery via drones, medium-level customers are served by trucks at designated pickup nodes, and low-level customers are directed to centralized self-pickup locations. Second, to better accommodate customer preferences, flexible time windows are introduced, including desired and tolerated time frames, with varying sensitivity coefficients assigned to different customer levels. Finally, a multi-objective optimization model is constructed to minimize costs and maximize customer satisfaction. To solve this model, a hybrid genetic algorithm-simulated annealing (GA-SA) approach is employed, incorporating dynamic adjustment strategies and a fast, non-dominated sorting algorithm to enhance computational efficiency. Benchmark instances are used to evaluate the proposed algorithm, demonstrating its capability to generate high-quality solutions. Additionally, a real-case study in Chongqing, China, validates the effectiveness of both the proposed model and algorithm. The results indicated that while the costs of truck-drone collaborative delivery were comparable whether or not customer classification was considered, customer satisfaction improved by 22.11% when classification was taken into account. This proves the potential of the proposed delivery strategy to enhance customer satisfaction while optimizing logistics delivery routes. The findings also have practical implications for various supply chains, confirming that integrating our proposed framework can significantly improve customer satisfaction.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:57:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592386</guid>
    </item>
    <item>
      <title>HQA: Hybrid Q-learning and AODV multi-path routing algorithm for Flying Ad-hoc Networks</title>
      <link>https://trid.trb.org/View/2569861</link>
      <description><![CDATA[Reliable and efficient data transmission between Unmanned Aerial Vehicle (UAV) nodes is critical for the control of UAV swarms and relies heavily on effective routing protocols in Flying Ad-hoc Networks (FANETs). However, Q-learning-based FANET routing protocols, which are gaining widespread attention, face two significant challenges: 1) insufficient stability of Q-learning leads to unreliable route selection in certain scenarios and higher packet loss rates; 2) in void regions with frequent topology changes and vast path exploration spaces, the slow convergence of Q-learning fails to adapt quickly to dynamic environmental changes, thereby reducing the packet delivery rate (PDR). This paper proposes a hybrid Q-learning/AODV (HQA) multi-path routing algorithm that integrates Q-learning and the AODV protocols to address these challenges. HQA includes a Bayesian stability evaluator for adaptive Q-learning/AODV switching and a dual-update reward mechanism that integrates reliable AODV paths into Q-learning training, enabling rapid void recovery and latency-optimized routing. Experimental results demonstrate HQA's superiority over baseline protocols: Compared to AODV, HQA reduces average end-to-end delay by 13.6–23.9% and improves PDR by 5.4–9.1% in non-void and void states, respectively. It outperforms QMR by 2.2–6.3% in PDR while achieving 25.6% and 53.2% higher average PDR than QMR and AODV across network densities. The hybrid design accelerates convergence by 40% versus standalone Q-learning through AODV-assisted rewards, maintaining scalability under dynamic topology changes. These findings indicate that the HQA algorithm can more rapidly adapt to the rapid changes in FANETs and better handle void regions, offering a promising solution for enhancing the performance and reliability of FANETs.]]></description>
      <pubDate>Thu, 10 Jul 2025 16:40:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569861</guid>
    </item>
    <item>
      <title>Coordinated last-mile deliveries with trucks and drones: A comparative study of operational modes</title>
      <link>https://trid.trb.org/View/2548169</link>
      <description><![CDATA[Throughout the recent decade, drone delivery technology has been developed to address problems such as last-mile delivery and recurrent traffic jams in metropolitan regions. To resolve the relatively short travel range and limited capacity issues of drones, researchers have proposed to further enhance the utility of drones by deploying them with ground vehicles (e.g., trucks) in tandem. This new concept leads to a new transportation planning problem, i.e., coordinated delivery of trucks and drones (CDTD). In this paper, the authors conduct a comparative study of three representative operational models: the flying sidekick traveling salesman problem (FSTSP), the traveling salesman problem with drone (TSP-D), and the parallel drone scheduling traveling salesman problem (PDSTSP). Metrics including delivery efficiency and vehicle utilization rate are evaluated for the three models under various scenarios, to extract insights on which operational model is advantageous for which scenario. The authors also benchmark the algorithms for CDTD by various metrics including result quality, runtime and scalability, and conduct sensitivity analysis to assess the performance of the system under various system parameters, including drone speed, drone range and customer geographic distribution. Finally, a case study is conducted to demonstrate the superior performance of CDTD model as compared to classical TSP model based on real-world data. Experimental results demonstrate that the FSTSP and TSP-D highlight superior performance over PDSTSP and truck-only TSP when the majority of customers are out of the flight range of drone and the customer demand is clustered, as a result of the synchronization and compensation for limitations between the two types of vehicles.]]></description>
      <pubDate>Thu, 10 Jul 2025 16:38:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548169</guid>
    </item>
    <item>
      <title>A branch-price-and-cut algorithm for the time-dependent multiple truck–drone routing problem</title>
      <link>https://trid.trb.org/View/2554438</link>
      <description><![CDATA[Time-varying traffic conditions are crucial features of urban logistics. Overlooking these conditions will pose a high coordination risk for drone-assisted routing problems. In this paper, a time-dependent multiple truck–drone routing problem (TD-MTDRP), which captures the time-varying traffic conditions as time-dependent travel times, is introduced. In the problem, trucks with time-dependent travel times travel through the urban roads, while drones fly over the urban areas, unaffected by the urban traffic, resulting in time-independent travel times. The authors’ objective is to the total duration of all routes. The authors present the ready time function on a truck–drone path level, which is proven to satisfy the FIFO property. A route-based model is introduced based on this function to formulate the problem. The model decomposes the set-partitioning problem into a master problem (MP) and a time-dependent shortest truck–drone path problem with resource constraints (TDSTDPPRC). A branch-price-and-cut algorithm is also developed to solve the model. In this algorithm, a column-and-row generation is introduced to solve the MP, and a labeling algorithm incorporating new label extension and dominance rules is presented to address the TDSTDPPRC. For the numerical results, the approaches are evaluated on two sets of instances derived from the benchmark instances, demonstrating the effectiveness of the exact method in solving the TD-MTDRP.]]></description>
      <pubDate>Fri, 20 Jun 2025 11:58:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2554438</guid>
    </item>
    <item>
      <title>An Adaptive Bendable Virtual Tunnel Routing Protocol for Flying Ad-Hoc Networks</title>
      <link>https://trid.trb.org/View/2540367</link>
      <description><![CDATA[Flying ad-hoc networks (FANETs) play a crucial role in disaster response, surveillance, and remote sensing. However, their highly dynamic topology and frequent link disruptions pose significant challenges to efficient routing. Existing protocols suffer from excessive control overhead, unstable links, and inefficient energy utilization, that limits their practical deployment. To address these issues, we propose an adaptive bendable virtual tunnel routing protocol (ABVTR), which constructs a three-dimensional (3D) adaptive bendable virtual relay tunnel (3D-ABVRT) using Bézier curves.This tunnel restricts the propagation of route request messages, reducing redundant transmissions, and enhancing routing stability, particularly in sparsely connected UAV networks. ABVTR employs a piecewise function to align UAV movement with the tunnel's centerline, effectively minimizing deviations and reducing delays. Furthermore, it dynamically determines the next-hop node by considering movement direction, relative speed, and residual energy. The latter two factors are assessed using a S-function and an exponential function, respectively, to enhance link reliability and optimize energy distribution. This work advances the state-of-the-art by introducing a more adaptive, energy-efficient, and scalable routing solution for FANETs. The simulation results show that ABVTR significantly outperforms existing protocols (EARVRT, iPipe, HMGOC, CF-GPSR, FM-DT-GDR) in end-to-end delay, packet delivery ratio (PDR), and routing overhead. These enhancements position ABVTR as a highly promising solution for mission-critical FANETs application, enabling more resilient, scalable, and efficient aerial networks.]]></description>
      <pubDate>Wed, 28 May 2025 17:02:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2540367</guid>
    </item>
    <item>
      <title>Exact Solution of the Vehicle Routing Problem with Drones</title>
      <link>https://trid.trb.org/View/2534991</link>
      <description><![CDATA[The vehicle routing problem with drones (VRP-D) that the authors consider is an extension of the capacitated vehicle routing problem in which the fleet consists of trucks equipped with one drone each. A truck and its drone can either move together or separately. A truck can release its drone at the depot or at a customer location and must pick it up later at another customer or the depot location. In this way, both trucks and drones deliver goods to customers working together as synchronized working units. A feasible route has to satisfy the capacity constraints of both the truck and the drone. A feasible solution to the VRP-D is a set of feasible routes such that each customer is served exactly once by either a truck or a drone. The authors investigate two standard objectives considered in the literature, that is, the minimization of the total routing cost and the sum of the routes’ durations. To solve the VRP-D exactly, the authors develop a branch-price-and-cut (BPC) algorithm. In particular, the authors present a new forward and implicit bidirectional labeling algorithm defined over an artificial network to solve the column-generation subproblems. The new bidirectional labeling algorithm substantially accelerates the solution process compared with its monodirectional counterpart. The time needed to solve the pricing problems is reduced by 55% on average when minimizing routing costs and by 30% when minimizing the sum of the routes’ durations. In further computational experiments, the authors analyze algorithmic components of the BPC algorithm, compare the cost and duration objectives, and highlight the impact of the drones’ speed on the structure of VRP-D solutions. For the routing-cost minimization objective, the authors' BPC algorithm is able to solve several VRP-D instances with 50 vertices to proven optimality within one hour of computation time. The same instances with duration minimization are more difficult, and the BPC algorithm provides only heuristic solutions with an average gap not exceeding 3%.]]></description>
      <pubDate>Fri, 02 May 2025 12:07:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534991</guid>
    </item>
    <item>
      <title>QRCF: A new Q-learning-based routing approach using a smart cylindrical filtering system in flying ad hoc networks</title>
      <link>https://trid.trb.org/View/2534479</link>
      <description><![CDATA[To ensure reliable data transmission in flying ad hoc networks (FANETs), efficient routing protocols are necessary to establish communication paths in FANETs. Recently, reinforcement learning (RL), particularly Q-learning, has become a promising approach for overcoming challenges faced by traditional routing protocols due to its capacity for autonomous adaptation and self-learning. This study presents a Q-learning-based routing strategy, enhanced by an innovative cylindrical filtering technique, named QRCF in FANETs. In QRCF, the dissemination interval of hello packets is adaptively adjusted based on the connection status of nearby UAVs. Then, this routing process leverages Q-learning to discover reliable and stable routes, using a state set refined by the cylindrical filtering technique to accelerate the search for the optimal path in the network. Afterward, the reward value is computed using metrics such as relative speed, connection time, residual energy, and movement path. Finally, QRCF is deployed in the network simulator 2 (NS2), and its performance is evaluated against three routing schemes, QRF, QFAN, and QTAR. These evaluations are presented based on the number of UAVs and their speed. In general, when changing the number of nodes, QRCF improves energy usage (about 5.01%), data delivery ratio (approximately 1.20%), delay (17.71%), and network longevity (about 3.21%). However, it has a higher overhead (approximately 10.91%) than QRF. Moreover, when changing the speed of UAVs in the network, QRCF improves energy usage (about 4.94%), data delivery ratio (approximately 2.36%), delay (about 17.5%), and network lifetime (approximately 8.75%). However, it increases routing overhead (approximately 15.47%) in comparison with QRF.]]></description>
      <pubDate>Mon, 28 Apr 2025 08:50:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534479</guid>
    </item>
    <item>
      <title>Computational intelligence-based routing schemes in flying ad-hoc networks (FANETs): A review</title>
      <link>https://trid.trb.org/View/2530077</link>
      <description><![CDATA[Recently, the rapid development of wireless technologies, low-priced equipment, advances in networking protocols, and access to modern communication, electrical, and sensing technologies have led to the evolution of flying ad hoc networks (FANETs). However, the high movement of unmanned aerial vehicles (UAVs) in these networks causes iterated failures of communication links and constant changes in network topology. These features challenge the design of a proper routing protocol in FANETs. Today, computational intelligence (CI) techniques are rapidly developing as a mighty and intelligent computing model. This promising technology can be used to improve various applied areas, especially routing in FANETs. This paper examines and assesses various CI-based routing techniques in FANETs. Accordingly, this paper introduces a classification of CI-based routing protocols for FANETs. This categorization includes three groups: learning system-based routing methods (including artificial neural networks, reinforcement learning, and deep reinforcement learning), fuzzy-based routing schemes, and bio-inspired routing schemes (evolutionary algorithms and swarm intelligence). Subsequently, based on the offered classification, the most recent CI-based routing methods and their key features are outlined. Ultimately, the opportunities and challenges in this area have been mentioned to help researchers familiarize themselves with future research directions in CI-based routing algorithms for FANETs and work toward improving these methods in such networks.]]></description>
      <pubDate>Thu, 17 Apr 2025 16:55:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2530077</guid>
    </item>
    <item>
      <title>An adaptive large neighborhood search method for the drone–truck arc routing problem</title>
      <link>https://trid.trb.org/View/2485440</link>
      <description><![CDATA[For applications such as traffic monitoring, infrastructure inspection, and security, ground vehicles (trucks) and unmanned aerial vehicles (drones) may collaborate to finish the task more efficiently. This paper considers an Arc Routing Problem (ARP) with a mixed fleet of a single truck and multiple homogeneous drones, called a Drone–Truck Arc Routing Problem (DT-ARP). While the truck must follow a road network, the drone can fly off of it. With a limited battery capacity, however, the drone has a length constraint, i.e., the maximum flight range. A truck driver can replace a battery for the drone after each flight trip. The authors first transform the DT-ARP into a node routing problem, for which we present a MIP formulation for the case with a truck and a drone. To solve large-size instances with multiple drones, a heuristic method based on Adaptive Large Neighborhood Search is proposed. The performance of ALNS is evaluated on small-size randomly generated instances and large-size undirected rural postman problem benchmark instances. In addition, an analysis is provided on the relationship between truck/drone speeds and the drone’s flight range, which affects the difficulty level to solve. The robustness of ALNS is shown via numerical experiments.]]></description>
      <pubDate>Mon, 24 Feb 2025 13:45:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2485440</guid>
    </item>
    <item>
      <title>Transportation and carbon emissions costs minimization for time-dependent vehicle routing problem with drones</title>
      <link>https://trid.trb.org/View/2488839</link>
      <description><![CDATA[Integrating drones in package delivery has emerged as an innovative application of unmanned aerial vehicle (UAV) technology in the logistics and transportation sector. In this context, trucks serve a dual role as delivery vehicles for customers and launch platforms for drones. Drones are deployed for efficient package delivery and can be retrieved from predetermined rendezvous locations using trucks. Our study explicitly targets the collaborative package delivery approach between trucks and drones within urban environments. To optimize this collaboration, we develop a mixed-integer programming (MIP) model for the time-dependent vehicle routing problem with drones (TDVRP-D), which aims to minimize the transportation costs of both trucks and drones, along with the carbon emissions costs associated with trucks. To solve this complex problem efficiently, we propose a highly effective metaheuristic algorithm based on the variable neighborhood search (VNS) technique. Through extensive experimental studies and rigorous comparisons with existing methods, we demonstrate the superiority of our proposed algorithm in terms of solution quality and computational efficiency, particularly for large-scale instances.]]></description>
      <pubDate>Thu, 20 Feb 2025 16:25:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2488839</guid>
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