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    <title>Transport Research International Documentation (TRID)</title>
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    <language>en-us</language>
    <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>Study on Quantum Approximation Optimization Algorithm in Airport Cargo Transportation Problem</title>
      <link>https://trid.trb.org/View/2707946</link>
      <description><![CDATA[The vehicle routing problem (VRP) is a core NP-hard combinatorial optimization problem in logistics and supply chain management. Quantum computing, particularly the Quantum Approximate Optimization Algorithm (QAOA), is being explored as a promising heuristic tool for tackling such problems. Therefore, this study employs the QAOA approach to solve the airport cargo VRP. Subsequently, through a concrete airport cargo case study, the paper analyzes the process of solving single-vehicle TSP and multivehicle VRP problems using QAOA. It compares the performance of different classical optimizers and discusses the impact of parameter settings (e.g., penalty factors, QAOA depth, and number of iterations) on the solution quality and feasibility, especially comparing the convergence speed of dynamic and fixed penalty coefficients. The paper also describes the design principles of QAOA quantum circuits for such VRP problems. Furthermore, the study presents a preliminary benchmark on a real quantum computer, demonstrating that its computation time for the problem’s QUBO matrix (14 ms) is approximately comparable to that of classical algorithms. Finally, it summarizes the current scalability challenges and limitations faced by QAOA in VRP applications concerning qubit count, circuit depth, and classical optimizer performance, and offers an outlook on future development directions. The research indicates that although QAOA demonstrates solution capabilities on specific small-scale VRP instances, the complexity of parameter tuning and sensitivity to the spatial distribution of aircraft stands are critical considerations before practical application.]]></description>
      <pubDate>Fri, 29 May 2026 09:00:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2707946</guid>
    </item>
    <item>
      <title>Drone routing problem for shore-to-ship delivery services considering non-linear energy consumption</title>
      <link>https://trid.trb.org/View/2667029</link>
      <description><![CDATA[This study investigates the emerging application of unmanned aerial vehicles (UAVs), or drones, for shore-to-ship delivery services between onshore and offshore locations. However, deploying drones for shore-to-ship delivery can encounter unique operational challenges, including constantly moving target vessels and non-linear drone energy consumption. To address these issues, we propose a novel and practical drone routing problem for shore-to-ship delivery services (DRP-SSDS) considering the non-linear energy consumption related to payload, flight phase, and flight time. The proposed DRP-SSDS is formulated as a mixed-integer second-order cone programming (MISOCP) model that integrates continuous decisions on both time and location to realistically capture vessel movements within port waters. We then develop a tailored branch-and-price algorithm that can solve DRP-SSDS exactly and efficiently for medium-scale instances. Additionally, we design an effective heuristic method that can provide high-quality solutions in a reasonable time limit for large-scale instances. Extensive numerical experiments demonstrate the superiority of the proposed solution methods over the off-the-shelf optimization solver and a benchmark method across all tested instances.]]></description>
      <pubDate>Tue, 26 May 2026 09:40:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667029</guid>
    </item>
    <item>
      <title>A Multifidelity-Based Ant Colony Optimization Algorithm for Capacitated Electric Vehicle Routing Problems</title>
      <link>https://trid.trb.org/View/2672798</link>
      <description><![CDATA[The capacitated electric vehicle routing problem (CEVRP) has drawn much attention from researchers in the recent decade against the background of the rising electric transportation industry. Existing studies have found the CEVRP more difficult to address than the typical CVRP since the CEVRP needs to simultaneously optimize routing plans and charging decisions at a high computational budget. Given a certain routing plan, it takes much computational cost to exhaustively or approximately achieve the accurate optimal charging decision under the routing plan. This paper proposes that it is unnecessary to search for the accurate optimal charging decisions for potentially low-quality routing plans found during the CEVRP optimization, and instead obtaining acceptable charging decisions significantly reduces the computational cost and helps maintain fast convergence. A multifidelity-based ant colony optimization (MFACO) algorithm is then proposed to flexibly search charging decisions based on the potential quality of routing plans so that CEVRPs can be addressed at high efficiency. The proposed MFACO employs a low-fidelity search strategy to obtain coarse charging decisions for potentially low-quality routing plans, whereas for potentially high-quality routing plans MFACO employs three high-fidelity search strategies to local search in different regions of decision space and provide accurate optimal charging decisions. Experimental results demonstrate that the proposed multifidelity method enhances the efficiency of ACO in solving CEVRPs and the proposed MFACO significantly outperforms four state-of-the-art algorithms for CEVRPs, providing a competitive performance in terms of both solution quality and computational cost.]]></description>
      <pubDate>Thu, 07 May 2026 11:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672798</guid>
    </item>
    <item>
      <title>Novel models and efficient heuristic for the vessel-unmanned surface vehicle routing problem</title>
      <link>https://trid.trb.org/View/2652351</link>
      <description><![CDATA[This paper addresses the vessel–unmanned surface vehicle (USV) routing problem (VURP), which jointly determines the routes of a vessel and a USV. The problem is applicable to offshore supply, search and rescue, and inspection operations. In the VURP, a vessel carries a loaded USV and the USV departs from the vessel to deliver goods to offshore platforms when in proximity. The objective is to minimize the total routing costs of both vehicles by optimizing their routes along with the USV’s departure and arrival points. We begin by exploiting the structural properties of the problem and propose two enhanced mixed-integer second-order conic programming (MISOCP) formulations, each further strengthened with valid inequalities to improve computational performance. Due to the NP-hard nature of the problem, we develop a tailored adaptive large neighborhood search (ALNS) algorithm designed to handle practical-sized instances. The proposed ALNS employs a two-phase framework to enable a multi-start mechanism: the first phase generates a diverse set of initial solutions using an effective approximation-based scheme, while the second phase iteratively improves these solutions through a problem-specific ALNS procedure. This structure balances global exploration and local intensification, enhancing both solution diversity and robustness. Extensive computational experiments demonstrate the strong performance of the improved formulations and the heuristic method. The integration of USVs significantly reduces vessel travel costs, especially when the USVs have larger capacities. Moreover, numerical results on benchmark instances show that our ALNS outperforms state-of-the-art heuristics, achieving 45 new best solutions among 72 open benchmark cases.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:20:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652351</guid>
    </item>
    <item>
      <title>Collaborative routing optimization of ground delivery vehicles and unmanned aerial vehicles considering time window constraints</title>
      <link>https://trid.trb.org/View/2643305</link>
      <description><![CDATA[In order to improve the logistics delivery efficiency, this paper proposes a collaborative ground delivery vehicle (GDV) and unmanned aerial vehicle (UAV) routing problem with time window constraints, which takes into account the actual airspace and road conditions. A mixed-integer programming (MIP) model is established. The proposed model approaches the interaction between the GDV and the UAV through two binary variables, allowing flexible collaboration to improve delivery efficiency. Numerical experiments reveal that customer groups who are time-sensitive and geographically dispersed are more suitable for collaborative delivery of the GDV and the UAV. Sensitivity analysis reveals critical points where airspace and road conditions have a significant impact on the efficiency of collaborative delivery. This study provides insights for the practical application of collaborative delivery with the GDV and the UAV in logistics operations management.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643305</guid>
    </item>
    <item>
      <title>Evaluation of environmental and operational impacts of a delivery system with parcel lockers</title>
      <link>https://trid.trb.org/View/2643297</link>
      <description><![CDATA[Many local authorities have enforced vehicle access restrictions to improve air quality and liveability in urban areas, prompting changes in last-mile delivery to increase the sustainability of the system. This paper aims to quantify the environmental and operational efficiency of a delivery system with parcel lockers. We consider a company operating in the Municipality of Padova (Italy), with an heterogeneous fleet and a given subset of lockers. The lockers' demand is estimated, and a traffic simulation model is implemented to evaluate the traffic conditions in the road network. Pollutant emissions generated by freight vehicles were quantified. The distribution problem is modelled as a vehicle routing problem with or without split delivery. Multiple scenarios are tested, considering the quantitative and spatial changes in the lockers to be served. The results show that the split delivery system decreases the generalised cost for the company and the CO₂ emissions.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643297</guid>
    </item>
    <item>
      <title>Capacity Vehicle Routing Problem with Time Windows: Simulation Tool for Footprint Network Design</title>
      <link>https://trid.trb.org/View/2579235</link>
      <description><![CDATA[The paper focuses on a decision support system designed for logistic experts and aimed at addressing Vehicle Routing Problem that includes multi-vehicles and multi-depot with time constraints and considers the capacities of vehicles and logistic nodes too. The paper proposes a novel solution featuring a three-layer architecture and a system able to simulate the behavior of the network. Therefore, the paper proposes a tool to assess the impact of changes in volumes and capacities on overall delivery times. The integration of information about sorting nodes, delivery nodes, travel distances, and daily item demands is crucial for simulating accurate arrival times at each destination point. Computational experiments are depicted for validating the model and showing its effectiveness and its application.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579235</guid>
    </item>
    <item>
      <title>Vehicle routing problem with en-route delivery</title>
      <link>https://trid.trb.org/View/2643243</link>
      <description><![CDATA[Traditional last-mile delivery usually requires customers to offer predetermined locations for receiving parcels. However, the advancement of information and communication technology enables the collection and utilization of real-time information for more innovative, flexible, and cost-saving ways of delivery. This paper introduces a new last-mile delivery problem where en-route deliveries can occur at any node or arc along customers’ trajectories. Customers’ trajectories are first converted into candidate time windows across nodes and arcs, with exactly one time window per customer required to enable delivery. This problem formulates a general routing problem that minimizes total travel cost, which is then transformed into a node-routing problem solvable through mixed-integer linear programming. Results show that flexible en-route deliveries significantly reduce transportation costs compared to traditional delivery approaches where delivery can be made at only one location. Moreover, en-route delivery is particularly effective when service time at arcs is shorter than at nodes.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643243</guid>
    </item>
    <item>
      <title>Optimal solution techniques to the vehicle routing problem arising in Lion Beer distribution in Sri Lanka</title>
      <link>https://trid.trb.org/View/2646862</link>
      <description><![CDATA[The problem investigated in this paper originated from the distribution of beers by a Colombo-based company, Lion Brewery (Ceylon) PLC, in Sri Lanka. Currently, this company’s outbound logistics consist of a decentralized distribution model and a redistribution process for its beer bottles and cans in the Colombo region. Extra routing costs due to unreasonable consumption of additional distance have been noticed in the current decentralized redistribution process. Here, the problem is modeled as a variant of the vehicle routing problem with a heterogeneous fleet. Our objective is to minimize the routing costs by imposing constraints on the volume of company vehicles. Centralized heuristic and genetic algorithm solution procedures for the problem are presented. The superior performance of the proposed heuristic is demonstrated relative to existing heuristics through a beer distribution instance and 10 additional small-scale real-world application instances. The computational investigation highlights the cost savings that the proposed heuristic can accrue. The cost savings can be as significant as 19.84% compared to a company’s existing decentralized method, 4.34% compared to the genetic algorithm, and 6.73% and 2.47% compared to the two recent methods. This cost-saving has a practical impact on supplying customers with a necessary drink, beer, at a reduced price.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646862</guid>
    </item>
    <item>
      <title>The logistics vehicle charging station location selection and routing problem with partial recharging and shared fleets</title>
      <link>https://trid.trb.org/View/2663823</link>
      <description><![CDATA[Growing global concern over environmental protection and climate change has led governments worldwide to promote electric vehicles to support carbon neutrality goals. However, limited charging infrastructure, particularly for urban logistics, remains a major barrier to the adoption of electric vehicles for delivery services. This study examines the logistics vehicle charging station location selection and routing problem with partial recharging and shared fleets. An electric vehicle charging model and a nonlinear energy consumption model are formulated to better represent real-world energy use and charging behavior. A bi-objective optimization model is proposed to minimize total operating costs and the required number of vehicles. To solve the model, a hybrid algorithm combining 3D k-means clustering and an improved multi-objective particle swarm optimization (IMOPSO) is developed. The 3D k-means clustering groups spatiotemporal customer data to support periodic resource allocation. The IMOPSO incorporates an enhanced update mechanism and elite selection to improve solution quality and convergence speed. In addition, a resource sharing strategy and a charging station insertion method are applied to further optimize vehicle deployment and station selection. The performance of IMOPSO is evaluated against the CPLEX solver, an improved non-dominated sorting genetic algorithm II, a flexible variable neighborhood search algorithm, and a multi-objective genetic algorithm with simulated annealing. A real-world case study in Chongqing City, China, assesses the proposed approach under sensitivity analysis of model parameters, five recharging levels, multiple resource sharing scenarios, and different collaboration modes. The results indicate that the proposed method supports efficient planning of urban delivery systems and charging infrastructure, contributing to a greener and more cost-effective logistics network.]]></description>
      <pubDate>Thu, 19 Mar 2026 08:57:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663823</guid>
    </item>
    <item>
      <title>In-plant autonomous mobile robot scheduling and routing problem considering battery consumption model</title>
      <link>https://trid.trb.org/View/2640804</link>
      <description><![CDATA[The evolving landscape of larger and increasingly intricate systems calls for the adoption of diverse and adaptable autonomous mobile robots (AMRs) to meet the movement requirements within a factory. To examine the battery consumption characteristics of AMRs, our study introduces a novel perspective by investigating the relationship between carrying capacity and battery consumption. We propose to model this relationship using simulation techniques, thus enabling our study to better aligns with real-world applications. The primary objective of this study is to develop optimal charging strategies within the constraints of limited charging stations while simultaneously minimizing overall tardiness. We develop a mixed integer programming (MIP) model for the AMR scheduling and routing problem with battery consumption model (AMRSRP-BCM). A dynamic operator sequence reheating simulated annealing hyper-heuristic (DOS-RSAH) algorithm is designed to solve this problem. Extensive experiments are conducted to demonstrate the robustness of our model and algorithm while sensitivity analysis is presented to show the application of our study, and valuable insights are gained.]]></description>
      <pubDate>Tue, 17 Mar 2026 09:47:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640804</guid>
    </item>
    <item>
      <title>Tramp ship routing problem considering the implementation of carbon intensity indicator</title>
      <link>https://trid.trb.org/View/2640676</link>
      <description><![CDATA[In 2023, the International Maritime Organization (IMO) formally implemented the mandatory carbon intensity indicator (CII) rating requirements, linking ships' carbon emissions to transport activities. The introduction of CII has significantly impacted shipping companies' fleet operation planning. In this paper, we examine tramp shipping companies' market contract selection and fleet scheduling under the backdrop of CII implementation. By incorporating the CII ship rating requirements into the constraints of a tramp ship routing and scheduling optimisation model, we expand the current research on the tramp ship routing problem to consider new carbon emission regulations. Additionally, a tailored adaptive large neighbourhood search (ALNS) algorithm is designed to provide a solution tool for large-scale instances of the model. Finally, we perform sensitivity analysis experiments on the model using multiple sets of different scales. The multifaceted impact of CII implementation on the fleet operation of shipping companies is discussed based on the experimental results.]]></description>
      <pubDate>Thu, 12 Mar 2026 14:02:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640676</guid>
    </item>
    <item>
      <title>A stochastic electric vehicle routing problem under uncertain energy consumption</title>
      <link>https://trid.trb.org/View/2636247</link>
      <description><![CDATA[The increasing adoption of Electric Vehicles (EVs) for service and goods distribution operations has led to the emergence of Electric Vehicle Routing Problems (EVRPs), a class of vehicle routing problems addressing the unique challenges posed by the limited driving range and recharging needs of EVs. While the majority of EVRP variants have considered deterministic energy consumption, this paper focuses on the Stochastic Electric Vehicle Routing Problem with a Threshold recourse policy (SEVRP-T), where the uncertainty in energy consumption is considered, and a recourse policy is employed to ensure that EVs recharge at Charging Stations (CSs) whenever their State of Charge (SoC) falls below a specified threshold. We formulate the SEVRP-T as a two-stage stochastic mixed-integer second-order cone model, where the first stage determines the sequences of customers to be visited, and the second stage incorporates charging activities. The objective is to minimize the expected total duration of the routes, composed by travel times and recharging operations. To cope with the computational complexity of the model, we propose a heuristic based on an Iterated Local Search (ILS) procedure coupled with a Set Partitioning problem. To further speed up the heuristic, we develop two lower bounds on the corresponding first-stage customer sequences. Furthermore, to handle a large number of energy consumption scenarios, we employ a scenario reduction technique. Extensive computational experiments are conducted to validate the effectiveness of the proposed solution strategy and to assess the importance of considering the stochastic nature of the energy consumption. The research presented in this paper contributes to the growing body of literature on EVRP and provides insights into managing the operational deployment of EVs in logistics activities under uncertainty.]]></description>
      <pubDate>Wed, 11 Mar 2026 14:45:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636247</guid>
    </item>
    <item>
      <title>A neighborhood-based matheuristic for the vehicle routing problem with delivery options</title>
      <link>https://trid.trb.org/View/2594575</link>
      <description><![CDATA[This paper investigates a realistic version of the vehicle routing problem with delivery options (VRPDO) in the context of last-mile logistics management. The problem aims to optimize vehicle routing while considering alternative customer (request) delivery options and service levels for the last-mile delivery. The shared delivery locations can serve multiple requests simultaneously, but the chosen locations should satisfy the specific capacity and service levels, which requires synchronization between different routes. This synchronization of resources makes route planning more consistent with the needs of actual logistics services but greatly increases the complexity of algorithm design. To address this complex problem, a novel neighborhood-based matheuristic is proposed to intensively search for high-quality solutions from both primal and dual perspectives. Then, a relax-and-fix based insertion heuristic is developed to obtain initial feasible solutions for the proposed algorithm, which effectively balances the feasibility and optimality in solution construction. Finally, the performance of the proposed algorithm is evaluated on two sets of VRPDO benchmarks (small and large instances) available in the literature. The experimental results show that our proposed algorithm has superior performance compared to state-of-the-art methods, obtaining 114 out of 120 and 85 out of 120 best-known solutions for the small- and large-scale benchmark instances, respectively. Furthermore, a real VRPDO case study of a third-party logistics company in China is introduced, which confirms that the proposed algorithm is suitable for processing VRPDO in newly introduced real-world instances.]]></description>
      <pubDate>Wed, 11 Mar 2026 14:44:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594575</guid>
    </item>
    <item>
      <title>A meta-learning enhanced deep reinforcement learning approach for generalizing across orienteering problem with time windows</title>
      <link>https://trid.trb.org/View/2630847</link>
      <description><![CDATA[The Orienteering Problem with Time Windows (OPTW) is a complex combinatorial optimization problem with applications in logistics, tourist route planning, and emergency services. Traditional methods for solving OPTW, including metaheuristics, often struggle with scalability, adaptability, and generalization to new instances. Recently, deep reinforcement learning (DRL) has shown promise in tackling routing problems. However, existing DRL methods typically rely on non-Markovian state representations and handcrafted masking rules, which limit their adaptability and generalization. This paper presents Meta Pointer Network for OPTW (MetaPNet-OPTW), a meta-learning-enhanced DRL framework that combines a Markovian state formulation with OR-based feasibility rules within a pointer network model. We introduce the Meta-Learning enhanced REINFORCE algorithm, which learns across diverse problem instances and enables rapid adaptation to unseen configurations with minimal fine-tuning. During inference, active search with beam search is used to refine solutions dynamically. Extensive experiments show that MetaPNet-OPTW outperforms existing DRL approaches in efficiency and generalization, and notably improves 20 of 33 best-known solutions on the 𝘎𝘢𝘷𝘢𝘭𝘢𝘴 benchmark. We further provide a t-SNE analysis of the learned latent space, enriched with spatio-temporal statistics, which explains why the model excels on 𝘎𝘢𝘷𝘢𝘭𝘢𝘴 instances while identifying harder clusters such as 𝘳2 and 𝘤2. This study contributes a scalable DRL framework for OPTW that not only achieves state-of-the-art performance but also provides new interpretability into benchmark difficulty and model adaptability.]]></description>
      <pubDate>Mon, 02 Mar 2026 08:56:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630847</guid>
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