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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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>The 𝙠-Traveling Repairman Problem with Stochastic Service Request Times</title>
      <link>https://trid.trb.org/View/2686238</link>
      <description><![CDATA[The traveling repairman problem focuses on minimizing total latency, defined as the waiting time experienced by customers after submitting a service request. This problem naturally arises in settings where customer experience is a key objective, particularly in time-sensitive service operations. We address a generalized traveling repairman problem that extends the classical setting by incorporating available probabilistic information on the timing of future requests as well as a more flexible method for measuring the impact of service latency. To account for potential future requests in addition to those known at the time of route planning, we develop a priori routes combined with recourse rules for their execution, aiming to minimize the total expected disutility caused by service latency across all customers. We formulate the problem as a stochastic, path-based traveling repairman problem and solve it using a branch-and-price algorithm, where expected latency is estimated through sample-scenario planning. We then evaluate the performance of our a priori routing approach through a factorial experiment; compare it with alternative routing strategies; and apply these methods to a large-scale, real-world bike-sharing setting.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:03:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686238</guid>
    </item>
    <item>
      <title>Picking the Best Bot: Collaboration Strategies for Humans and Bots in Order Pick Systems with Traveling Salesman Problem Routing</title>
      <link>https://trid.trb.org/View/2686236</link>
      <description><![CDATA[The rapid growth of e-commerce has increased the demand for efficient order picking systems in large warehouses. To improve throughput performance, many facilities deploy autonomous mobile robots (AMRs) to assist human pickers. Warehouse throughput critically depends on the choice of human-robot collaboration policy. This study focuses on two popular policies: the swarm policy, in which pickers switch between AMRs while picking, and the system-directed policy, in which a picker completes an order with a single AMR. An analytical framework is developed to evaluate these policies. We model the swarm policy as a closed queuing network with a synchronization station, and we derive closed-form expressions for its steady-state probabilities and throughput given load-dependent service rates. The service rates of the network nodes are estimated by Monte Carlo simulation, accounting for stochastic travel times, varying order sizes, item allocation strategies, matching rules, and warehouse layouts. The analytical predictions are validated against detailed discrete-event simulations, with average relative errors below 2% in 12, 000 instances. The results indicate that the swarm policy generally provides higher throughput than the system-directed policy, with gains increasing in the AMR-to-picker count and speed ratios. The system-directed policy is more effective when AMR and picker speeds are similar, the orders are large, and there is a limited number of AMRs. Managerial insights are provided to guide policy choice.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:03:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686236</guid>
    </item>
    <item>
      <title>Beware of the classical benchmark instances for the Traveling Salesman Problem with Time Windows</title>
      <link>https://trid.trb.org/View/2679469</link>
      <description><![CDATA[We propose a simple and exact method for the Traveling Salesman Problem with Time Windows and Makespan objective (TSPTW-M) that solves all instances of the classical benchmark with 50 or more customers in less than ten seconds each. Applying this algorithm as an off-the-shelf method, we also solve all but one of these instances for the Duration objective. Our main conclusion is that these instances alone are no longer representative for evaluating the TSPTW-M and its Duration variant: their structure can be exploited to yield results that seem outstanding at first glance. Additionally, caution is advised when designing hard training sets for machine learning algorithms.]]></description>
      <pubDate>Wed, 17 Jun 2026 12:23:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679469</guid>
    </item>
    <item>
      <title>Improving analytic approximations of TSP tour lengths with adjustment factors</title>
      <link>https://trid.trb.org/View/2663010</link>
      <description><![CDATA[Optimizing Traveling Salesman Problem (TSP) tours requires substantial computational effort, leading researchers to develop approximations relating tour length to the number of visited points, 𝘯. Existing models, such as the √𝘯𝘈 predictor, effectively approximate tour lengths for large-capacity vehicles but sacrifice accuracy for small 𝘯 values relevant for most practical applications. Consequently, this study addresses this gap by proposing models with uniform node distributions, which incorporate realistic factors, such as central vs. random starting points and various service zone shapes. These factors are then integrated into a single equation, enhancing applicability. Furthermore, the exponent of 𝘯 is statistically estimated to be significantly different from 0.5, challenging previous studies. Our proposed model estimates TSP tour lengths more accurately, particularly for small 𝘯 values, and maintains accuracy for large 𝘯 values, with errors below 3.11% for up to 600 points. This model offers a more precise and versatile alternative to current models.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:38:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663010</guid>
    </item>
    <item>
      <title>Multi-UAV Coverage Path Planning for Autonomous Aircraft Inspection</title>
      <link>https://trid.trb.org/View/2652013</link>
      <description><![CDATA[This paper addresses the multi-UAV path-planning problem for autonomous aircraft inspection. Building on our previous research on parameterization-based inspection path planning, we apply this methodology to a multi-agent scenario. First, we compute a set of viewpoints using the parameterization-based planning algorithm, which enhances inspection quality. We then formulate and solve a multi-objective multiple traveling salesmen problem (MOMTSP) using our proposed heuristic algorithm, Non-dominated Sorting Genetic Ant Colony Optimization (NSGACO). Additionally, we implement a migration mechanism to identify the optimal depot location, ensuring the repeatability of the inspection mission. We validate the performance of NSGACO through numerical simulations, demonstrating its superiority over other heuristic approaches. Finally, software-in-the-loop simulations reveal significant improvements in inspection quality; specifically, when compared to traditional sampling approaches, the reconstruction model quality is markedly enhanced.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:35:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652013</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>
    </item>
    <item>
      <title>Charging-on-the-Move Public Transit Systems Constituted by Modular Trolleybuses and Modular Buses</title>
      <link>https://trid.trb.org/View/2511574</link>
      <description><![CDATA[The introduction of electric vehicles (EVs) into public transit systems poses challenges in harmonizing charging requirements with vehicle operations, especially during rush hours. In this study, we propose a charging-on-the-move public transit system (CPTS) based on existing feasible technologies. This system adopts electric-driven modular vehicle (MV) units, including modular trolleybuses (MTs) and modular buses (MBs). These MV units can seamlessly join and detach during operation. The MT acquires power from the overhead catenary via a pantograph, enabling the charging of MBs on the move when they join the same platoon. The optimization problem of CPTS is formulated as a substation-to-main station assignment (S2Ma) problem and a passenger-to-MB assignment (P2Ma) problem, aiming to jointly minimize system energy consumption and passenger travel costs. We introduce a nested genetic algorithm (GA) that incorporates the Lin-Kernighan–Helsgaun (LKH) traveling salesman problem (TSP) solver to address the proposed optimization model for CPTS. The case study, based on real road networks and actual demands not only demonstrates the superiority of the solution algorithm proposed in this article but also proves that CPTS has advantages in transportation services compared to traditional public transit and taxi services. Sensitivity analyses go a step further in clarifying how changes in both demand and supply of CPTS affect its performance, offering valuable managerial insights.]]></description>
      <pubDate>Mon, 16 Feb 2026 17:29:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511574</guid>
    </item>
    <item>
      <title>A Bi-criterion Steiner Traveling Salesperson Problem with Time Windows for Last-mile Electric Vehicle Logistics</title>
      <link>https://trid.trb.org/View/2608654</link>
      <description><![CDATA[This paper addresses the problem of energy-efficient and safe routing of last-mile electric freight vehicles. With the rising environmental footprint of the transportation sector and the growing popularity of E-Commerce, freight companies are likely to benefit from optimal time window feasible tours that minimize energy usage while reducing traffic conflicts at intersections and thereby improving safety. We formulate this problem as a Bi-criterion Steiner Traveling Salesperson Problem with Time Windows (BSTSPTW) with energy consumed and the number of left turns at intersections as the two objectives while also considering regenerative braking capabilities. We first discuss an exact mixed-integer programming model with scalarization to enumerate points on the efficiency frontier for small instances. For larger networks, we develop an efficient local search-based heuristic, which uses several operators to intensify and diversify the search process. We demonstrate the utility of the proposed methods using benchmark data and real-world instances from Amazon delivery routes in Austin, US. Comparisons with state-of-the-art solvers show that our heuristics can generate near-optimal solutions within reasonable time budgets, effectively balancing energy efficiency and safety under practical delivery constraints.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:56:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2608654</guid>
    </item>
    <item>
      <title>Learning-guided iterated local search for the minmax multiple traveling salesman problem</title>
      <link>https://trid.trb.org/View/2601959</link>
      <description><![CDATA[The minmax multiple traveling salesman problem involves minimizing the costs of a longest tour among a set of tours. The problem is of great practical interest because it can be used to formulate several real-life applications. To solve this computationally challenging problem, we propose a learning-driven iterated local search approach that combines an effective local search procedure to find high-quality local optimal solutions and a multi-armed bandit algorithm to select removal and insertion operators to escape local optimal traps. Extensive experiments on 77 commonly used benchmark instances show that the algorithm achieves excellent results in terms of solution quality and running time. In particular, it achieves 32 new best results (improved upper bounds) and matches the best-known results for 35 other instances. Additional experiments shed light on the understanding of the algorithm’s constituent elements. Multi-armed bandit selection can be used advantageously in other multi-operator local search algorithms.]]></description>
      <pubDate>Tue, 11 Nov 2025 09:23:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2601959</guid>
    </item>
    <item>
      <title>Multi-UAV Inspection Optimization for Offshore Wind Farms Considering Battery Exchange Process</title>
      <link>https://trid.trb.org/View/2591865</link>
      <description><![CDATA[Unmanned Aerial Vehicles (UAVs) have become a highly effective tool for inspecting Offshore Wind Farms (OWFs) due to their controllability, speed, and safety. A practical route planning program ensures timeliness of UAV OWFs inspection. The previous studies’ models do not reflect real-world constraints of battery exchange operation-based UAV multistrip flights within UAV range and airport deployments. In this paper, an OWF inspection tasks allocation problem of UAVs (OWFI-TAP-of UAVs) is analyzed, which considers the UAV battery exchange process. Firstly, the UAV model to calculate the inspection time consumption is established, which considers the relation between UAV velocity and energy and the battery exchange process; then, a multi-UAV inspection task allocation model for minimizing the UAV inspection time by combining the Multiple Traveling Salesman Problem (MTSP) mathematical expressions and the battery exchange specificities is proposed. Secondly, an improved genetic algorithm (IGA) is proposed that includes a clustering initialization strategy and a task balance strategy to optimize the OWFI-TAP-of UAVs globally. Finally, taking Jiaxing OWF as the simulation case through numerical experiments validates the effectiveness of the proposed approach.]]></description>
      <pubDate>Fri, 07 Nov 2025 11:34:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591865</guid>
    </item>
    <item>
      <title>Generating practical last-mile delivery routes using a data-informed insertion heuristic</title>
      <link>https://trid.trb.org/View/2583345</link>
      <description><![CDATA[Couriers often deviate from pre-planned delivery routes due to practical realities that routing algorithms may overlook. The authors statistically demonstrate that, in addition to travel time, factors like turn sharpness, backtracking distance, and neighbourhood visit timing influence a driver’s navigational choices and propose a Data-informed Insertion Heuristic (DIIH) for Travelling Salesman Problems (TSPs), which considers a custom cost function inferred from historical routes. The DIIH is trained on the dataset from Amazon’s Last-mile Research Challenge, which contains historical TSP instances classified into one of three qualities: high, medium, or low, indicating the satisfaction level of Amazon’s logistics planners of a route based on productivity, courier experience, and customer satisfaction levels. The authors train an energy-based model to predict the likelihood of a route being of high quality. Compared to existing benchmarks, the DIIH generates 22.4% and 24.1% additional high-quality solutions than the Amazon challenge winner and courier-performed routes, respectively. This improvement comes with an increase of 20.6% and 13.9% in the median travel time. While optimizing purely for travel time would result in shorter routes, the authors account for both travel time and human preferences, which explains the observed tradeoff. The authors show that the probabilistic evaluation of a route measured by the energy-based model developed in this study is a promising metric for estimating a routing algorithm’s practical performance.]]></description>
      <pubDate>Fri, 24 Oct 2025 16:53:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2583345</guid>
    </item>
    <item>
      <title>A synchronized vessel and autonomous vehicle model for environmental monitoring: Mixed integer linear programming model and adaptive matheuristic</title>
      <link>https://trid.trb.org/View/2569977</link>
      <description><![CDATA[In offshore environmental monitoring projects, ocean currents enable the detection of chemical signals from a distance, with longer observation times at each point increasing the area that can be monitored. Leveraging this principle, the covering tour problem with varying coverage was recently introduced for environmental monitoring. In this paper, the authors introduce a generalization of this problem, where they utilize a main vessel and a fleet of autonomous underwater vehicles (AUVs), and the properties of time-varying coverage, which refers to the dynamic changes in the area that can be monitored based on the duration spent at each location, to minimize the required time to visit or cover a set of pre-specified locations in an area of interest. This problem can be presented as a rich covering salesperson problem, namely the multi-visit multi-drone covering salesperson problem with varying coverage (mCSP-VC). The authors  present a mixed integer linear programming (MILP) model of mCSP-VC, and considering the complexity of the problem, design and implement an adaptive matheuristic algorithm and showcase its effectiveness. Moreover, the authors investigate the effects of altering different parameters on the solutions and provide managerial insights into optimizing monitoring operations and resource allocation.]]></description>
      <pubDate>Mon, 08 Sep 2025 14:54:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569977</guid>
    </item>
    <item>
      <title>Novel operational algorithms for ride-pooling as on-demand feeder services</title>
      <link>https://trid.trb.org/View/2564169</link>
      <description><![CDATA[Ride-pooling (RP) service, as a form of shared mobility, enables multiple riders with similar itineraries to share the same vehicle and split the fee. This makes RP a promising on-demand feeder service for patrons with a common trip end in urban transportation. The authors propose the RP as Feeder (RPaF) services with tailored operational algorithms. Specifically, the authors have developed (i) a batch-based matching algorithm that pools a batch of requests within an optimized buffer distance to each RP vehicle; (ii) a dispatching algorithm that adaptively dispatches vehicles to pick up the matched requests for certain occupancy target; and (iii) a repositioning algorithm that relocates vehicles to unmatched requests based on their level of urgency. The authors also embed the Traveling-Salesman-Problem (TSP) model to generate routing plans for each dispatched vehicle. An agent-based microscopic simulation platform is designed to execute these operational algorithms (via the Operator module), generate spatially distributed random requests (Patron module), and account for traffic conditions (Vehicle module) in street networks. Extensive numerical experiments are conducted to showcase the effectiveness of RPaF services across various demand scenarios in typical morning rush hours. The authors compare RFaF with two on-demand feeder counterparts proposed in previous studies: Ride-Sharing as Feeder (RSaF) and Flexible-Route Feeder-Bus Transit (Flex-FBT). Comparisons reveal that given the same fleet size, RPaF generally outperforms RSaF in higher service rates (i.e., the percentage of requests served over all requests) and Flex-FBT in shorter average trip times for patrons. Lastly, the authors illustrate the implementation of RPaF in a real-world case study of the uptown Manhattan network (USA) using actual taxi trip data. The results demonstrate that RPaF effectively balances the level of service (service rate and patrons’ average trip time) with operational costs (fleet size). The proposed simulation platform offers a valuable testbed for evaluating and optimizing various on-demand feeder services.]]></description>
      <pubDate>Thu, 21 Aug 2025 09:19:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2564169</guid>
    </item>
    <item>
      <title>Learning for routing: A guided review of recent developments and future directions</title>
      <link>https://trid.trb.org/View/2573459</link>
      <description><![CDATA[This paper reviews the current progress in applying machine learning (ML) tools to solve NP-hard combinatorial optimization problems, with a focus on routing problems such as the traveling salesman problem (TSP) and the vehicle routing problem (VRP). Due to the inherent complexity of these problems, exact algorithms often require excessive computational time to find optimal solutions, while heuristics can only provide approximate solutions without guaranteeing optimality. With the recent success of machine learning models, there is a growing trend in proposing and implementing diverse ML techniques to enhance the resolution of these challenging routing problems. The authors propose a taxonomy categorizing ML-based routing methods into construction-based and improvement-based approaches, highlighting their applicability to various problem characteristics. This review aims to integrate traditional OR methods with state-of-the-art ML techniques, providing a structured framework to guide future research and address emerging VRP variants.]]></description>
      <pubDate>Fri, 01 Aug 2025 08:29:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2573459</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>
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