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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Metaheuristic approaches for the Manhattan metric straddle carrier routing problem with buffer areas</title>
      <link>https://trid.trb.org/View/2704178</link>
      <description><![CDATA[This study investigates an optimization problem in container terminals, where straddle carriers (SCs) transport containers between seaside and stacking areas. Container transportation sequences respect both the predetermined loading sequence at quay cranes (QCs) and the capacity restrictions of buffer areas located below QCs for container exchange between SCs and QCs. We propose two sets of strategies. The first prioritizes runtime efficiency, employing methods such as a local search procedure and two variants of Variable Neighborhood Descent. For the second strategy, we use two different metaheuristics, namely Variable Neighborhood Search and a Greedy Randomized Adaptive Search Procedure, to provide solutions with superior objective function values. The performance of these methods is assessed in an extensive computational study and compared with benchmarks from the literature, showing that the proposed methods effectively allocate containers to SCs, resulting in minimized idle times of QCs and, consequently, shorter turnaround times for vessels.]]></description>
      <pubDate>Wed, 02 Sep 2026 09:21:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704178</guid>
    </item>
    <item>
      <title>Solving heterogeneous vehicle routing problems using decomposition and set covering</title>
      <link>https://trid.trb.org/View/2697014</link>
      <description><![CDATA[In practice, last-mile delivery carriers must efficiently transport goods to a large number of customers using a limited fleet of various vehicle types. While research into complex vehicle routing problem (VRP) variants is expanding, much of the established work focuses on developing solution algorithms for small- to mid-scale vehicle routing problems (VRPs), often assuming a single vehicle type with unrestricted availability. This study introduces an iterative matheuristic framework that leverages decomposition and set covering methods to address large-scale VRPs with a heterogeneous fleet (HVRP). In every iteration, those instances are decomposed into smaller, homogeneous VRPs using customer- and route-based unsupervised machine learning. The resulting subproblems are solved by existing routing software, and the created routes are collected in a route pool. A solution to the original problem is found by solving the set covering problem. Computational experiments demonstrate the framework’s ability to produce high-quality solutions across multiple HVRP variants within reasonable computing time, including several new best-known solutions for academic benchmark instances.]]></description>
      <pubDate>Fri, 28 Aug 2026 08:34:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697014</guid>
    </item>
    <item>
      <title>An attention-enhanced meta-heuristic algorithm for multi-drone assisted humanitarian delivery problem</title>
      <link>https://trid.trb.org/View/2697265</link>
      <description><![CDATA[There is a growing interest in the collaborative delivery approach involving trucks and drones to deliver relief supplies to disaster-affected areas efficiently. However, existing studies fail to account for two key characteristics of real-world relief delivery scenarios: the linear service-interval-dependent demand for relief supplies in affected areas and the multi-period decision-making environment. To better reflect reality, this study introduces the linear service-interval-dependent multi-drone assisted humanitarian delivery problem with mobile satellites (LSIDMDHDP-MS), which integrates the aforementioned practical characteristics. The LSIDMDHDP-MS is computationally intensive, as it combines two NP-hard routing problems and involves significant mutual influences among demands, vehicle routes, and drone routes due to linear service-interval-dependent demands. Furthermore, the interdependence of vehicle and drone routes results in a massive solution space. To tackle the LSIDMDHDP-MS, an attention-enhanced meta-heuristic algorithm is proposed. The algorithm can generate high-quality solutions by leveraging its ability to explore a broad solution space, learning representations, and parallel computing. Extensive experiments demonstrate that it can achieve optimal solutions for small instances much faster than traditional solvers like Gurobi. For medium and large instances, the algorithm reduces the average cost by 8.38% in a shorter computational time compared to existing algorithms. Furthermore, sensitivity analyses provide key insights for operational decision-making.]]></description>
      <pubDate>Fri, 28 Aug 2026 08:34:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697265</guid>
    </item>
    <item>
      <title>Freight-on-Transit operational problems with robot delivery: Genetic algorithm and Benders decomposition approaches</title>
      <link>https://trid.trb.org/View/2694896</link>
      <description><![CDATA[Freight-on-Transit (FoT) is an approach that holds considerable promise for the efficient delivery of parcels from suburban depots to inner-city customers. By enabling the loading of parcels onto public transportation, such as buses or trams with available capacity at off-peak hours, FoT facilitates the transportation of a greater volume of goods while concomitantly mitigating the impact on in-town traffic and local pollution. The present study investigates the Three-Tier Star Variant Delivery Problem with Public Transportation (3T-SVDPPT), an operational-level planning problem within the context of FoT. This problem involves the transportation of parcels from depots to public transport stops, followed by their pickup by public transport vehicles (buses, trams, etc.) and subsequent delivery to customers by environmentally-friendly autonomous robots. We study the fully-capacitated problem where all tiers have limited capacities, and a semi-capacitated variant where only public vehicles have limited capacities, depending on the vehicle and schedule. For the most general setting, we design a Genetic Algorithm (GA) exploiting the specific problem structure and tier decomposition. For the semi-capacitated case, we propose a reformulation of the 3T-SVDPPT, thereby creating a novel model specifically tailored for a Benders decomposition technique. Empirical evidence demonstrates that for large problem sizes, both approaches outperform their compact formulation counterparts.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:03:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694896</guid>
    </item>
    <item>
      <title>Multi-depot electric vehicle routing problem with half-open rotations: a formulation and metaheuristic algorithms</title>
      <link>https://trid.trb.org/View/2692476</link>
      <description><![CDATA[Motivated by achievement of net-zero emission goals surrounding all over the world, this paper introduces a novel problem called Multi-Depot Electric Vehicle Routing Problem with Half-Open Rotations (MDEVRP-HOR). While rotation refers to the set of all routes assigned to an EV, half-open signifies that the departure and arrival depot of an EV may differ. EVs meet customer demands, can replenish load at any depot, and may recharge at depots or recharging stations. The objective is to minimize the total cost arising from use of EVs and distance traversed by respecting load capacity, battery capacity, and maximum tour time constraints. The problem is formulated as Mixed Integer Linear Program (MILP) and procedures from the recent literature are tailored to improve the mathematical model. However, since CPLEX can inherently find optimum solutions for only small-sized instances, two metaheuristics namely Memetic Algorithm (MA) and Simulated Annealing (SA) are developed with a novel encoding and decoding strategy along with bespoke mechanisms specifically designed to exploit the half-open rotation structure of the problem. Algorithm parameters are optimized using the Taguchi method, and the proposed approaches are evaluated on benchmark instances from the literature. Computational results demonstrate that (i) the half-open rotation strategy significantly improves routing decisions and reduces total cost, and (ii) while MA provides higher-quality solutions, SA achieves competitive results with substantially shorter computational times. We also report comparative results of the MA with state-of-the-art literature for the special case of the problem where there exist a single depot and time windows.]]></description>
      <pubDate>Thu, 23 Jul 2026 09:14:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692476</guid>
    </item>
    <item>
      <title>A dual-heuristic crossover genetic algorithm with variable neighborhood search for flexible job shop scheduling with transportation times</title>
      <link>https://trid.trb.org/View/2692463</link>
      <description><![CDATA[The flexible job shop scheduling problem with transportation time (FJSPTT) is a critical challenge in machining production, as the transit time of jobs significantly affects the overall completion time. Efficiently addressing this problem is essential for improving productivity and reducing makespan. To tackle this challenge, we propose a Dual Heuristic Crossover with Variable Neighborhood Search Genetic Algorithm (DHC-VNSGA). The method integrates a decoding strategy that accounts for transportation time. A time-based heuristic crossover is designed for machine selection, and an operation-number-based crossover is applied for operation sequencing to improve offspring quality. In addition, a two-gene-inspired mutation operator enhances machine selection diversity, while variable neighborhood search refines critical operations and ensures uniqueness of solutions. The effectiveness of the proposed method is validated through a series of experiments. First, orthogonal experiments are conducted to determine appropriate parameter settings, and ablation studies are performed to verify the contribution of each algorithmic component. Then, comparative experiments on the Brandimarte and Fattahi benchmark instances show that DHC-VNSGA consistently achieves better makespan performance and overall solution quality than several representative algorithms. Further robustness experiments indicate that this method maintains stable performance even when subjected to mild and moderate perturbations in transportation time. Finally, in a real-world case study from Company G, the proposed method reduces the maximum completion time by 6.86% compared with the original scheduling scheme. These results demonstrate that DHC-VNSGA is an effective and practical approach for solving FJSPTT.]]></description>
      <pubDate>Thu, 23 Jul 2026 09:14:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692463</guid>
    </item>
    <item>
      <title>A memory-enhanced Greedy Randomized Adaptive Search Procedure for the Multi-Pickup and Delivery Problem with Time Windows</title>
      <link>https://trid.trb.org/View/2686891</link>
      <description><![CDATA[This paper addresses the Multi-Pickup and Delivery Problem with Time Windows (MPDPTW), a complex extension of the classical pickup and delivery problem involving multiple pickups per request under precedence and time window constraints.We introduce a memory-enhanced heuristic framework based on a Greedy Randomized Adaptive Search Procedure (GRASP). Unlike classical GRASP implementations, which rely on independent multi-start constructions followed by local search, the proposed framework embeds a structured long-term memory that records both feasible routes and infeasible request subsets. This mechanism prevents repeated evaluations of identical request combinations and accelerates feasibility checks throughout the search process. A set-covering-based recombination phase is then applied to consolidate high-quality routes and further strengthen overall solution quality.Computational experiments on standard benchmark instances show that the proposed approach consistently reaches all best-known solutions reported in the literature and improves them by an average of 0.41%. These results demonstrate the effectiveness of integrating cumulative memory guidance with global route recombination for solving large and highly constrained MPDPTW instances.]]></description>
      <pubDate>Fri, 10 Jul 2026 09:42:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686891</guid>
    </item>
    <item>
      <title>A multi-objective approach for the integrated allocation of quay cranes, internal trucks, and yard cranes in container terminal considering actual handling efficiency</title>
      <link>https://trid.trb.org/View/2684676</link>
      <description><![CDATA[The growth of maritime container trade volume and the frequent berthing of ships require container terminals to shorten the ship turnaround time. As the main production tool of container terminal, the reasonable allocation of equipment resources is conducive to minimizing the ship turnaround time and the operating cost of container terminal. In order to provide a reasonable and refined equipment configuration scheme, this paper studies the joint allocation of quay cranes (QCs), internal trucks (ITs), and yard cranes (YCs) in container terminal with consideration of container batch operations. Two formulas are proposed to measure the relationship between the number of IT, QC work efficiency, and YC work efficiency. A multi-objective mixed integer programming model for the joint allocation of QCs, ITs, and YCs is established to minimize the ship turnaround time, the operating cost of the terminal side, and the operating cost of the yard side. Among them, three configuration profile decision variables are proposed to describe the equipment allocation scheme. To solve this problem, an enhanced multi-objective evolutionary algorithm based on the fitness evaluation mechanism of fuzzy correlation entropy is designed, and an adaptive local reinforcement search strategy founded on tabu search and multiple neighborhood structures is introduced to improve the global search capability. Finally, the Shanghai Port is taken as a case study, and experiments of different scales verify the effectiveness of the model and method.]]></description>
      <pubDate>Wed, 01 Jul 2026 09:36:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684676</guid>
    </item>
    <item>
      <title>Ride-pool assignment algorithms: Swapping heuristics and modern implementation</title>
      <link>https://trid.trb.org/View/2684669</link>
      <description><![CDATA[On-demand ride-pooling has emerged as a popular urban transportation solution, addressing the efficiency limitations of traditional ride-hailing services by grouping multiple riding requests with spatiotemporal proximity into a single vehicle. Although numerous algorithms have been developed for the Ride-pool Assignment Problem (RAP) – a core component of ride-pooling systems, there is a lack of open-source implementations, making it difficult to benchmark these algorithms on a common dataset and objective. In this paper, we present the implementation details of a ride-pool simulator that encompasses several key ride-pool assignment algorithms, along with associated components such as vehicle routing and rebalancing. We also open-source a highly optimized and modular C++ codebase, designed to facilitate the extension of new algorithms and features. Additionally, we introduce a family of swapping-based local-search heuristics to enhance existing ride-pool assignment algorithms, achieving a better balance between performance and computational efficiency. Extensive experiments on a large-scale, real-world dataset from Manhattan, NYC reveal that while all selected algorithms perform comparably, the newly proposed Multi-Round Linear Assignment with Cyclic Exchange (LA-MR-CE) algorithm achieves a state-of-the-art service rate with significantly reduced computational time. Furthermore, an in-depth analysis suggests that a performance barrier exists for all myopic ride-pool assignment algorithms due to the system’s capacity bottleneck, and incorporating future information could be key to overcoming this limitation.]]></description>
      <pubDate>Wed, 01 Jul 2026 09:36:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684669</guid>
    </item>
    <item>
      <title>Bicycle Improvement Design Problem using real bike trajectories</title>
      <link>https://trid.trb.org/View/2684664</link>
      <description><![CDATA[The development of low-carbon mobility such as cycling is a crucial issue today, especially in urban areas where the extensive use of cars causes difficulties such as pollution, traffic congestion, and overcrowded parking. Existing literature highlights that a crucial factor in the adoption of bicycle commuting is the availability and density of bike networks (Schoner and Levinson, 2014; Buehler and Dill, 2016). In this context, computational methods can serve as decision support systems to determine how bicycle network should be expanded. Recent years have seen two key technological developments that can be integrated into these systems: (i) collaborative open data platforms such as OpenStreetMap that offer accurate information about cycling networks, and (ii) dedicated navigation apps for cyclists, such as Geovelo [from La Compagnie des Mobilités (Geovelo, 2009)], that record and analyze cycling journeys. In this study, we propose using a set of GPS tracks of the routes taken by cyclists to recommend budget-constrained improvements to cycling networks. Our objective is to determine where bicycle facilities should be located to improve overall user satisfaction. To address this problem we introduce a mathematical model and heuristics. In our approach, we show that individual GPS trajectories can play a crucial role in effectively improving the cycling network. First, the use of origin–destination pairs corresponding to actual trajectories is useful for determining where the network needs to be improved; second, actual bicycle trajectories are used to determine each cyclist’s trade-offs between distance and danger. These individual trade-offs are considered in our optimization model and result evaluation, allowing us to account for the heterogeneity of cyclists. The use of a representative set of trajectories is therefore essential. Experiments using real datasets are presented to evaluate the quality of the proposed methods.]]></description>
      <pubDate>Wed, 01 Jul 2026 09:36:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684664</guid>
    </item>
    <item>
      <title>New heuristics for the operation of an ambulance fleet under uncertainty</title>
      <link>https://trid.trb.org/View/2684367</link>
      <description><![CDATA[The operation of an ambulance fleet involves ambulance selection decisions about which ambulance to dispatch to each emergency, and ambulance reassignment decisions about what each ambulance should do after it has finished the service associated with an emergency. For ambulance selection decisions, we propose four new heuristics: the Best Myopic (BM) heuristic, a NonMyopic (NM) heuristic, and two greedy heuristics (GHP1 and GHP2). For ambulance reassignment decisions, we propose several strategies to choose which emergency in queue to send an ambulance to or which ambulance station to send an ambulance to when it finishes service. These heuristics are also used in a rollout approach: each time a new decision has to be made (when a call arrives or when an ambulance finishes service), a two-stage stochastic program is solved. The proposed heuristics are used to efficiently compute the second stage cost of these problems. We apply the rollout approach with our heuristics to data of the Emergency Medical Service (EMS) of a large city, and show that these methods outperform other heuristics that have been proposed for ambulance dispatch decisions. We also show that better response times can be obtained using the rollout approach instead of using the heuristics without rollout. Moreover, each decision is computed in a few seconds, which allows these methods to be used for the real-time management of a fleet of ambulances.]]></description>
      <pubDate>Fri, 26 Jun 2026 08:41:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684367</guid>
    </item>
    <item>
      <title>E-cargo bike route optimization with rider fatigue considerations: A chance-constrained programming approach</title>
      <link>https://trid.trb.org/View/2679286</link>
      <description><![CDATA[Electric cargo (e-cargo) bikes offer a promising, sustainable alternative for last-mile logistics. However, rider fatigue remains a critical, yet often overlooked, constraint, impacting both operational performance and rider well-being. This study introduces and formulates a Chance-Constrained Heterogeneous and Multi-Trip Vehicle Routing Problem (CC-HMVRP) that accounts for rider fatigue, incorporating load mass, environmental conditions, and rider characteristics into delivery planning. A mixed-integer linear programming (MILP) formulation and a modified adaptive large neighborhood search (ALNS) solution method are proposed to handle larger instances. We examine how wind speed and temperature influence battery and rider energy levels. Numerical results show a 27.2% reduction in total energy consumption compared to deterministic models, while riders retain 62.8% more available time before reaching fatigue. These findings enhance sustainability, improve efficiency, and support rider well-being, offering new insights for optimizing last-mile delivery operations under real-world constraints.]]></description>
      <pubDate>Thu, 25 Jun 2026 09:40:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679286</guid>
    </item>
    <item>
      <title>Stochastic production-distribution planning with transportation mode-dependent lead times</title>
      <link>https://trid.trb.org/View/2673125</link>
      <description><![CDATA[Plants and distribution centers often deliver their products to numerous customers spread over a vast territory and can thus rely on a combination of different transportation modes (such as road, air, rail, and maritime) to make their deliveries. These means of transportation have different costs but also different lead times, and there is thus a fundamental trade-off to be considered: a shorter lead time typically comes at a higher cost but offers more flexibility to react quickly to changes in demand. Hence, the plant faces the complex problem of making simultaneous production and transportation decisions, which include the selection of the transportation modes to use for shipping products to different customers. The objective is often to minimize the expected cost of production, transportation, inventory, and lost sales. We consider this problem in a setting with a discrete and finite time horizon during which customers face a stochastic demand. As such, this problem is an extension of the stochastic lot sizing problem. In each time period, the plant has to make production and transportation decisions before the demand is revealed. We solve the resulting multi-stage problem approximately in a rolling horizon framework that relies on a static-dynamic representation of the problem. To efficiently solve this static-dynamic problem, we present a tree-search heuristic based on Anytime Column Search and Limited Discrepancy Search. For the node selection strategy, we develop a guide heuristic that aggregates all the considered scenarios to quickly improve the current solution with respect to the set-up decisions of the current tree-search node. The tree-search framework and the guide heuristic are evaluated on medium-size instances and compared to CPLEX. The guide heuristic proves to be able to select solutions with an average gap below 0.1% compared to the best one while being 316 times faster than CPLEX on average. The presented results highlight the challenges that complex stochastic lot-sizing problems pose for general-purpose commercial solvers. The performance of the proposed framework underscores the necessity of developing tailored meta-heuristic architectures and efficient sub-problem formulations to navigate the computational complexity of multi-stage optimization under uncertainty.]]></description>
      <pubDate>Wed, 17 Jun 2026 12:23:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673125</guid>
    </item>
    <item>
      <title>TuneNSearch: A hybrid transfer learning and local search approach for solving vehicle routing problems</title>
      <link>https://trid.trb.org/View/2673243</link>
      <description><![CDATA[This paper introduces TuneNSearch, a hybrid transfer learning and local search approach for addressing diverse variants of the vehicle routing problem (VRP). Our method uses reinforcement learning to generate high-quality solutions, which are subsequently refined by an efficient local search procedure. To ensure broad adaptability across VRP variants, TuneNSearch begins with a pre-training phase on the multi-depot VRP (MDVRP), followed by a fine-tuning phase to adapt it to other problem formulations. The learning phase utilizes a Transformer-based architecture enhanced with edge-aware attention, which integrates edge distances directly into the attention mechanism to better capture spatial relationships inherent to routing problems. We show that the pre-trained model generalizes effectively to single-depot variants, achieving performance comparable to models trained specifically on single-depot instances. Simultaneously, it maintains strong performance on multi-depot variants, an ability that models pre-trained solely on single-depot problems lack. For example, on 100-node instances of multi-depot variants, TuneNSearch outperforms a model pre-trained on the CVRP by 44%. In contrast, on 100-node instances of single-depot variants, TuneNSearch performs similar to the CVRP model. To validate the effectiveness of our method, we conduct extensive computational experiments on public benchmark and randomly generated instances. Across multiple CVRPLIB and TSPLIB datasets, TuneNSearch consistently achieves performance deviations of less than 3% from the best-known solutions in literature, compared to 6%–25% for other neural-based models, depending on problem complexity. Overall, our approach demonstrates strong generalization to different problem sizes, instance distributions, and VRP formulations, while maintaining polynomial runtime complexity despite the integration of the local search algorithm.]]></description>
      <pubDate>Wed, 17 Jun 2026 12:23:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673243</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>
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