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    <title>Transport Research International Documentation (TRID)</title>
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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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Digitalising the sales process for e-commerce warehouse services at a logistics service provider</title>
      <link>https://trid.trb.org/View/2652014</link>
      <description><![CDATA[Online shopping has grown in popularity in recent years, and e-commerce is expected to continue expanding. Logistics service providers (LSPs) play a crucial role in e-commerce, as e-tailers outsource transportation and warehousing to focus on their core business. E-commerce represents a significant new market, but rising operating costs, labour shortages and limited warehouse space necessitate enhanced efficiency and productivity. Traditionally, LSPs have tailored their services to individual client needs, but balancing this customisation with a growing client base is challenging. Thus, standardising services and digitalising sales processes are essential to meeting this demand. This study explores the development of a sales tool designed to digitalise the sales process for e-commerce warehouse services at a leading global LSP through action research. The case study showed that the approach could guide LSPs in the development of a sales tool, streamline business processes and centralise data, potentially improving data quality in the future.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652014</guid>
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    <item>
      <title>Robotic sorting systems: Robot management and layout design optimization</title>
      <link>https://trid.trb.org/View/2647676</link>
      <description><![CDATA[In the contemporary logistics industry, automation plays a pivotal role in enhancing production efficiency and expanding industrial scale. In particular, autonomous mobile robots have become integral to modernization efforts in warehouses. One noteworthy application in robotic warehousing is the robotic sorting system (RSS), which is distinguished by its cost-effectiveness, simplicity, scalability, and adaptable throughput control. Previous research on RSS efficiency often assumed an ideal robot management system, ignoring potential traffic delays and assuming constant travel times. To address this gap, we introduce a novel robot traffic management method, named Rhythmic Control for the Sorting Scenario (RC-S), for RSS operations, along with an analytical estimation formula that establishes the quantitative relationship between system performance and configurations. Simulations validate that RC-S reduces average service time by 10.3 % compared to the classical cooperative A* algorithm, while also improving throughput and runtime. Based on the performance analysis of RC-S, we develop a layout optimization model that considers system configurations, desired throughput, and costs to minimize expenses and determine the optimal layout. Numerical studies show that facility costs dominate at lower throughput levels, while labor costs prevail at higher throughput levels. Additionally, due to traffic efficiency limitations, an RSS is well-suited for small-scale operations like end-of-supply-chain distribution centers.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647676</guid>
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    <item>
      <title>H²DGL: Adaptive Metapath-Based Dynamic Graph Learning for Supply Forecasting in Logistics System</title>
      <link>https://trid.trb.org/View/2617705</link>
      <description><![CDATA[The advanced logistics systems are increasingly transitioning towards integrated warehousing and distribution supply networks (IWDSN), where accurately forecasting supply capacity is essential for maintaining delivery capabilities that meet user demands. However, existing research often overlooks the impact of dynamic changes in network topology, resulting in limitations in capturing dynamic routing and diverse node responses. These limitations become particularly pronounced in the context of external events such as pandemics, heavy rain, and promotions. To address the above limitations, we propose  $\mathtt {H^{2}DGL}$ , a Hierarchical Heterogeneous Dynamic Graph Learning framework based on adaptive metapath aggregation, for forecasting supply capabilities in logistics systems. Specifically,  $\mathtt {H^{2}DGL}$  comprises three main modules: (1) Hierarchical Heterogeneous Node Representation, where the micro graph captures dynamic routing information through adaptive meta-path aggregation from routing and event view graphs, and the macro graph extracts spatial representations using bipartite graph learning. (2) The Dynamic Graph Encoding module integrates macro and micro features from different snapshots to derive unified node representations. (3) The Spatio-temporal Joint Forecasting combines spatial features with temporal features from a time-series encoder to predict future supply capacity. Extensive experiments on two real-world datasets from different cities demonstrate that  $\mathtt {H^{2}DGL}$  achieves state-of-the-art performance compared to advanced baseline models. The code is available at https://github.com/kaiwxai/H2DGL]]></description>
      <pubDate>Wed, 25 Mar 2026 17:11:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617705</guid>
    </item>
    <item>
      <title>An enhanced dynamic programming approach to the deterministic time-dependent lot-sizing problem under shared warehousing</title>
      <link>https://trid.trb.org/View/2645504</link>
      <description><![CDATA[The lot-sizing problem for one-to-one distribution with time-dependent demand has long been studied in logistics at the operational level. Conventionally, the heuristic-based forward dynamic programming method is usually utilized for the case considering only the trade-off among operational costs, such as transportation and inventory costs. With the onset of e-commerce, many small- and medium-sized e-retailers that have insufficient volume to warrant a dedicated facility are now outsourcing warehousing and logistics services. A single warehouse today may be shared by multiple e-retailers with time-dependent space requirements. Thus, it becomes critical to incorporate the shared warehousing cost, or the rent cost, which was often considered at the tactical level into the lot-sizing problem. This paper deals with the optimal lot-sizing problem in which rent cost and other operational costs are considered jointly. Four necessary conditions for the optimal solution are identified. An enhanced forward dynamic programming approach based on the identified necessary conditions is proposed to solve this multi-objective optimization problem. The performance of the proposed approach is examined for a broad range of cost parameters and demand functions. The results indicate that the proposed approach is promising in terms of computation time and solution quality.]]></description>
      <pubDate>Fri, 20 Mar 2026 08:41:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2645504</guid>
    </item>
    <item>
      <title>An analysis of the ailments in inventory management in the Indian warehouses</title>
      <link>https://trid.trb.org/View/2639332</link>
      <description><![CDATA[Warehousing has been a prominent and efficient tool for developed countries, and it has aided their economy at large with manageable costs and effective control. India, despite being one large country, is yet to set its foot in the warehousing section. Due to an almost absenteeism of warehousing or little warehousing structure India faces highest amount of food, grain, materials wastage. This study concentrates itself on setting up better, efficient, cost-controlled, and result-oriented warehouses and in turn a better economy for the nation. Warehousing provides proficient and hygienic storing facilities of goods to ensure a continuous and timely flow of goods to the market and consumers. It protects perishable and semi-perishable items from deterioration. It maintains and controls the demand and supply chain even for the seasonal commodities. It stabilises prices and helps keep them at an affordable range. The sufferings with the likes of infrastructure, land availability, credit, political interferences, proper segments and their role, taxes, labour, and it is training, etc. have added to the miseries of warehousing. Hence there is sure and need immediate requisite for proper and analytical research which could mend this torn, but important industry.]]></description>
      <pubDate>Thu, 12 Mar 2026 08:49:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2639332</guid>
    </item>
    <item>
      <title>“Keeping up with changing customer demand”: An adaptive data-driven approach for storage and repositioning decisions in automated g warehouses</title>
      <link>https://trid.trb.org/View/2659407</link>
      <description><![CDATA[In warehouses, products are often not stored in their optimal positions, elongating retrieval and order picking time. A main reason is that storage assignment is based on historical demand frequency, whereas current demand patterns might just differ. However, as many warehouses are now automated or robotized, opportunities exist to dynamically and opportunistically reposition product loads based on real known demand and still reduce the makespan (the total time needed for retrieval, storage, and optional repositioning). We investigate the optimal retrieval of a known block of requests by explicitly additionally allowing in-between repositioning options. Surprisingly, in spite of the extra work and time involved, we show opportunistic repositioning may indeed be beneficial for reducing the makespan. We study the problem for two automated unit-load storage warehouses: automated storage and retrieval (AS/R) crane-based systems and robotic mobile fulfillment (RMF) systems, which have different travel metrics for the retrieval robots. The data-driven storage and repositioning (DDSR) problem, formulated as an integer linear program, leverages actual customer order data. The problem appears to be intractable for realistic systems due to the combinatorial nature of the possible repositions. We then reformulate the model, making it more tractable for moderate-sized problems. This model appears to beat real-life storage assignment heuristics like closest-open location assignment or demand-frequency class-based storage (even when these have full foresight of demand changes). The benefits appear to be around a 14%-30% shorter makespan, depending on the number of loads to be retrieved. For larger rack space utilization, the benefits decrease (since there are fewer options for repositioning). The method is sufficiently fast to be used in real warehouse systems, e.g., by using a rolling horizon policy where repositions are calculated for the next block of requests while the current requests are executed. Our method offers managers an additional powerful tool to reduce system response time and thereby increase throughput capacity by smarter scheduling of their automated equipment and more efficient use of available storage space.]]></description>
      <pubDate>Mon, 02 Mar 2026 08:55:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659407</guid>
    </item>
    <item>
      <title>A branch-and-price algorithm for task allocation and global path planning of multiple AGVs in intelligent warehouses</title>
      <link>https://trid.trb.org/View/2641022</link>
      <description><![CDATA[Automated guided vehicles (AGVs) are essential components of modern intelligent warehouse logistics systems, serving as the cornerstone of efficient material handling. Their growing importance highlights their vital role in enhancing the functionality and effectiveness of intelligent warehouses in today’s industries. These material handling systems encompass three key aspects of decision-making: task-to-AGV assignment, AGV path planning, and the scheduling of arrival and departure times for each AGV at various stations. While extensive research has focused on these elements independently, integrated studies addressing the problem comprehensively remain relatively rare. This study proposes a co-optimization problem for task allocation and global path planning for multiple AGVs in intelligent warehouse systems. It effectively integrates dispatching, conflict-free routing, and scheduling of these AGVs. We formulate the problem as a mixed-integer linear programming (MILP) model to minimize the delay time for all tasks and the operational time for all AGVs. Given that this problem is NP-hard, solving the MILP model efficiently for realistic-scale instances is challenging. To tackle the complexities involved, we developed a tailored branch-and-price (BP) algorithm specifically designed for small- to medium-scale problems, complemented by an efficient heuristic algorithm tailored for larger-scale challenges. Enhancements to the BP algorithm’s performance were achieved by incorporating several acceleration techniques that cater to the specific characteristics of our problem. Our experimental results reveal three key findings: (i) the BP algorithm effectively addresses the problem, (ii) the heuristic serves as a viable standalone solution for large-scale scenarios, while also providing high-quality initial solutions for the BP algorithm promptly, and (iii) the introduced acceleration methods significantly reduce the computational time required by the BP algorithm. Overall, our paper presents a robust and tailored approach to AGV material handling systems, providing valuable insights for warehouse operators and supporting their decision-making processes.]]></description>
      <pubDate>Tue, 30 Dec 2025 09:46:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2641022</guid>
    </item>
    <item>
      <title>Trade-offs in warehousing storage location reassignment</title>
      <link>https://trid.trb.org/View/2593713</link>
      <description><![CDATA[In the low-level picker-to-parts warehouses the order picking is the most time- and cost consuming process. The performance indicator for order picking is lead time. The system can be considered efficient if these lead times can be kept low, but this is heavily influenced by the storage location assignment in the warehouse, the routing, and the warehouse layout. The objective of this research is to investigate in what cases and to what extent reassignment and repositioning tasks following efficiency deterioration as well as seek answers to how to minimise these costly tasks and maintain a near-ideal storage location assignment. To solve this problem, an intelligent system concept is presented, which aims to support the warehouse operator in making replenishment decisions, which picking storage to replenish based on the current rotation of item, and which products to repositioning, while maintaining a near ideal storage location assignment. The aim of this paper is to highlight the potential decision points and circumstances, when adaptive storage location reassignment would be necessary and how this concept can help everyday warehouse logistics.]]></description>
      <pubDate>Tue, 18 Nov 2025 11:04:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593713</guid>
    </item>
    <item>
      <title>The changing and complexifying geography of logistics activity in U.S. Metropolitan areas</title>
      <link>https://trid.trb.org/View/2598086</link>
      <description><![CDATA[This paper examines the spatial distribution of logistics activity (trucking & warehousing) across and within fifty of the largest US metropolitan areas between 2005 and 2020, using Gini coefficients at the county level to measure concentration. Previous research has shown decentralization corresponding with suburbanization and the growth of larger warehouses to meet consumer demand. However, the rapid growth in e-commerce since 2005 suggests that re-centralization may be occurring as logistics providers develop fulfillment centers closer to the centers of population. In this paper, we find that this re-centralization does in fact seem to be occurring, but in a more complicated pattern than before. Warehousing is still decentralizing in logistical hubs and cities in the shadow of those hubs but is re-concentrating in other, smaller metropolitan areas that serve as local and regional logistics centers. We find strong evidence for the formation of a national logistics network, with different cities playing different roles. Furthermore, rather than one spatial pattern of decentralization for logistics activity within metropolitan areas, we find multiple possible patterns, corresponding to the city's location in that national network.]]></description>
      <pubDate>Fri, 14 Nov 2025 08:46:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598086</guid>
    </item>
    <item>
      <title>Omnichannel transportation, warehousing and manufacturing quality characteristics in a post-COVID environment: case and empirical study</title>
      <link>https://trid.trb.org/View/2550910</link>
      <description><![CDATA[The transportation and warehousing industry include industries providing logistical services of passengers and cargo, warehouses for storage, and support activities related to transportation - companies within this industry-use transportation equipment and related facilities as a productive asset. A case study of two Pittsburgh, PA transportation companies and an empirical study of 1,008 respondents from the same service yielded information about the growth of AI, automation and cloud business intelligence to provide quality-added activities, especially for working professionals. In general, although low-cost tactics counter the value-added through high quality, both sexes felt that there is a need for high-quality manufactured goods, but the low cost can be a mitigating factor. Keeping operational and transportation costs low and transferring these savings in lower prices and shipping costs to the consumer are strategic advantages for these companies.]]></description>
      <pubDate>Thu, 26 Jun 2025 11:42:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2550910</guid>
    </item>
    <item>
      <title>Enabling within-the-hour fresh food deliveries: Integrated order batching and zone-picking through overhead conveyors</title>
      <link>https://trid.trb.org/View/2544594</link>
      <description><![CDATA[New retail concepts that embrace a hybrid “online + offline” business paradigm promise super-fast order fulfillment of groceries within the next hour. In this ”online + offline” retail scenario, it is crucial to efficiently fulfill many online orders within the stipulated time while adhering to the layout rules of offline retail products. Zone-picking and overhead conveyors have been introduced to cope with the significant volume of orders batching, picking, and delivering fresh products. This has led to a new integrated order batching and picking decision problem, aiming for human-machine reconciliation in Industry 5.0. For such a problem, two new mixed integer linear programming models are developed, considering minimizing the number of picking task releases and the total delay time of all orders. The computational complexity of the two problems is provided. A customized two-stage heuristic framework is developed to solve the two models with distinct solution space structures. Numerical experiments have been conducted to test the performance of the proposed methods and provide solution analysis for practical insights. The results show that the proposed heuristic reduces the number of picking tasks for workers by 19% and the total delay in completing orders by 74% compared to prevailing store practices. The proposed framework complements the existing models in the literature. It contributes to developing a comprehensive analysis of order picking by integrating human factors into operational efficiency improvement in the new retailing industry.]]></description>
      <pubDate>Wed, 28 May 2025 10:13:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2544594</guid>
    </item>
    <item>
      <title>An enhanced Modelling approach for warehouse sharing platform system designing problem</title>
      <link>https://trid.trb.org/View/2548236</link>
      <description><![CDATA[With the increasing importance of sustainability, warehouse sharing arises as a possible way to improve the logistics system efficiency. This paper studied the warehouse sharing platform systems (WSPS) and proposed an enhanced modelling approach for the WSPS design problem (WSPSDP) using the multi-allocation hub location routing problem framework. New elements, such as inter-warehouse transportation and multi-allocation scheme, were considered compared to the existing WSPS model. Then, an adaptive large neighbourhood decomposition search heuristic was applied to solve the problem. Computational experiments were conducted on different-sized instances to compare the WSPS model without inter-warehouse transportation (WSPSDP-WI) and the WSPS model with single-allocation scheme (WSPSDP-SA). The results suggested that the proposed WSPSDP model is more cost-efficient than the existing WSPS models, reducing 15.38% and 1.43% operation costs compared to the WSPSDP-WI and WSPSDP-SA models. Finally, the proposed WSPSDP model also has the potential to promote the utilisation of existing cheap idle warehouses.]]></description>
      <pubDate>Tue, 20 May 2025 16:13:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548236</guid>
    </item>
    <item>
      <title>Augmented reality technologies application in the warehouse system</title>
      <link>https://trid.trb.org/View/2528620</link>
      <description><![CDATA[Warehousing objects are an integral part of distribution and reverse logistics. These are operational objects where goods are stored in the short or long term and eventually processed. Within each process-organized business, there is a group of inbound and outbound processes, and their implementation requires the optimal level of internal activities, in terms of speed and quality of performance in order to maintain the level of competitiveness in the market. Due to the increase in service quality level and following the development of Augmented Reality (AR) technology, auxiliary systems for logistics process optimization are gradually being developed. Augmented reality includes a visual perception of the real-world physical environment enhanced by computer devices. The paper analyzes relevant existing research in the field of AR application in warehouse facilities to optimize warehouse processes. The narrower purpose of the paper is to emphasize the possibility of increasing the quality level of warehousing processes by introducing AR technology. The paper concludes with highlighted guidelines for future research that will be focused on connecting the AR application to the performance of warehouse processes in specific working conditions.]]></description>
      <pubDate>Tue, 20 May 2025 11:37:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2528620</guid>
    </item>
    <item>
      <title>Impacts of E-commerce on Warehousing and Distribution in California</title>
      <link>https://trid.trb.org/View/2543845</link>
      <description><![CDATA[The purpose of this research is to document and analyze trends in location patterns of warehousing and distribution (WD) activity in California over the past decade, and to explore the relationship between these trends and the growth of e-commerce. This research builds on a previous study of WD trends in California 2003-2013 and extends to 2022. The research has two parts. Part 1 is a descriptive analysis of WD trends, Part 2 estimates models to explain these trends. There was an approximate doubling of WD establishments over the period, but the overall spatial distribution of activity was markedly stable. There is no evidence of decentralization; growth took place throughout the state’s metro areas. The authors estimate both cross section and time series models, finding that local market attributes consistently explain WD location. Transport access plays a less significant role. The authors conclude that continued growth even in high density core areas is consistent with the rapid growth in e-commerce that took place over the same period.]]></description>
      <pubDate>Mon, 05 May 2025 08:56:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2543845</guid>
    </item>
    <item>
      <title>The logistics trade-off between D&amp;D and warehousing costs</title>
      <link>https://trid.trb.org/View/2537183</link>
      <description><![CDATA[This study analyses the impact of container demurrage and detention (D&D) on the total logistics costs (TLC) of a shipper. More specifically, it looks into how these costs influence the decision to store cargo in a container throughout the hinterland chain or in a warehouse. To do this, a case study with company-specific data is implemented. The findings show, first of all, that the lead time plays an important role in whether D&D occurs. Longer lead times exceeding the D&D free time result in D&D costs which increase the shippers’ TLC considerably. Second, the impact of D&D on the TLC is shipping line dependent. Hence, shippers can limit the TLC increase by choosing shipping lines that offer more free time for intermodal transport. Third, increases in the warehousing cost component can result in higher TLC compared to the TLC when D&D charges occur, suggesting that it might become more interesting for the shipper to store its goods in the container throughout the hinterland chain and thus pay D&D instead of in a warehouse. These results, although case-dependent, give insights into alternative storage options throughout the chain and their costs for shippers as well as that incurring D&D charges can be acceptable under certain logistics strategies.]]></description>
      <pubDate>Tue, 29 Apr 2025 09:23:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2537183</guid>
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