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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>An Improved K-Means Clustering Algorithm Based on Eiq Analysis for Order Batching of Shuttle-Based Storage/Retrieval Systems</title>
      <link>https://trid.trb.org/View/2714393</link>
      <description><![CDATA[Shuttle-based storage/retrieval systems (SBS/RS) require efficient order batching to optimise split-case picking. Original K-means clustering, which groups orders based on overlapping SKUs to minimise bin presentations, struggles with high-dimensional, sparse pharmaceutical data due to computational inefficiency, unsuitable distance metrics and unstable initialisation. We propose an enhanced K-means algorithm based on EIQ analysis. High-frequency SKUs are selected using IK frequency filtering, while Pearson correlation is applied to remove redundant features and reduce dimensionality. Cluster centre initialisation is improved using a roulette-based strategy, and cosine distance replaces Euclidean distance to better capture SKU similarity. Case studies using real data from Company A show that the proposed method outperforms both first-come-first-serve (FCFS) and standard K-means in reducing bin presentations and enhancing processing stability. The algorithm remains robust regardless of SKU popularity shifts. Sensitivity analysis confirms strong performance within appropriate thresholds for feature selection (n: 20–25) and correlation filtering (Pearson correlation: 0.8–0.9). Furthermore, as the number of item-lines per order increases, the improved algorithm yields greater efficiency gains. This algorithm can also be well applied to other industries. © 2026 Faculty of Transport and Traffic Engineering.]]></description>
      <pubDate>Wed, 16 Sep 2026 11:21:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714393</guid>
    </item>
    <item>
      <title>The Robust Two-Echelon Inventory Routing Problem for Vaccine Supply System – a Heuristic Method</title>
      <link>https://trid.trb.org/View/2714392</link>
      <description><![CDATA[Vaccine supply chains confront several challenges in guaranteeing security of vaccines from the phase of production to the injection. Thus, it is crucial to construct a robust vaccine supply system that helps store and deliver vaccines safely and effectively. Inspired by a real-world vaccine supply case, this paper studies a robust two-echelon inventory routing problem that integrates the optimisation of vendor managed inventory (VMI), inventory policy and vehicle routing. A deterministic model is formulated to jointly optimise two-echelon vehicle routes as well as inventory at both distribution warehouse and vaccine demand nodes. Then, a robust modelling approach is introduced and converted into an equivalent linear form. An efficient solution algorithm is proposed, merging simulated annealing (SA) and adaptive large neighbourhood search (ALNS) to address large-scale cases. The computational efficiency of the proposed algorithm is demonstrated through numerical comparisons to the solutions founded by CPLEX and ALNS. The relation between input parameters and optimised results is investigated by a real-world vaccine supply system. The findings from results and sensitivity analyses suggest some interesting managerial implications.]]></description>
      <pubDate>Wed, 16 Sep 2026 11:21:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714392</guid>
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    <item>
      <title>Synthesis of Information Related to Transit Practices. Topic SG-23. Data-Driven Approaches for Optimizing Transit Parts Procurement, Lead Time Risk, and Critical Spare Parts Planning</title>
      <link>https://trid.trb.org/View/2772554</link>
      <description><![CDATA[Transit agencies depend on the timely availability of spare parts to maintain fleet reliability, minimize vehicle downtime, and sustain safe and efficient service. Agencies employ a variety of approaches to procure, stock, and manage spare parts inventories; however, practices vary considerably based on fleet composition, agency size, operating environment, warehouse capacity, staffing resources, and supplier relationships.

In recent years, transit agencies have experienced increasing challenges associated with extended supplier lead times, supply chain disruptions, parts obsolescence, and limited availability of critical vehicle components. At the same time, agencies are making greater use of maintenance management systems, enterprise resource planning (ERP) systems, inventory management software, vehicle diagnostic data, asset management programs, and other operational data to improve procurement and inventory decision-making.

Although many agencies have developed innovative practices to address these challenges, there has been limited documentation of current industry approaches for managing spare parts inventories, mitigating supply chain risks, identifying critical spare parts, and using data to support procurement decisions. A synthesis would provide transit agencies with a comprehensive understanding of current practices, emerging approaches, and lessons learned across the industry.

OBJECTIVE: The objective of this synthesis is to document current practices used by transit agencies to optimize spare parts procurement, inventory management, supplier lead time risk, and critical spare parts planning. The synthesis should identify how agencies use data, technology, and operational practices to improve parts availability, reduce vehicle downtime, and support reliable transit service.

]]></description>
      <pubDate>Tue, 01 Sep 2026 17:16:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2772554</guid>
    </item>
    <item>
      <title>Unraveling Cross Docking: Operations Performance, Demand Variation, and Fulfillment Mechanism</title>
      <link>https://trid.trb.org/View/2704265</link>
      <description><![CDATA[Cross docking (CD) is a supply chain strategy that moves products from the inbound dock directly to the outbound dock with little or no storage at a warehouse. Using product-level data collected from one of the largest supermarket chains in China which consists of 71 supermarket stores and a single warehouse and a cross-docking station located in the DC, we develop econometric models to evaluate the operations performance, the effect of demand variation, and the fulfillment mechanism of cross docking (CD). We find that the benefits of CD are more nuanced than we thought. At the stores, on average, CD does not affect on-hand inventory but increases days of stockouts compared to traditional warehousing (TW). The effects differ in products’ demand characteristics. CD products with lower demand or higher demand variation have lower on-hand inventories than TW products, and CD products with higher demand or higher demand variation have lower days of stockouts than TW products. We do not find the heterogenous effect of the supply-side factor on CD’s performance. At the distribution center (DC) and system, CD reduces the inventories by as much as 93.68% and 25.86% on average, respectively, suggesting that CD not only reduces the total inventory in the system but also re-distributes the inventory from the DC to the stores for some products. Our findings, supported by our interviews with store and DC managers, suggest that increased fulfillment uncertainty resulting from imperfect order consolidation and synchronization in CD may be the sources of performance degradation.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704265</guid>
    </item>
    <item>
      <title>Integrated Optimization of Inventory Routing, Transshipments, and Backordering: A Fix-and-Optimize Matheuristic</title>
      <link>https://trid.trb.org/View/2713878</link>
      <description><![CDATA[This paper addresses a multi-product, multi-vehicle Inventory Routing Problem with Transshipments and Backordering (IRPTB), integrating replenishment, lateral inventory sharing, and backordering decisions within a unified framework. The problem is formulated as a mixed-integer linear program (MILP) and solved using a matheuristic based on the Fix-and-Optimize (F&O) paradigm, designed to balance solution quality and computational efficiency. Extensive computational experiments on benchmark instances under varying demand variability levels demonstrate that the proposed approach consistently delivers near-optimal solutions with substantial computational gains over the CPLEX solver. Across moderate, high, and extreme demand ranges, the matheuristic achieves average relative gaps below 2.2%, 1.5%, and 1.7%, respectively, while reducing computation times by factors ranging from 3× to 15×. The results further indicate strong robustness of the approach, with limited sensitivity to increasing demand variability. From a managerial perspective, the findings confirm that combining transshipments with backordering improves service levels by alleviating lost sales and reducing inventory imbalances across the network. Overall, the proposed F&O matheuristic offers an effective and scalable solution method for complex inventory routing problems under demand variability.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2713878</guid>
    </item>
    <item>
      <title>Backorder prediction in supply chain management - comparing the contribution of machine learning and LSTM classifier in an industrial context</title>
      <link>https://trid.trb.org/View/2693695</link>
      <description><![CDATA[In this study, the comparative analysis between various machine learning techniques and LSTM (deep learning classifier) are employed to predict demand and backorders based on previous sales, national inventory, the minimum recommended local stock, pipeline inventory, and lead time. Various machine learning algorithms are used for evaluating three-month, six-month, and nine-month data for predicting backorder. The performance metrics considered in study are accuracy, precision, F1-score, AUC values, MAE, RMSE, and log-loss. A dataset from Kaggle's repository has been taken to conduct experiments for backorder prediction the findings of various experiments favour AdaBoost classifier, which has AUC value 82.6% for three-month forecasting. Random forest provides 99.3% AUC value for six-month and 85.65% for nine-month prediction. Conversely, the deep learning (LSTM) classifiers provide AUC values of 89.37%, 91.51% and 80.76% for three-month, six-month, and nine-month prediction processes respectively. LSTM gives AUC, log-loss and MSE values much better compare to other model. In the end, it is found that the bagging classifier provides better results as compared to the single classifier, and is even much better than deep learning (LSTM) classifier, there are not many classifiers that can compete with AdaBoost and random forest in terms of accuracy.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:50:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693695</guid>
    </item>
    <item>
      <title>A multi-item supply chain with credit periods and weight freight cost discounts</title>
      <link>https://trid.trb.org/View/2690746</link>
      <description><![CDATA[It is possible to realize considerable savings by aggregating the replenishment of a variety of items in a multi-item supply chain. This joint multi-item replenishment policy has already been widely applied in a variety of industries. This type of policy may make it possible for the retailer to take advantage of transport economies of scale by the utilization of freight discounts for greater weight. In addition, a supplier will often extend forward financing to a retailer. In this paper, a multi-item supply chain with a credit period and weight freight cost discounts is considered. The retailer bears the freight costs, but the freight carrier provides freight-transport discounts that are positively related to the weight of the cargo transported. From both the individual and the channel perspectives, we deal with the dual problems of determining the ideal supplier credit period, and of the best way for the retailer to make multi-item replenishment and pricing decisions, while still maximizing profits. We outline the optimal properties and develop algorithms for solving the problems described, as well as discuss the impact of the freight cost discounts, the inventory holding cost, and the interest rate on the behavior of both parties.]]></description>
      <pubDate>Mon, 13 Jul 2026 10:45:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2690746</guid>
    </item>
    <item>
      <title>The impact of e-commerce development on inventory management</title>
      <link>https://trid.trb.org/View/2698119</link>
      <description><![CDATA[The objective of this paper was to analyze the impact of e-commerce on inventory management through a case study. The rapid development of e-commerce, particularly during the COVID-19 pandemic, placed significant pressure on supply chains to manage a substantial increase in shipment volumes. While it became evident that the implementation and growth of online stores led to higher sales and increased inventory turnover, it raised the question of how stock levels were affected and what factors influence these changes. Using the example of an automotive retailer and wholesaler, the case study demonstrates that there is no universal approach to inventory management; outcomes depend largely on the specific business model. In this case, it was shown that a centralized warehouse can successfully serve both online and physical stores without an increase in stock levels, but with a noticeable reduction in stock holding time.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698119</guid>
    </item>
    <item>
      <title>Inventory routing with heterogeneous vehicles and hazardous material backhauling</title>
      <link>https://trid.trb.org/View/2622419</link>
      <description><![CDATA[Efficient coordination of distribution and backhauling is a critical challenge for many industries. This paper is motivated by a real-world case study at Hydro-Québec, a large-scale utility company in North America, and introduces an inventory routing problem that integrates inventory management and vehicle routing under several operational constraints. The problem involves distributing multiple commodities to customer sites while backhauling hazardous materials to depots. The objective is to minimize delivery, collection, and inventory holding costs using a fleet of capacitated heterogeneous vehicles, while ensuring that hazardous materials are transported separately from regular delivery commodities. In each period, a customer’s delivery and backhauling can be split and satisfied by multiple vehicles. We propose a mathematical formulation, introduce valid inequalities, and solve the resulting model using a branch-and-cut algorithm. To tackle large-size instances, a two-phase decomposition matheuristic is developed. To highlight the value of split delivery and backhauling, we compare the solutions from our model with those when split delivery is prohibited and when backhauling is optimized independently. In addition, we investigate the order-up-to level policy and the case when stockout is allowed. An extensive numerical study is conducted on synthetic instances to evaluate the performance of the models and solution approaches. The heuristic algorithm solves the synthetic instances in less than two hours with an average optimality gap of less than 2 %. Finally, a case study is conducted on the Hydro-Québec network to demonstrate the real-world applicability of the model and quantify the benefits to the company. Our proposed model reduces the total routing costs by 21% compared to the case where backhauling is not integrated and split delivery is not allowed.]]></description>
      <pubDate>Thu, 18 Jun 2026 09:09:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2622419</guid>
    </item>
    <item>
      <title>Optimal fulfillment and transshipment strategies in omnichannel retailing with cross-channel returns</title>
      <link>https://trid.trb.org/View/2669998</link>
      <description><![CDATA[The popularity of online shopping has been steadily increasing among consumers, especially after the COVID-19 pandemic. To meet growing customer demand and remain competitive, retailers are increasingly focusing on leveraging online sales growth, particularly adopting cross-channel strategies. However, operating online and offline channels seamlessly and efficiently presents significant challenges. To address these challenges, we develop a dynamic programming model that determines optimal fulfillment and transshipment strategies for omnichannel retailing with cross-channel returns. We consider a retailer operating both online and offline channels, each carrying its own inventory. We allow omnichannel fulfillment (i.e., online orders can be fulfilled from any channel) and cross-channel returns (i.e., online orders can be returned online or to any store, while store purchases can only be returned to any store location). Our study provides insights into a retailer’s initial inventory allocation and dynamic fulfillment and transshipment decisions that maximize total profit under uncertainty given customer returns. We determine the optimal initial inventory allocation across channels, jointly with fulfillment and transshipment decisions. Specifically, we address three key decisions simultaneously: (i) how to allocate initial inventory (ii) where to fulfill an online order over time, and (iii) when and where to transship items to balance inventory over time. To support practical implementation, we develop two computationally efficient heuristics that approximate optimal decisions in complex omnichannel settings.]]></description>
      <pubDate>Tue, 26 May 2026 09:40:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669998</guid>
    </item>
    <item>
      <title>Smart Drop-Shipping and Stocking Decision Support System</title>
      <link>https://trid.trb.org/View/2703794</link>
      <description><![CDATA[Drop-shipping is an increasingly important order fulfillment strategy in modern supply chains, allowing firms to reduce inventory holding costs by shipping products directly from suppliers to customers. However, because inventory is not directly controlled by the firm, drop-shipping can introduce uncertainty in product availability, delivery lead times, and service reliability. To compensate, firms often rely on expedited transportation, which increases costs and may negatively affect safety and efficiency in freight operations. These trade-offs create a challenging decision problem: determining which products should be stocked internally, fulfilled through drop-shipping, or managed under a mixed fulfillment strategy.
Industry interviews with a major U.S. wholesaler indicate that firms tend to rely on drop-shipping for slow-moving products due to limited warehouse space and capital constraints, yet lack systematic, data-driven methods to guide these decisions Existing research largely focuses on single-product settings or coordination issues between retailers and suppliers and does not address multi-product decisions under warehouse capacity constraints.
This project aims to fill this gap by developing an optimization-based decision support framework for drop-shipping and inventory planning across multiple stock-keeping units (SKUs). The proposed approach integrates mixed-integer programming with meta-heuristic methods to support large-scale, real-world applications. The model incorporates demand patterns, inventory holding costs, transportation costs, service level requirements, and cash flow constraints. A complementary simulation framework will be developed to evaluate system performance under uncertainty in demand, supplier inventory availability, and delivery times.
The project supports Mid-America Transportation Center (MATC) themes of Safety and Transportation Systems of the Future by enabling more predictable and efficient freight movements, reducing reliance on expedited shipping, and promoting data-driven planning in distributed fulfillment networks. Expected outcomes include an implementable decision support tool, analytical insights for industry stakeholders, and dissemination through publications and conference presentations.]]></description>
      <pubDate>Sat, 16 May 2026 11:49:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703794</guid>
    </item>
    <item>
      <title>The sustainable reverse inventory-routing problem with time windows and simultaneous pickup and delivery</title>
      <link>https://trid.trb.org/View/2663938</link>
      <description><![CDATA[Circular economy principles are increasingly central to sustainable supply chain management, as they foster closed-loop systems that reduce waste, extend product lifecycles, and promote resource efficiency. In particular, they have strong implications for the social dimension, since reverse logistics and reuse systems directly influence working conditions. Optimizing transport operations is crucial for minimizing environmental impact, reduce operational costs and enhance social outcomes such as operator well-being. This paper addresses the Inventory Routing Problem with Pickup and Delivery with time window (IRP-PD-TW) in the context of pallet logistics, focusing on the distribution of loaded pallets and the collection of empty ones.The problem we tackle lies in the lack of integrated optimization approaches that jointly consider economic, environmental, and social criteria within this context. To address this gap, we propose a multi-objective Mixed Integer Linear Programming (MILP) model designed to optimize multiple objectives: minimizing total costs, reducing CO₂ emissions by accounting for both load and distance factors, and improving driver welfare. The model is implemented and solved using Gurobi, with validation conducted on literature instances. Additionally, the problem is tackled using two metaheuristic approaches, namely NSGA-II and SPEA2, which leverage Pareto optimization to explore trade-offs among the objectives. The results demonstrate the effectiveness of both methods in achieving balanced optimization of economic, environmental, and social objectives, thus offering a comprehensive approach to sustainable supply chain.]]></description>
      <pubDate>Thu, 14 May 2026 17:04:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663938</guid>
    </item>
    <item>
      <title>Innovative Logistics of Spent Coffee Grounds Reuse: An IoT-Based Rolling Horizon Approach</title>
      <link>https://trid.trb.org/View/2682092</link>
      <description><![CDATA[Coffee is one of the most consumed beverages worldwide, driving an industry worth billions of euros annually. However, its life cycle does not end with consumption; it generates a significant amount of by-products, including spent coffee grounds (SCG), often treated as waste. If managed through innovative recovery and reuse practices, these by-products can be recycled and transformed into valuable resources, reducing environmental impact, and optimizing logistics processes. This research explores the recovery of SCG as secondary raw materials in other production chains, leveraging IoT technologies and web-based platforms for real-time monitoring and informed decision-making. The study proposes a heuristic approach that simultaneously plans coffee deliveries and SCG collections (pick-ups) and related routing for a set of coffee shops, while dynamically adapting to evolving requests through a Rolling Horizon technique. Performance results on test cases based on extensive territorial and realistic consumption data are promising, highlighting the potential of this approach for sustainable supply chain management.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682092</guid>
    </item>
    <item>
      <title>Material Stock and Environmental Burdens of Coastal Bridge Infrastructure in China: A Bottom-up Life Cycle Perspective</title>
      <link>https://trid.trb.org/View/2691592</link>
      <description><![CDATA[China's rapid expansion of maritime infrastructure has positioned coastal bridges as critical components of national economic and transport strategies. However, their material stock scale, material intensity and long-term environmental impacts remain underexamined. This study presents the first integrated assessment of material stock (MS) and lifecycle environmental burdens associated with China's coastal bridges, using a bottom-up material flow analysis and ReCiPe 2016 midpoint life cycle assessment (LCA). Results based on 510 coastal bridges show a total in-use material stock of 369.32 million tons, with cable-stayed bridges accounting for 63.9%. Suspension bridges exhibit the highest material intensity (18.78 t/km), primarily due to anchorage systems requiring 12.4 times more crushed stone and 4.7 times more concrete than cable-stayed designs. Life cycle assessment reveals that raw material production contributes 66.6% of total environmental impacts, with steel production alone accounting for 60.9% of global warming potential (GWP) and over 90% of mineral scarcity and human carcinogenicity. Although steel comprises only 13.9% of MS, its unit mass impacts are up to 380 times greater than those of sand and gravel. In addition, hidden flows associated with structural overdesign, anticorrosive coatings, and deferred maintenance shift substantial burdens to upstream phases, contributing 27.8% of GWP and 49.9% of marine ecotoxicity. Mitigation strategies include dynamic LCA frameworks, circular material flows, modular low-carbon construction, bio-based coatings, and alignment with global carbon governance. These findings provide practical guidance for advancing low-carbon, climate-resilient coastal infrastructure in China.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691592</guid>
    </item>
    <item>
      <title>Green logistics: a bibliometric analysis and review</title>
      <link>https://trid.trb.org/View/2651889</link>
      <description><![CDATA[The rapidly evolving domain of logistics has seen an exponential growth in research articles in this discipline. A variety of literature evaluations have been produced, each concentrating on specific aspects of logistics such as network architecture, supplier inventory control, information systems, and others with a wider framework. This paper includes a complete bibliometric analysis as well as a descriptive review of green logistics, sustainability, transportation and reverse logistics. The investigation begins with the identification of 1,296 scientific studies, which are subsequently narrowed down to works in supply chain-related domains. This comprehensive analysis of the domain aids in graphical interpretation of publication history through time as well as identifying areas of current research interest and prospective directions for future study. The findings provide a solid path for future research in this domain.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2651889</guid>
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