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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxODA3IiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSIyeWVhcnMiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycyAvPjxyYW5nZXMgLz48c29ydHM+PHNvcnQgZmllbGQ9InB1Ymxpc2hlZCIgb3JkZXI9ImRlc2MiIC8+PC9zb3J0cz48cGVyc2lzdHM+PHBlcnNpc3QgbmFtZT0icmFuZ2V0eXBlIiB2YWx1ZT0icHVibGlzaGVkZGF0ZSIgLz48L3BlcnNpc3RzPjwvc2VhcmNoPg==" rel="self" type="application/rss+xml" />
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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>
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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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      <title>Strategic Allocation of Research Funding: A Network Analysis Approach with a Focus on Batteries for Road Transport - A Case Study</title>
      <link>https://trid.trb.org/View/2581603</link>
      <description><![CDATA[This study builds upon a previous work extending the analysis of the properties of the networks constructed by Horizon 2020 funded projects and in the UK for one specific research area, batteries for electric vehicles, in order to gain further insight into what impact funding can have in achieving the goals of the EU. Social Network Analysis is used to determine the network properties. The results show the impact of funding compared to the previous analysis on creating structure in the area of batteries that was associated with a thinly spread network of primarily research organizations. An investigation of the direct impact of funding found that similar structuring of the network could be achieved with similarly significant investments exemplary in the UK. Moreover, in both national and EU example cases a few partners stand out and may be playing an important role in connecting partners and projects. In an additional step it was attempted to show that it is possible to create a structure of theoretical structure (based on the existing collection of projects) via extending one variable; this gives valuable insight into where future funds or individual project structures could have the strongest impact on achieving an idealized reference structure.]]></description>
      <pubDate>Thu, 18 Jun 2026 08:54:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581603</guid>
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    <item>
      <title>Towards a new last-mile delivery system: Cost and energy-optimized robot and van allocation</title>
      <link>https://trid.trb.org/View/2598886</link>
      <description><![CDATA[In recent years, autonomous delivery robots have gained tremendous momentum in last-mile logistics operations due to their potential to reduce costs, emissions, and congestion while providing access to narrow roads. However, their operation is restricted by distance, capacity, and maintenance needs. These limitations necessitate a combination of robot and van operations, wherein vans deliver non-bulky parcels to robots while performing bulky and distant deliveries themselves. Building on insights from the Helsingbotica project in Sweden, which includes key stakeholders such as VTI, Hugo, Apotea, BEST Transport, and the City of Helsingborg, this paper aims to evaluate the potential of a joint robot-van setup by analyzing the cost and energy savings of this approach at different geographic scales and configurations. We propose a universally applicable parcel demand estimation framework that uses OpenStreetMap building density and road network data to simulate parcel demand and create meaningful service zones using K-Medoids clustering. The estimated demand and constructed zones serve as input for an integer programming model that assigns parcels to robots and vans in a cost- and energy-saving manner, considering restrictions such as distance and parcel weight. The model’s parameters are calibrated based on structured workshops with industry partners. Our results demonstrate that integrating autonomous delivery robots can reduce operational costs by up to 57% and energy consumption by up to 42%, depending on the configuration. Thus, this study concludes that integrating robots into last-mile delivery can enhance the flexibility and efficiency of logistics service providers, offering a sustainable solution for urban deliveries.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598886</guid>
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    <item>
      <title>Multi-period binding freight contract using swing options</title>
      <link>https://trid.trb.org/View/2605022</link>
      <description><![CDATA[The truckload freight market is a critical component of the US transportation industry, yet freight contracts used in this market are considered non-binding. Most shippers are challenged with freight rejection problems and forced to source from the spot market. The swing option is an exotic option that allows the holder to purchase or sell a defined quantity of underlying assets. This study proposes the use of swing options as multi-period binding freight contracts between shippers and carriers and builds a fair valuation framework using a mean-reverting process for the freight rates, the Hull-White tree model, and dynamic programming. Numerical examples are given to explain the multi-period price behaviours of the swing option freight contract. Sensitivity analyses are conducted on several key model parameters to understand the impacts of model parameters on the price of the swing option freight contract. Moreover, the performance of six tendering strategies is compared under different market conditions, which helps shippers make informed decisions on purchasing or entering into swing option contracts. Our results show that the shipper in an existing freight contract with a high freight rate benefits the most by tendering to the spot market and purchasing the short-term call swing option freight contract at a low strike price.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2605022</guid>
    </item>
    <item>
      <title>Combined China-Europe Railway Express and maritime transport with subsidy and emission tax considerations</title>
      <link>https://trid.trb.org/View/2605021</link>
      <description><![CDATA[This paper investigates the effects of the combined China-Europe Railway Express (CERE) and maritime transport on the China-Europe cargo transport market with subsidy and emission tax considerations. The interrelationships among shippers, railway carrier, domestic liner carrier, foreign liner carrier and the social planner are described by a vertical structure model. Four types of carrier operating regimes, namely full competition, full cooperation, and the cooperation between railway carrier and domestic or foreign liner carrier, are explored and compared. The aim of the social planner is to achieve the welfare maximization of the system by optimizing the subsidies for the railway carrier and the emission taxes on the domestic and foreign liner carriers for creating a mankind community from a perspective of the shared future. The emission amounts before and after implementing the policies of the subsidies and the emission taxes are compared. The results show that: (i) the cooperation of the railway carrier and the domestic (or foreign) liner carrier reduces (or raises) the emission tax on the domestic (or foreign) liner carrier, but raises (or reduces) the subsidy for the railway carrier; (ii) the social planner tends to impose the emission tax on the domestic liner carrier for a low congestion level at the transfer port and a high substitution degree between combined CERE-maritime transport and direct waterway transport; the planner is inclined to subsidize the foreign liner carrier and the railway carrier for a low congestion level and a low substitution degree to encourage more shippers to use the low-carbon mode of the combined CERE-maritime transport; and (iii) the subsidy and emission tax policies are beneficial to emission reduction for a low congestion level and a high substitution degree. These findings provide a guidance for the Chinese and European governments to promote the development of the combined CERE-maritime transport mode and to control the carbon emissions of the China-Europe cargo transport market through adjusting the subsidy and emission tax policies.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2605021</guid>
    </item>
    <item>
      <title>Centralized and decentralized supply chains: Performance maps for comparing the cost-effectiveness of distribution network configurations</title>
      <link>https://trid.trb.org/View/2605016</link>
      <description><![CDATA[Optimizing Distribution Networks (DNs) is crucial for retailers, impacting service levels and logistics costs. A key DN configuration decision is the stock deployment policy, which entails choosing between centralized, decentralized, and hybrid DNs for each Stock Keeping Unit (SKU). Choosing the stock deployment policy is complex due to many variables influencing the decision (e.g., number of customers served, SKU purchasing costs, customer demand, etc.). Moreover, this decision must be revisited whenever customer demands changes, which can be time-consuming when DN resilience is challenged by geopolitical changes, market trends, and disruptive events. Dimensional Analysis (DA), and particularly the Buckingham Theorem (BT), shows capabilities to support retailers in guiding and streamlining stock deployment decisions. After modeling the stock deployment problem in a mathematical form, BT can identify its influential variables, extract knowledge on how variables mutually interact when affecting the stock deployment performance, and aid informed decision-making on the most cost-effective policy. Accordingly, BT enables creating performance maps which compare the characteristics of different DNs and SKUs, then suggesting similar stock deployment decisions for similar (scaled) DNs and SKUs. Despite the potential utility of these performance maps, no prior study has explored BT’s capabilities for stock deployment decisions. This paper bridges this gap by proposing BT to create supportive maps for multidimensional scaling, similarity analysis, and economic performance prediction across centralized, decentralized, and hybrid DNs. The resultant maps provide retailers with visual decision support tools for associating similar DNs and SKUs with optimal stock deployment policies, ultimately improving DN performance and resilience.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2605016</guid>
    </item>
    <item>
      <title>Train capacity optimization under stochastic demand: A flexible composition strategy with extra-long trains</title>
      <link>https://trid.trb.org/View/2604734</link>
      <description><![CDATA[In railway systems, the transport capacity of trains depends on the number of train compositions, which are then allocated to different OD pairs. Therefore, train composition planning is a critical capacity inventory control technique in railway revenue management. However, passenger demand for high-speed railway (HSR) exhibits a strong spatial-temporal imbalance, with significant differences between peak and off-peak periods, and also entails considerable uncertainty, directly impacting enterprise revenue. To address these challenges, this study accounts for the stochastic nature of passenger demand and proposes a flexible train composition (FLTC) strategy that integrates extra-long trains (XLTs), allowing train lengths to exceed platform lengths. This strategy allows for the flexible adaptation of train compositions to accommodate variations in passenger demand. By deploying XLTs without necessitating alterations to the current infrastructure, it enhances transport capacity during peak hours, thereby boosting operational revenue for the enterprise. Additionally, for XLTs, we propose a new operational mechanism to fully utilize their transport capacity while ensuring passenger boarding and alighting experiences, i.e., docking position control and seat allocation methods. Then, the problem studied can be formulated as a two-stage stochastic programming (SP) model, where the first stage determines the number of train compositions and the docking positions of train composition units (TCUs), and the second stage determines the seat allocation scheme based on demand. To enhance the tractability of the model, we reformulate it as a mixed-integer linear programming (MILP) model using appropriate linearization techniques. To solve the proposed model efficiently, we develop a column generation (CG)-based solution method, thereby enabling the generation of near-optimal solutions. To evaluate the effectiveness of the proposed method, numerical experiments are conducted using both a small-scale instance and a real-world case study based on the Beijing-Shanghai HSR corridor. The computational results demonstrate that the proposed approach can significantly improve transport capacity during peak periods, thereby increasing the overall revenue of the HSR system, while also effectively accommodating the stochastic nature of passenger demand.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604734</guid>
    </item>
    <item>
      <title>Mitigating resource mismatches-oriented optimal cross-regional green hydrogen supply strategy considering cost and risk</title>
      <link>https://trid.trb.org/View/2604732</link>
      <description><![CDATA[Low-cost green hydrogen can be produced via water electrolysis in regions with abundant renewable energy; however, the geographical mismatch between hydrogen supply and demand necessitates efficient production site and transport mode selection to ensure its cost-effectiveness and safety. This study addresses this challenge by developing the cross-regional hydrogen supply strategies optimization model under hydrogen supply costs and risks minimization goals for the government in areas with limited resources. A wind-solar-battery renewable energy system is proposed for green hydrogen production, incorporating tube trailers, liquid hydrogen trucks, and pipelines as transport options, to jointly determine optimal production site locations and transport routes. A hydrogen risk assessment method is proposed, which combines population density and transportation distance variables with a natural language processing-aided analysis of historical hydrogen accident frequencies. A case study conducted in Chongqing, China, demonstrates the model’s effectiveness through various scenario analyses. The study reveals a trade-off between cost and risk across different hydrogen supply strategies and identifies a negative power-law relationship between the distance of production sites and production costs, indicating that cost savings diminish as transport distances increase. Furthermore, sensitivity analysis is carried out to explore the uncertainties in hydrogen demand, the impacts of extreme heat weather, the technology development of tube-trailer storage and transport, and the policy support for risk and investment mitigation.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604732</guid>
    </item>
    <item>
      <title>Pricing policy and queue-length disclosure in on-demand service platforms</title>
      <link>https://trid.trb.org/View/2607001</link>
      <description><![CDATA[Online service platforms, such as ride-hailing and freight exchanges, generate revenue and profits through commissions. To attract users and maximize profit while managing platform costs, these platforms strategically implement different pricing and queue-length disclosure policies. Dynamic pricing based on queue length can increase revenue but may also reduce user loyalty, leading to higher platform costs. The choice of queue-length disclosure policy influences customer balking behavior: revealing queue lengths may deter users from joining if they perceive the wait as too long, while not disclosing the queue length leads customers to decide probabilistically, driven by uncertainty about the wait time. We analyze a two-sided queueing model in which both customers and suppliers arrive randomly, queue separately, and engage in immediate matching at the front of the queue. We examine different pricing and queue-length disclosure policies to maximize the platform’s expected profit. Optimizing the underlying semi-Markov decision process requires solving a non-convex quadratically constrained quadratic program. Through uniformization, we derive and solve the optimality equations, and then compare the resulting optimal prices, profits, and throughput. Our findings indicate that pricing and queue-length disclosure policies are complementary. Specifically, dynamic pricing and visible queue lengths both increase expected profit, while static pricing and invisible queue lengths both increase throughput. These outcomes are driven by changes in the average transaction price under different policies. We identify unique thresholds that determine the preferred pricing and queue-length disclosure policies. The choice of pricing policy depends on the extra cost of implementing dynamic pricing compared to a static price, while the selection of queue-length disclosure policy depends on customer sensitivity to service delay.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2607001</guid>
    </item>
    <item>
      <title>Cross-market risk contagion and portfolio diversification: a novel dynamic connectedness framework for fossil fuel and shipping markets</title>
      <link>https://trid.trb.org/View/2606992</link>
      <description><![CDATA[With geopolitical tensions and global uncertainties on the rise, the transmission of shocks from energy to shipping markets has intensified, triggering a renewed and urgent interest in the energy-shipping interconnection. This paper applies a dynamic connectedness approach based on the DCC-GARCH framework to examine the relationship between oil/coal prices and their corresponding shipping route freight rates. We also construct an innovative portfolio technique focused on minimizing connectedness within the DCC-GARCH model. Furthermore, we quantify the risk contagion between these markets using the Copula-GARCH-CoVaR method. Empirical results suggest that portfolios can benefit from diversification by incorporating both oil/coal and shipping assets. Notably, the minimum connectedness portfolio technique offers substantial risk reduction and return enhancement. We identify an asymmetric risk contagion between the oil/coal and shipping markets, with risks propagating more rapidly and intensively from oil/coal markets to shipping markets. Both upside and downside risks in oil/coal and shipping assets are significantly underestimated. Additionally, deadweight tonnage and shipping route distance influence the degree of risk contagion, with larger tonnages and longer routes amplifying the transmission of oil/coal price risk to shipping freight rate.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606992</guid>
    </item>
    <item>
      <title>Taking a “Free” Ride: Where do we Stand on Free or Low Fare Public Transport and Where do we go Next?</title>
      <link>https://trid.trb.org/View/2706715</link>
      <description><![CDATA[Fare-free and low-fare public transport (FFPT) has shifted from a marginal idea to a visible policy in debates on transport equity, climate mitigation and cost-of-living relief. This paper reviews 147 studies on FFPT and deep fare reductions, identified through a PRISMA-informed search of databases and grey literature. The review spans decades and covers empirical evaluations, theoretical work and policy analyses from Europe, North America, South America, Asia and Oceania. This review paper broadly addresses the main positive impacts of FFPT; key constraining factors; which groups benefit most, and under which conditions; and identifies research gaps. Across contexts, FFPT has been found to increase ridership and improve accessibility, particularly for low-income users, students and older adults, and can strengthen social inclusion and supports framing public transport as a public good. Evidence of substantial, durable modal shift from private cars, or of large emissions reductions, is limited and context dependent. Financial sustainability, capacity pressures and unintended social effects are recurrent challenges. The few studies estimating fare elasticities find that public transport demand is price-inelastic, so pricing is only one lever shaping travel behaviour. Persistent gaps include scarce longitudinal analyses, limited environmental measurement, regional bias towards Europe and North America, weak intersectional equity perspectives and incomplete understanding of how fare reform interacts with transport, land-use and welfare systems. Overall, FFPT improves accessibility and social equity, but its environmental and behavioural impacts are modest unless combined with broader strategies of service enhancement, demand management and integrated urban planning in diverse urban contexts.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706715</guid>
    </item>
    <item>
      <title>The carrot or the chase? Distinguishing earned and anticipated discounts in public transportation</title>
      <link>https://trid.trb.org/View/2706379</link>
      <description><![CDATA[We study a loyalty program in public transportation to disentangle the causal effects of two key behavioral drivers of customer expenditure: the “anticipation effect” (the prospect of earning a future reward) and the “consumption effect” (the impact of using an earned reward). To do so, we analyze “ZVV Bonus”, a program by Switzerland’s largest regional tariff association, using panel data from approximately 485,000 individuals. The program’s design, which awarded credits for single-ticket purchases for use in the following month, allows for its interpretation as a natural experiment, which we evaluate using a within-subject difference-in-differences (DiD) approach. Finding that the baseline DiD estimates are likely biased due to violations of the parallel-trends assumption, we propose and apply a novel offline-benchmarked trend-correction method, which leverages aggregated offline sales trends to construct a counterfactual that is robust to common time-varying demand shocks. Our corrected estimates show that while the anticipation effect significantly increased gross expenditure, the consumption effect was negligible and associated with a near one-to-one cannibalization of sales. Consequently, the program resulted in a net revenue loss for the tariff association. Our findings suggest that for high-frequency services, such as public transportation, the anticipation of a reward is a more powerful motivator than the reward itself.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706379</guid>
    </item>
    <item>
      <title>Optimizing dedicated lanes and tolling schemes for connected and autonomous vehicles to address bottleneck congestion considering morning commuter departure choices</title>
      <link>https://trid.trb.org/View/2701206</link>
      <description><![CDATA[The introduction of connected and autonomous vehicles (CAVs) provides a significant opportunity to address the persistently increasing problem of urban traffic congestion. By virtue of their connectivity and automation features, CAVs can reduce vehicle headways, thereby increasing road capacity and enhancing throughput. It has been hypothesized that CAV-infrastructure design policies can influence traveler behavior in ways that could reduce congestion. This research focuses on the potential of using CAV-dedicated lanes (CAVL) to alleviate traffic congestion in a bottleneck corridor that serves both human-driven vehicles (HDVs) and CAVs. We delve into investigating the impacts of CAVLs on the departure time and lane choices of morning commuters. The study first expresses traffic equilibrium conditions as a linear program with complementarity constraints. Then, a system-optimal commute congestion management design is formulated to minimize the overall system cost, which consists of queuing delays and early and late arrival costs. The results of the computational experiments suggest that: (i) the CAV technological advancements can significantly reduce traffic congestion under CAVL deployment with an almost similar effect as a tolling policy; and (ii) the lower value of time for CAV commuters leads them to depart closer to their desired arrival time without a tolling policy, which could significantly increase the bottleneck traffic congestion that commuters experience, particularly HDVs.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701206</guid>
    </item>
    <item>
      <title>Logistics park charging station optimization using improved genetic algorithms</title>
      <link>https://trid.trb.org/View/2701203</link>
      <description><![CDATA[The one-time investment and later operation efficiency of the supporting charging station are critical for the length of the investment return cycle. In this article, to meet the charging demand of logistics parks as the precondition, through the minimum total cost (construction, operation, maintenance, and depreciation) as the objective function, the improved genetic algorithm (GA) is used to look for the optimal solution. The operations of the charging station planning of a logistics park in off-season and peak-season are simulated and analyzed. The results show that the model and calculation method proposed in this article can provide effective support for the planning decision of logistics park charging. This study can guide the improvement and management in charging station configuration.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701203</guid>
    </item>
    <item>
      <title>Strategic investment in supply chain resilience: A study of fast-moving consumer goods under demand uncertainty</title>
      <link>https://trid.trb.org/View/2633768</link>
      <description><![CDATA[The growing disruptions require Fast-Moving Consumer Goods (FMCG) supply chains to evolve beyond cost efficiency and incorporate resilience to sustain competitiveness and growth. FMCG supply chains face unique challenges, including perishability, intense competition and high demand uncertainty. Although investing in supply chain resilience(SCR) is crucial in FMCG, few studies provide applicable frameworks to evaluate the value of this strategic investment and address demand uncertainty. This paper addresses this gap by introducing a net present value (NPV)-based resilience indicator integrated with FMCG performance metrics. To estimate resilience cost savings used in this indicator, we develop stochastic optimization, robust optimization, and Wasserstein distributionally robust optimization (WDRO) models for a two-echelon, multi-product FMCG supply chain, and explore risk preferences through expected total cost, conditional value at risk (CVaR), and mean-CVaR. The model is further extended to capture demand uncertainty under disruption. Numerical experiments suggest that resilience investments can yield up to 13 % cost savings under disruptions, rising to 15 % when accounting for demand uncertainty. WDRO emerges as the most effective model for strategy selection. The results demonstrate that both the level and distribution shape of demand uncertainty influence investment, with an optimal uncertainty level that strongly encourages investment. Sensitivity analysis further shows market share loss, lost sales, customer service requirement, perishability and profitability as positive drivers of investment, whereas transportation, production and scrap costs are negatively correlated. This study offers valuable decision-making guidance for FMCG supply chain managers on resilience investments.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633768</guid>
    </item>
    <item>
      <title>Grain drying capacity planning and scheduling under yield uncertainty: Minimizing post-harvest losses and operational costs</title>
      <link>https://trid.trb.org/View/2618278</link>
      <description><![CDATA[This paper investigates the joint optimization of grain drying capacity planning and scheduling under yield uncertainty, to minimize operational costs and reduce post-harvest losses. We propose a two-stage stochastic optimization model that integrates decisions on post-harvest service center (PHSC) selection, initial drying machine deployment, and grain origin allocation in the first stage. In the second stage, after yield realization, the model addresses machine supplementation and detailed drying task scheduling. To handle the complexity arising from scheduling feasibility constraints, we propose an exact solution approach based on logic-based Benders decomposition (LBBD). In this approach, scheduling feasibility is efficiently determined using the preemptive earliest due date (PEDD) rule, and customized Benders cuts are developed. Additionally, acceleration techniques, including multi-cut strategies, valid inequalities, and the minimal infeasible subset (MIS) method, are employed to enhance computational efficiency. We validate the effectiveness and scalability of the proposed solution approach through extensive computational experiments, which solve instances with up to 120 origins and 200 yield scenarios within one hour. Two sensitivity analyses are conducted to assess the impact of second-stage machine costs and different scheduling rules on resource utilization and total cost performance. A real-world case study based on data from Hubei Province in China further demonstrates the practical applicability of our model and method. In this case study, a data-driven approach is used to characterize yield uncertainty by fitting historical yield data to probability distributions to ensure realistic scenario generation.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618278</guid>
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