搜索结果：Truckloads

Accurate truck-to-shipment matching using GPS data is foundational for full truckload supply chain visibility, enabling real-time tracking and accurate estimated time of arrival (ETA) predictions. However, missing or corrupted vehicle identifiers prevent traditional matching approaches, leaving shipments without visibility. This paper presents Intelligent Truck Matching (ITM) 2.0, a machine learning system that addresses this critical gap by formulating matching as a probabilistic ranking problem. Our approach leverages Uber H3 hexagonal spatial indexing to discretize GPS pings into route similarity features, combined with temporal information, then applies LightGBM gradient boosting with threshold-based post-processing. Through rigorous evaluation including offline model selection (SVM, XGBoost, LightGBM), comprehensive ablation studies, and production shadow testing, we demonstrate substantial gains over rule-based baselines. ITM 2.0 achieves 26 percentage point precision improvement in North America and 14 points in Europe, while doubling coverage. Deployed in production at Project44 handling full truckload shipments, the system demonstrates robustness to geocoding errors up to

The freight industry is undergoing a digital revolution, with an ever-growing volume of transactions being facilitated by digital marketplaces. A core capability of these marketplaces is the fulfillment of demand for truckload movements (loads) by procuring the services of carriers who execute them. Notably, these services are procured both through long-term contracts, where carriers commit capacity to execute loads (e.g., contracted fleet of drivers or lane-level commitments), and through short-term spot marketplaces, where carriers can agree to move individual loads for the offered price. This naturally couples two canonical problems of the transportation industry: contract assignment and spot pricing. In this work, we model and analyze the problem of coordinating long-term contract supply and short-term spot supply to minimize total procurement costs. We develop a Dual Frank Wolfe algorithm to compute shadow prices which allow the spot pricing policy to account for the committed contract capacity. We show that our algorithm achieves small relative regret against the optimal -- but intractable -- dynamic programming benchmark when the size of the market is large. Importantly, our

We present an integrated framework for truckload procurement in container logistics, bridging strategic and operational aspects that are often treated independently in existing research. Drayage, the short-haul trucking of containers, plays a critical role in intermodal container logistics. Using dynamic programming, we identify optimal operational policies for allocating drayage volumes among capacitated carriers under uncertain container flows and spot rates. The computational complexity of optimization under uncertainty is mitigated through sample average approximation. These optimal policies serve as the basis for evaluating specific capacity arrangements. To optimize capacity reservations with strategic and spot carriers, we employ an efficient quasi-Newton method. Numerical experiments demonstrate significant cost-efficiency improvements, including a 21.2% cost reduction in a four-period scenario. Monte Carlo simulations further highlight the strong generalization capabilities of the proposed joint optimization method across out-of-sample scenarios. These findings underscore the importance of integrating strategic and operational decisions to enhance cost efficiency in truckl

Truckload procurement plays a vital role in integrated container logistics, particularly under the uncertainties of container flow and market conditions. We formulate the operational volume allocation problem in drayage procurement as a multistage stochastic transportation problem and solve it using stochastic dual dynamic programming (SDDP). We employ a multivariate count time series approach from the literature to model cargo flow dynamics, relaxing independence assumptions and capturing complex correlations. Our numerical experiments demonstrate the scalability of SDDP and its effectiveness in approximating high-quality policies across realistic problem instances. Sensitivity analyses highlight the significant impact of inflow uncertainties on costs, while spot market variability has a comparatively minor effect. Additionally, we propose an alternative stopping rule for SDDP iterations, balancing computational efficiency and solution fidelity.

Less-than-truckload (LTL) shipment is vital in modern freight transportation yet is in dire need of more efficient usage of resources, higher service responsiveness and velocity, lower overall shipping cost across all parties, and better quality of life for the drivers. The industry is currently highly fragmented, with numerous small to medium-sized LTL carriers typically operating within dedicated regions or corridors, mostly disconnected from each other. This paper investigates the large-scale interconnection of LTL carriers enabling each to leverage multi-carrier networks for cross-region services exploiting their mutual logistic hubs, in line with Physical Internet principles. In such a network, efficient open cooperation strategies are critical for optimizing multiparty relay shipment consolidation and delivery, transport and logistic operations and orchestration, and enabling inter-hub driver short hauls. To dynamically plan relay truck transportation of involved carriers across hyperconnected hub networks, we develop an optimization-based model to build loads, coordinate shipments, and synchronize driver deliveries. We report a simulation-based experiment in a multiparty LTL

Important pricing problems in centralized matching markets -- such as carpooling, food delivery and freight shipping platforms -- often exhibit a bi-level structure. At the upper level, the platform sets prices for heterogeneous demand types (e.g., rides across origin-destination pairs, food delivery orders across restaurant-customer pairs, or less-than-truckload shipments). The lower level subsequently matches converted demands to minimize operational costs; for example, by pooling riders into shared vehicles or consolidating multiple orders into single courier or trailer routes. Motivated by these applications, we study the optimal value (cost) function of a linear programming model with respect to demand arrival rates, originally proposed by Aouad and Saritac (2022) for cost-minimizing dynamic stochastic matching under limited time. In particular, we study the concavity properties of this cost function. We show that it suffices for every optimal basic feasible solution of the linear program to be nondegenerate in order to guarantee weak concavity. Leveraging this insight, we further establish that weak concavity holds when all demand types have strictly positive unmatched rates

Food waste and food insecurity are two closely related pressing global issues. Food rescue organizations worldwide run programs aimed at addressing the two problems. In this paper, we partner with a non-profit organization in the state of Indiana that leads \emph{Food Drop}, a program that is designed to redirect rejected truckloads of food away from landfills and into food banks. The truckload to food bank matching decisions are currently made by an employee of our partner organization. In addition to this being a very time-consuming task, as perhaps expected from human-based matching decisions, the allocations are often skewed: a small percentage of the possible recipients receives the majority of donations. Our goal in this partnership is to completely automate Food Drop. In doing so, we need a matching algorithm for making real-time decisions that strikes a balance between ensuring fairness for the food banks that receive the food and optimizing efficiency for the truck drivers. In this paper, we describe the theoretical guarantees and experiments that dictated our choice of algorithm in the platform we built and deployed for our partner organization. Our work also makes contri

Less than truckload shipping plays a critical role in modern supply chains by consolidating freight from multiple shippers into shared vehicles. Despite its operational flexibility and potential sustainability benefits, the LTL sector faces persistent challenges, including high per unit costs and financial instability, as evidenced by recent industry bankruptcies. This paper investigates two structural issues limiting LTL performance, including the constrained consolidation potential imposed by proprietary logistics networks, and the inefficiency of fixed pricing models that fail to reflect realtime network conditions. To address these, we explore a Physical Internet enabled, hyperconnected LTL logistics system based on open asset sharing and dynamic flow consolidation. We then propose a dynamic pricing framework tailored for this network. Through a simulation based study grounded in Freight Analysis Framework data and cost estimates from industry sources, we evaluate system performance across three demand and cost uncertainty scenarios in the Southeastern U.S. The results validate our system effectiveness and suggest a promising path forward for building more efficient LTL logisti

The Steiner Multicycle problem consists of, given a complete graph, a weight function on its vertices, and a collection of pairwise disjoint non-unitary sets called terminal sets, finding a minimum weight collection of vertex-disjoint cycles in the graph such that, for every terminal set, all of its vertices are in a same cycle of the collection. This problem generalizes the Traveling Salesman problem and therefore is hard to approximate in general. On the practical side, it models a collaborative less-than-truckload problem with pickup and delivery locations. Using an algorithm for the Survivable Network Design problem and T -joins, we obtain a 3-approximation for the metric case, improving on the previous best 4-approximation. Furthermore, we present an (11/9)-approximation for the particular case of the Steiner Multicycle in which each edge weight is 1 or 2. This algorithm can be adapted to obtain a (7/6)-approximation when every terminal set contains at least 4 vertices. Finally, we devise an O(lg n)-approximation algorithm for the asymmetric version of the problem.

With multiple carriers in a logistics market, customers can choose the best carrier to deliver their products and packages. In this paper, we present a novel approach of using the stochastic evolutionary game to analyze the decision-making of the customers using the less-than-truckload (LTL) delivery service. We propose inter-related optimization and game models that allow us to analyze the vehicle routing optimization for the LTL carriers and carrier selection for the customers, respectively. The stochastic evolutionary game model incorporates a small perturbation of customers' decision-making which exists due to irrationality. The solution of the stochastic evolutionary game in terms of stochastically stable states is characterized by using the Markov chain model. The numerical results show the impact of carriers' and customers' parameters on the stable states.

In the for-hire truckload market, firms often experience unexpected transportation cost increases due to contracted transportation service provider (carrier) load rejections. The dominant procurement strategy results in long-term, fixed-price contracts that become obsolete as transportation providers' networks change and freight markets fluctuate between times of over and under supply. We build behavioral models of the contracted carrier's load acceptance decision under two distinct freight market conditions based on empirical load transaction data. With the results, we quantify carriers' likelihood of sticking to the contract as their best known alternative priced load options increase and become more attractive; in other words, carriers' contract price stickiness. Finally, we explore carriers' contract price stickiness for different lane, freight, and carrier segments and offer insights for shippers to identify where they can expect to see substantial improvement in contracted carrier load acceptance as they consider alternative, market-based pricing strategies.

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