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Digital transformation of organizations managing freight transportation corridors is today, under the so-called "Nearshoring" trend, a key challenge for the logistics sector operating in regions such as the USMCA (United States-Mexico-Canada Agreement). Since Mexico has a significant role in North American industrial supply chains' regionalization, this paper proposes a graph theory-based logistics approach to model and measure the operational robustness of Mexican freight transportation corridors. The proposed method consisted of four steps: (a) construct a dual network for evaluating road section indexes by transforming road links into nodes; (b) assign weights to the edges based on each route distance; (c) simulating potential disruptions in road sections based on topological indices and variations in distance; and (d) developing a web-based digital tool to manage and evaluate the impact of freight transportation corridor management's impact on supply chains' efficiency, sustainability, and resilience. This paper makes two contributions to the current body of knowledge and practical applications. Firstly, it identifies the limitations of some topological indices when assessing the dual graph. Secondly, it discusses the challenges and opportunities for implementing the digital transformation of freight transportation corridor management in Mexico. In conclusion, this paper presents insights for policymakers and researchers and outlines future research directions. [ABSTRACT FROM AUTHOR]

Understanding the mobility of surface freight transportation is relevant in urban planning and for developing public policies. Literature shows that most previous efforts on this topic rely on surveys and limited data. In contrast to other works, in this paper, we present an innovative methodology for characterizing last-mile freight transportation that uses a novel and copious data source: mobile phone data, which provides a broader scope. Our methodology involves calibrating supervised machine-learning models that allow us to link cell phones with truck drivers. In this endeavor, we construct several input variables that track mobile phone's daily movement patterns, including traveled distances, interactions with highway networks, and land use variables. We test our approach by conducting a case study in Santiago, Chile, for which we analyze mobility patterns and logistics indicators disaggregated by day, hour, and zoning. For this case, we show that all supervised models performed well regarding AUC, which can be attributed to the high granularity and handling of the data. However, we chose to use NGBoost in all subsequent experiments, as it provided slightly better results on our validation data. Our work has several implications for practice. For instance, our results can support decision-makers and policymakers in identifying critical areas where urban logistics centers and transportation interventions are needed. Finally, several research lines stem from our work, which include assessing the impact of incorporating geospatial information and the measurement of logistics sprawl over time. [ABSTRACT FROM AUTHOR]

• Formulated the autonomous truck platooning problem with platoon size limit and size-dependent fuel consumption. • Jointly optimized platoon formation, scheduling, and routing. • Proposed a computationally efficient exact algorithm. • Performed sensitivity analyses on platoon size limit and the diffusion and tightness of AT time windows. Autonomous trucks offer a promising avenue for enhancing the efficiency and reducing the environmental impact of road freight transportation. This paper examines a transitional phase towards a fully unmanned truck fleet, focusing on a platooning approach with a lead driver. We develop a novel optimization model for autonomous truck platooning that simultaneously considers platoon formation, scheduling, and routing to minimize costs related to labor and fuel. The model incorporates the possibility of detours, nonlinear fuel savings due to air-drag reduction, and the practical platoon size limit. We present an enhanced column generation method, termed the platoon-generation-and-updating approach, which demonstrates high effectiveness in reducing computational time and complexity. Our numerical analysis, based on the Hong Kong highway network, demonstrates the substantial cost advantages of autonomous truck platooning. It also investigates how platooning efficiency is influenced by various operating factors, including truck fleet size, platoon size restrictions, labor-to-fuel cost ratio, and the strictness of delivery time windows, with practical implications interwoven into the discussion. [ABSTRACT FROM AUTHOR]