Aliza Sharmin

Resilient intermodal freight networks are vital for sustaining supply chains amid increasing threats from natural hazards and cyberattacks. Transportation resilience has been widely studied; understanding how random and targeted disruptions affect structural connectivity and functional performance remains a key challenge. To address this, this study evaluates the robustness of the US intermodal freight network, which consists of rail and water modes, using a simulation-based framework that integrates graph-theoretic metrics with flow-weighted centrality measures. Disruption scenarios are examined, including random failures as well as targeted node and edge removals based on static and dynamically updated degree and betweenness centrality. To reflect more realistic conditions, flow-weighted degree centralities (WDC) and partial node degradation are considered. Two resilience indicators are used: (1) the size of the giant connected component to measure structural connectivity; and (2) flow-weighted network efficiency (NE) to assess freight mobility under disruption. The results show that progressively degrading nodes ranked by WDC to 60% of their original functionality causes a sharper decline in normalized NE, for up to approximately 45 affected nodes, than complete failure (100% loss of functionality) applied to nodes targeted by weighted betweenness centrality or selected at random. This highlights how partial degradation of high-tonnage hubs can produce disproportionately large functional losses. The findings emphasize the need for resilience strategies that go beyond network topology to incorporate freight flow dynamics.

Intermodal transportation (IMT) has long been recognized as a key strategy for decarbonizing freight transportation (FT), which is one of the most polluting sectors worldwide. While IMT has been extensively examined using operations research (OR) methods, the integration of decarbonization objectives has only recently gained momentum. Despite this growing interest, to the best of our knowledge, no prior comprehensive review has systematically synthesized OR methodologies specifically addressing IMT decarbonization. To address this gap, we conduct a systematic literature review of OR studies on IMT decarbonization and organize the survey into two complementary parts. Part I focuses on methodological foundations of OR applications in IMT decarbonization. We classify studies by problem type and OR technique, analyzing modeling characteristics, solution approaches, and uncertainty treatment. Our analysis reveals that exact methods dominate the literature (41% of studies), while meta-heuristics show rapid recent growth with 50% of studies published recently. Approximately 20% of studies incorporate uncertainty, and they are predominantly demand-focused. We identify critical research gaps including limited multistage stochastic frameworks to capture cascading uncertainties, insufficient attention to terminal operations and network reliability, and the underutilization of emerging technologies such as reinforcement learning and digital twins. This systematic synthesis establishes the current state of OR methodologies in IMT decarbonization and provides a foundation for future innovations in sustainable freight systems.

Building upon the methodological synthesis presented in Part I, this second part of our two-part survey examines how operations research (OR) models have been applied to capture the broader dynamics of intermodal transport (IMT) in pursuit of decarbonization. The analysis integrates chronological, modal, and sustainability-oriented perspectives to reveal how IMT strategies evolve across transportation modes, policy environments, and temporal contexts. We identify how efficiency gains, modal shifts, and low-carbon technologies interact within OR frameworks, and assess their implications for emissions reduction, energy use, and network resilience. By bridging technical modeling approaches with system-level sustainability objectives, this study offers a holistic understanding of the pathways through which OR supports the transition toward low-carbon freight systems and highlights research gaps for future interdisciplinary work.

This paper reviews intermodal transportation systems and their role in decarbonizing freight networks from an operations research perspective, analyzing over a decade of studies (2010-2024). We present a chronological analysis of the literature, illustrating how the field evolved over time while highlighting the emergence of new research avenues. We observe a significant increase in research addressing decarbonization since 2018, driven by regulatory pressures and technological advancements. Our integrated analysis is organized around three themes: a) modality, b) sustainability, and c) solution techniques. Key recommendations include the development of multistage stochastic models to better manage uncertainties and disruptions within intermodal transportation systems. Further research could leverage innovative technologies like machine learning and blockchain to improve decision-making and resource use through stakeholder collaboration. Life cycle assessment models are also suggested to better understand emissions across transportation stages and support the transition to alternative energy sources.

According to the Immunization Status Survey conducted by the Tennessee Department of Health in 2023, Tennessee ranks in the bottom 25th percentile among states for vaccination rates by the age of 24 months, based on the full series of recommended vaccines. To tackle this issue, the SmartSHOTS mobile application was developed to reduce barriers to vaccinations for children aged 0-24 months. The app includes features such as vaccine information, the ability to add and calculate vaccine due dates, and the ability to locate health departments and transportation services based on zip codes. The app was developed and usability tested using an iterative design process, based on a needs assessment conducted across regions of Tennessee with community members who served on the state’s county health council. These community members also reviewed the app wireframes. Parents or guardians of children aged 0-24 months are currently evaluating the app (preliminary findings reported here). SmartSHOTS will then be available on Google Play and the Apple Store.

The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an applied workflow that encompasses natural language processing, Methontology-based prompt tuning, and Generative Pre-trained Transformer (GPT), to automate the construction of scenario-based ontologies using existing research articles and technical manuals of urban datasets and simulations. From these ontologies, knowledge graphs can be derived using widely adopted formats and protocols, guiding various tasks towards data-informed decision support. The performance of our methodology is evaluated through a comparative analysis that contrasts our AI-generated ontology with the widely recognized pizza ontology, commonly used in tutorials for popular ontology software. We conclude with a real-world case study on optimizing the complex system of multi-modal freight transportation. Our approach advances urban decision support systems by enhancing data and metadata modeling, improving data integration and simulation coupling, and guiding the development of decision support strategies and essential software components.

The use of drones for package delivery, commonly known as drone delivery or unmanned aerial vehicle (UAV) delivery, has gained significant attention from academia and industries. Compared to traditional delivery methods, it provides greater flexibility, improved accessibility, increased speed and efficiency, enhanced safety, and even some environmental benefits. With the increasing interest in this technology, it is crucial for researchers and practitioners to understand the current state of the art in drone delivery. This paper aims to review the current literature on drone delivery and identify research trends, challenges, and future research directions. Specifically, the relevant literature is identified and selected using a systematic literature review approach. We then categorize the literature according to the characteristics and objectives of the problems and thoroughly analyze them based on mathematical formulations and solution techniques. We summarize key challenges and limitations associated with drone delivery from technological, safety, societal, and environmental aspects. Finally, potential research directions are identified.