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.

Wildfire incidents are catastrophic events with a high level of uncertainty. Therefore, resource allocation towards its suppression is a challenging task. Our research presents a method for wildfire management that combines digital twins with a real-time deep reinforcement learning (DRL) technique. The detection of wildfires is supported by the use of real-time satellite imagery. The developed digital twin operates under two stages. Initially, a discrete simulation approach is employed to model the behavior of fires, their immediate impact on the population, and the response times of firefighting resources. Then, an Advantage Actor-Critic (A2C) DRL policy optimizes the response under different scenarios accounting for a time-based environment to adjust agent actions. The holistic approach presented in this study strives to synergize and minimize the effect of wildfires over population to create an adaptive and effective wildfire response system.

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.

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.

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.