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Logistics network is one of the most important parts of supply chains with significant share in achieving sustainability across them. In this paper, we investigate a new multi-objective mixed integer linear programming model for the design of multimodal logistics network. A bi-objective mathematical model is introduced and two conflicting objectives including the minimization of total cost and the total environmental impact are taken into account. Effective environmental life cycle assessment–based method is incorporated in the model to estimate the relevant environmental impacts. Due to budget constraints, financing decisions for facility construction are considered in the proposed model. To cope with the model objective functions, the augmented ε -constraint method is applied. Computational analysis is also provided by using a cement multimodal rail-road logistics network case study to present the significance of the proposed model. Results show that utilizing the proposed multi-period optimization model influences the location of multimodal terminals and their construction time. Also, the results show that the use of the proposed model enhances the efficiency of terminals. On the other hand, computational results indicate that preferences of decision-makers and the importance of environmental objective have significant impacts on the topology of transportation network.

In today's ever-growing logistics landscape, intelligent robots play a key role in improving efficiency, reducing costs and enhancing safety. Traditional path planning methods are difficult to adapt to changing environments, which may lead to problems such as collisions and conflicts. This research aims to solve the problem of path planning and control of logistics robots in complex environments. The method proposed in this study can integrate information from different perception modalities to achieve more accurate path planning and obstacle avoidance control, and improve the autonomy and reliability of logistics robots. The method proposed in this paper first uses 3D CNN to learn the feature representation of objects in the environment, so as to realize the object recognition function. Then, the spatio-temporal features are modeled by LSTM to predict the behavior and trajectory of dynamic obstacles. This helps the robot to more accurately predict the future position of obstacles in complex environments, thereby avoiding potential collision risks. Finally, the Dijkstra algorithm is used for path planning and control decisions to ensure that the robot chooses the optimal path in different scenarios. In a series of experiments, the method proposed in this paper performs well in both path planning accuracy and obstacle avoidance performance, achieving significant improvements over traditional methods. This intelligent path planning and control scheme will effectively improve the practicability of logistics robots in complex environments, and promote the efficiency and safety of the logistics industry.

This paper examines the feasibility of transitioning road cargo to waterborne transport in the UK, aiming to reduce emissions and alleviate road congestion. Key objectives include (1) developing a modal shift technology to establish freight highways across the UK, (2) designing a small, decarbonized barge vessel concept that complements the logistics framework, and (3) assessing the economic and environmental viability of a multimodal logistics network. Using discrete event simulation (DES), four transportation scenarios were analyzed to evaluate the efficiency and sustainability of integrating coastal and inland waterways into the logistics framework. Results indicate that waterborne transport is more cost-effective and environmentally sustainable than road transport. A sweeping design study was conducted to optimize time, cost, and emissions. This model was applied to a case study, providing insights into optimal pathways for transitioning to waterborne freight by finding the optimized number of TEUs. Consequently, our study identified 96 TEUs as the optimal capacity to initiate barge design, balancing cost, time, and emissions, while 126 TEUs emerged as the best option for scalability. Findings offer critical guidance for supporting the UK’s climate goals and governmental policies by advancing sustainable transportation solutions.

This study explores the evolving trends and research issues in the field of drone-assisted multimodal logistics over the past two decades. By employing various text-mining techniques on related research publications, we identify the most frequently investigated topics and research issues within this domain. Specifically, we utilize titles, abstracts, and keywords from the collected studies to perform both Latent Dirichlet Allocation techniques and Term Frequency-Inverse Document Frequency analysis, which help in identifying latent topics and the core research themes within the field. Our analysis focuses on three primary categories of drone-assisted logistics: drone–truck, drone–ship, and drone–robot systems. The study aims to uncover which latent topics have been predominantly emphasized in each category and to highlight the distinct differences in research focuses among them. Our findings reveal specific trends and gaps in the existing literature, providing a clear roadmap for future research directions in drone-assisted multimodal logistics. This targeted analysis not only enhances our understanding of the current state of the field but also identifies critical areas that require further investigation to advance the application of drones in logistics.

The efficient and timely delivery of pharmaceuticals is critical, particularly in regions with dispersed populations and challenging logistics. Inclement weather often disrupts ground transport, complicating the consistent supply of essential medications. Advanced air mobility (AAM), particularly through the use of drones, presents a promising solution to these logistical challenges by enabling smaller, more frequent deliveries to low density populated places and bypassing traditional transport constraints. This study evaluates the potential benefits of AAM for pharmaceutical transport in North Dakota (ND). The authors developed a comprehensive GIS and optimization framework to identify optimal locations for logistical centers and routes for drone and truck transport. The study introduces a person-years-saved (PYS) metric to rank the potential for AAM deployments to foster healthcare improvements in underserved communities. Moreover, the study found that drone trips were significantly more cost-effective and efficient than truck trips, with trucks being 2.3 times more expensive and having a 2.8 times higher underutilization rate. The study concludes with recommendations for regulatory support and future research to validate and expand the application of AAM in pharmaceutical logistics, contributing to improved healthcare delivery and operational efficiency in often overlooked rural populations. These insights provide a foundation for the practical implementation of AAM technologies, emphasizing their potential to revolutionize pharmaceutical logistics in challenging environments.

Optimizing last-mile delivery operations is an essential component in making a modern city livable, particularly in the face of rapid urbanization, increasing e-commerce activity, and the growing demand for fast deliveries. These factors contribute significantly to traffic congestion and pollution, especially in densely populated urban centers like Casablanca. This paper presents an innovative approach to optimizing last-mile delivery by integrating public transportation into the logistics network to address these challenges. A custom-built environment is developed, utilizing public transportation nodes as transshipment nodes for standardized packets of goods, combined with a realistic simulation of traffic conditions through the integration of the travel time index (TTI) for Casablanca. The pickup and delivery operations are optimized with the proximal policy optimization algorithm within this environment, and experiments are conducted to assess the effectiveness of public transportation integration and three different exploration strategies. The experiments show that scenarios integrating public transportation yield significantly higher mean rewards—up to 1.49 million—and more stable policy convergence, compared to negative outcomes when public transportation is absent. The highest-performing configuration, combining PPO with segmented training and public transport integration, achieves the best value loss (0.0129) and learning stability, albeit with a trade-off in task completion. This research introduces a novel, scalable reinforcement learning framework to optimize pickup and delivery with time windows by exploiting both public transportation and traditional delivery vehicles.

Starting from the 3rd millennium BC, Indian maritime trade has augmented the life of a common man and businesses alike. This study, finds that India can leverage on the 7,500 long coast line and derive holistic development in terms of interconnected ports with hinterland connectivity and realize lower expenditure coupled with reduced carbon emission. This research analyzed a decade of cargo data from origination to destination and found that around 82.95 per cent (953 MMTPA in 2017–18) of road based consignments in India comprised of Fertilizers, Hydrocarbons, Coal, Lubricants and Oil. Essentially, a quantum of this i.e. 78.39 per cent of MMTPA cargo consignments (State Owned Hydrocarbons) traverses on Indian roads. The study drew parameters of this transportation paradigm and modeled the same using Artificial Intelligence to depict a monumental opportunity to rationalize costs, improve efficiency and reduce carbon emission to strengthen the argument for the employment of Multimodal Logistics in the Maritime Sector. Subsequent to model derivation the same set of parameters are plotted as an efficient transit map of Interstate transit lines connecting 16 major hubs which now handle bulk cargo shipped by all modes of transport. For the pollution segment a collaborative game theoretic approach i.e., Shapley value is proposed for improved decision making. This study presents data driven and compelling research evidence to portray the benefits of collaboration between firms in terms of time and cost. The study also proposes the need and method to improve hinterland connectivity using a scalable greedy algorithm which is tested with real time data of Coal and Bulk Cargo. As a scientific value addition, this study presents a mathematical model that can be implemented across geographies seamlessly using Information Communication Technology.

Information and Communication Technologies are increasingly enhancing dayto- day operations and long-time programmes within the whole transportation sector. Furthermore, they allow for different modes of transport to be integrated in a more efficient way; not just among themselves, but at the same time with surrounding environments like cities, urban areas or more extended regions involved in transportation supply chains. In February 2011 Genoa Port Authority, as a leading partner, began activities to create the ICT platform named MoS 24 (Motorways of the Sea 24). The main goal of the project is to create a unique information system connecting autonomous programmes, developed by different modes' transport operators, already operating along the multimodal Corridor 24 between Genoa (Italy) and Rotterdam (The Netherlands). The city of Genoa, one of the main ports in the Mediterranean area, is directly involved with the project's development as a gateway for goods from the global market heading to the North European area and vice versa. The core aim of this paper is to analyze how the city may improve its connectivity with the European transport network and obtain advantages from the smart use of trans-European ICT platforms. Roles and ideas of the eleven players (both public and private) taking part in the operations will be considered as well. A deeper analysis will be shown through data and opinions collected during face-to-face interviews with partners' delegates.

The article presents the problem of supply chain optimization from the perspective of a multimodal logistics provider and includes a mathematical model of multilevel cost optimization in the form of MILP (Mixed Integer Linear Programming). The costs of production, transport, distribution and environmental protection were adopted as optimization criteria. Timing, volume, capacity and mode of transport were also taken into account. The model was implemented in the LINGO ver.12. The numerical experiments were carried out using sample data to show the possibilities of practical decision support and supply chain optimization.