Search Results (31)

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. Full article

To enhance the operational efficiency of container multimodal transportation and mitigate carbon emissions during freight transit, this study investigates carbon emission-conscious multimodal transportation route optimization models and solution methodologies. Addressing the path optimization challenges under uncertain conditions, triangular fuzzy numbers are employed to characterize transportation time uncertainty, while a scenario-based robust regret model is formulated to address freight price volatility. Concurrently, the temporal value attributes of cargo are incorporated by transforming transportation duration into temporal costs within the model framework. Through the implementation of four distinct low-carbon policies, carbon emissions are either converted into cost metrics or established as constraint parameters, thereby constructing an optimization model with total cost minimization as the objective function. For model resolution, fuzzy chance-constrained programming is adopted for defuzzification processing. Subsequently, a multi-strategy improved whale optimization algorithm (WOA) is developed to solve the formulated model. Numerical case studies are conducted to validate the proposed methodology through comparative analysis with conventional WOA implementations, demonstrating the algorithm’s enhanced computational efficiency. The experimental results confirm the model’s capability to adapt multimodal transportation schedules for cargo with varying temporal value attributes and effectively reduce CO₂ emissions under different carbon reduction policies. This research establishes a comprehensive decision-making framework that provides logistics enterprises with a valuable reference for optimizing low-carbon multimodal transportation operations. Full article

Multimodal transport refers to the integrated transportation in a logistics system in the form of multiple transportation modes, such as highway, railway, waterway, etc. In recent years, the deep integration of electric trucks and route optimization has significantly improved the cost-effectiveness and operational efficiency of multimodal transportation. It has provided strong support for the sustainable development of the logistics system. Based on whether to consider low-carbon requirements, uncertainty, and special cargo transportation, the literature is divided into five areas: traditional multimodal transport path optimization, multimodal transport path optimization considering low-carbon requirements, multimodal transport path optimization considering uncertainty, multimodal transport path optimization considering low-carbon requirements and uncertainty, and multimodal transport path optimization considering special transport needs. In this paper, we searched the literature on multimodal path optimization after 2016 in WOS (Web of Science) and CNKI (China National Knowledge Infrastructure), and found that the number of publications in 2024 is three times that in 2016. We collected 130 relevant studies to summarize the current state of research. Finally, with the development of multimodal transport to collaborative transport and the improvement of the application of in-depth learning in different fields, the research mainly focuses on two future research directions: collaborative transport and the use of in-depth learning to solve uncertain problems, and combining it with the problem of multimodal transport route optimization to explore more efficient and perfect transport solutions. Full article

Technological innovation toward electrification and digitalization is revolutionizing aviation, paving the way for new aeronautical paradigms and novel modes to transport goods and people in urban and regional environments. Advanced Air Mobility (AAM) leverages vertical and digital mobility, driven by safe, quiet, sustainable, and cost-effective electric vertical takeoff and landing (VTOL) aircraft. A key enabler of this transformation is the development of vertiports—dedicated infrastructure designed for VTOL operations. Vertiports are pivotal in integrating AAM into multimodal transport networks, ensuring seamless connectivity with existing urban and regional transportation systems. Their design, placement, and operational framework are central to the success of AAM, influencing urban accessibility, safety, and public acceptance. These facilities should accommodate passenger and cargo operations, incorporating charging stations, takeoff and landing areas, and optimized traffic management systems. Public and private sectors are investing in vertiports, shaping the regulatory and technological landscape for widespread adoption. As cities prepare for the future of aerial mobility, vertiports will be the cornerstone of sustainable, efficient, and scalable air transportation. Full article

Effective cooperation between railways and seaports is crucial for enhancing the efficiency of seaport-hinterland corridors (SHC) . However, existing challenges stem from fragmented decision-making across seaports, rail operators, and inland cities, leading to asynchronous routing and scheduling, suboptimal service coverage, and delays. Addressing these issues requires a comprehensive approach to scheduled train service design from a network-based perspective. To tackle the challenges in SHCs, we propose a targeted networked solution that integrates multimodal coordination and resource optimization. The proposed framework is built upon a time-space-state network model, incorporating service selection, timing, and frequency decisions. Furthermore, an improved adaptive large neighborhood search (ALNS) algorithm is developed to enhance computational efficiency and solution quality. The proposed solution is applied to a representative land–sea transport corridor to assess its effectiveness. Compared to traditional operational strategies, our optimized approach yields a 7.6% reduction in transportation costs and a 56.6% decrease in average cargo collection time, highlighting the advantages of networked service coordination. The findings underscore the potential of network-based operational strategies in reducing costs and enhancing efficiency, particularly under unbalanced demand distributions. Additionally, effective demand management policies and targeted infrastructure capacity enhancements at bottleneck points may play a crucial role in practical implementations. Full article

Aiming at the uncertainty in cargo demand in the transportation process, the multimodal transportation path optimization problem is studied from the perspective of a low-carbon economy, and the robust optimization modeling method is introduced. Firstly, a robust optimization model for multimodal transportation is built using the multimodal transportation path optimization model under demand certainty, and the total transportation cost is then calculated by taking into account not just only the cost of transportation and trans-shipment but, additionally, the price of waiting because of schedule restrictions on trains and airplanes. Secondly, carbon emissions are added into the model as a constraint or cost by converting four different low-carbon policies. Then, the simulated annealing mechanism is introduced to improve the ACO algorithm. Finally, solomon calculus is used for the solution. The outcomes demonstrate that the improved annealing ant colony hybrid algorithm simulation can essentially improve the multimodal transportation path optimization problem with uncertain demand and promote multimodal transportation emission reduction. Among the four carbon emission policies, the mandatory carbon emission policy means are tough, and the greatest impact comes from reducing emissions and using less energy. Energy conservation and emission reduction have the second-best impact, while the three policy tools of carbon taxes, carbon trading and carbon payment are more modest. Full article

The cold chain logistics industry faces significant challenges in terms of transportation costs and carbon emissions. It is imperative to plan multimodal transportation routes efficiently to address these issues, minimize food waste, and reduce carbon emissions. This paper focuses on four key optimization objectives for multimodal cold chain transport: minimizing total transportation time, costs, carbon emissions, and food waste. To tackle these objectives, we propose a high-dimensional multiobjective route optimization model for multimodal cold chain networks. Our approach involves the development of a multiobjective evolutionary algorithm, utilizing Monte Carlo simulation and a one-by-one selection strategy. We evaluate the proposed algorithm’s performance by analyzing various convergence and distribution indicators. The average values for the minimum total transportation time, transportation cost, carbon emission cost, and cargo loss rate derived from the proposed algorithm ultimately converge to 6721.7, 5184.4, 301.5, and 0.21, respectively, demonstrating the effectiveness of the algorithmic solution. Additionally, we benchmark our algorithm against the existing literature to showcase its efficiency in solving high-dimensional multi-objective route optimization problems. Furthermore, we investigate the impact of different parameters, such as carbon tax rates, temperature, and cargo activation energy, on carbon emissions, and food waste. Moreover, we conduct a real-world case study to apply our approach to solving a practical business problem related to multimodal cold chain transportation. The insights gained from this research offer valuable decision-making support for multimodal carriers in developing low-carbon and environmentally friendly transportation strategies to efficiently transport perishable goods. Full article

Integrating advanced air mobility (AAM) into the logistics of high-value electronic commodities can enhance efficiency and promote sustainability. The objective of this study is to optimize the logistics network for high-value electronics by integrating AAM solutions, specifically using heavy-lift cargo drones for middle-mile transport and using the mostly rural and small urban U.S. state of North Dakota as a case study. The analysis utilized geographic information system (GIS) and spatial optimization models to strategically assign underutilized airports as multimodal freight hubs to facilitate the shift from long-haul trucks to middle-mile air transport. Key findings demonstrate that electronics, because of their high value-to-weight ratio, are ideally suited for air transport. Comparative analysis shows that transport by drones can reduce the average cost per ton by up to 60% compared to traditional trucking. Optimization results indicate that a small number of strategically placed logistical hubs can reduce average travel distances by more than 13% for last-mile deliveries. Cost analyses demonstrate the viability of drones for middle-mile transport, especially on lower-volume rural routes, highlighting their efficiency and flexibility. The study emphasizes the importance of utilizing existing infrastructure to optimize the logistics network. By replacing truck traffic with drones, AAM can mitigate road congestion, reduce emissions, and extend infrastructure lifespan. These insights have critical implications for supply chain managers, shippers, urban planners, and policymakers, providing a decision support system and a roadmap for integrating AAM into logistics strategies. Full article

The current literature mainly uses hub capacity or transport route selection to manage the congestion of emergency multimodal transport and pays less attention to transshipment scheduling. This paper proposes an integrated optimization problem of transport routes and transshipment sequences (ITRTSP) and constructs a hybrid flow shop scheduling model to describe it. Based on this model, a recursive method is proposed to calculate the minimum waiting times that cargoes consume in queues at hubs, given the transport routes and transshipment sequences. Furthermore, a memetic algorithm is designed with route selection as the outer layer and transshipment sequence selection as the inner layer for solving ITRTSP. Compared with existing achievements, the model and algorithms can quantify the dependency between transshipment sequence selection and emergency transport time in multimodal transport network settings. The model and algorithms are applied to solve some real-scale examples and compared with the first-come-first-served (FCFS) rule commonly used in the current literature. The results indicate that the makespan is reduced by up to approximately 4.2%, saving 33.68 h. These findings demonstrate that even with given hub capacities and transport routes, congestion can still be managed and the schedule optimized through transshipment scheduling, further improving emergency transport efficiency. Full article

Our research area concerns the development of an intelligent e-service system to help manage multimodal transportation processes. To better respond to the requirements of sustainable development, we encourage the development of multimodal cargo transportation. Therefore, it is important to ensure that the dissemination and management of information in multimodal transportation requires more accurate information transmission and implementation for better coordination of these processes with the interaction of all process participants. Also, contextual data integration into the e-service provision processes is important for more adequate real cargo transportation management. The transition to multimodal freight transport and the increase in its activity directly impact the sustainable development of this sector as transport flows are removed from ground roads and distributed more evenly to load more railways and sea vessels. This research aims to develop an approach to developing the infrastructure of an e-service system with the ability to integrate contextual data and influence the management of multimodal transportation. The methodological approach is based on methods of conceptual representation of information and methods for recognizing the flow of needful information during multimodal freight transportation according to adaptable management processes. The e-service provision system creates benefits for cargo drivers and delivery managers with more accurate information implementation and more adequate coordination of processes under real conditions by helping them make the right decisions. Full article

With the increasing global concern over climate change, reducing greenhouse gas emissions has become a universal goal for governments and enterprises. For oversize and heavyweight cargo (OHC) transportation, multimodal transportation has become widely adopted. However, this mode inevitably generates carbon emissions, making research into effective emission reduction strategies essential for achieving low-carbon economic development. This study investigates the optimization of multimodal transportation paths for OHC (OMTP-OHC), considering various direct carbon pricing policies and develops models for these paths under the ordinary scenario—defined as scenarios without any carbon pricing policies—and two carbon pricing policy scenarios, namely the emission trading scheme (ETS) policy and the carbon tax policy, to identify the most cost-effective solutions. An enhanced genetic algorithm incorporating elite strategy and catastrophe theory is employed to solve the models under the three scenarios. Subsequently, we examine the impact of ETS policy price fluctuations, carbon quota factors, and different carbon tax levels on decision-making through a case study, confirming the feasibility of the proposed model and algorithm. The findings indicate that the proposed algorithm effectively addresses this problem. Moreover, the algorithm demonstrates a small impact of ETS policy price fluctuations on outcomes and a slightly low sensitivity to carbon quota factors. This may be attributed to the relatively low ETS policy prices and the characteristics of OHC, where transportation and modification costs are significantly higher than carbon emission costs. Additionally, a comparative analysis of the two carbon pricing policies demonstrates the varying intensities of emission reductions in multimodal transportation, with the ranking of carbon emission reduction intensity as follows: upper-intermediate level of carbon tax > intermediate level of carbon tax > lower-intermediate level of carbon tax = ETS policy > the ordinary scenario. The emission reduction at the lower-intermediate carbon tax level (USD 8.40/t) matches that of the ETS policy at 30%, with a 49.59% greater reduction at the intermediate level (USD 50.48/t) compared to the ordinary scenario, and a 70.07% reduction at the upper-intermediate level (USD 91.14/t). The model and algorithm proposed in this study can provide scientific and technical support to realize the low-carbonization of the multimodal transportation for OHC. The findings of this study also provide scientific evidence for understanding the situation of multimodal transportation for OHC under China’s ETS policy and its performance under different carbon tax levels in China and other regions. This also contributes to achieving the goal of low-carbon economic development. Full article

The core problem of a multimodal transportation system is integrating various transportation modes into a cohesive, efficient, and user-friendly network. This study introduces a novel centralized load concentration approach for regions facing geographic challenges. The principal aim is improving multimodal transportation systems by mitigating CO₂ emissions and improving operational efficiency. This will significantly reduce high logistics costs and the environmental impact caused by greenhouse gas emissions, particularly in land transportation, aligning with the global sustainable development goals and offering a promising path towards a more sustainable future. The proposed method implicates direct cargo transportation from its origin to the export ports without passing through intermediate centers. The mathematical model determines the most efficient means of transportation for each route, considering variables such as distance, volume, and type of cargo. Research results indicate that multiple hubs may not be necessary in scenarios with high freight concentration, which could streamline transportation and logistics operations. The modal preferences vary depending on regional dynamics and cargo characteristics, with rail and sea transport emerging as preferable options in specific circumstances, outperforming road transport. The proposed model shows reductions in logistics costs and CO₂ emissions compared to road-focused scenarios. This study provides an adaptable framework for optimizing multimodal transportation systems in regions with similar geographic and logistical attributes. It offers a versatile solution to various contexts and needs. Lastly, the strategic integration of multiple modes of transportation is fundamental to improving efficiency and sustainability. Full article

Cross-border e-commerce logistics activities increasingly use multimodal transportation modes. In this transportation mode, the use of high-performance optimizers to provide decision support for multimodal transportation for cross-border e-commerce needs to be given attention. This study constructs a logistics distribution optimization model for cross-border e-commerce multimodal transportation. The mathematical model aims to minimize distribution costs, minimize carbon emissions during the distribution process, and maximize customer satisfaction as objective functions. It also considers constraints from multiple dimensions, such as cargo aircraft and vehicle load limitations. Meanwhile, corresponding improvement strategies were designed based on the Sand Cat Swarm Optimization (SCSO) algorithm. An improved swarm intelligence algorithm was proposed to develop an optimizer based on the improved swarm intelligence algorithm for model solving. The effectiveness of the proposed mathematical model and improved swarm intelligence algorithm was verified through a real-world case of cross-border e-commerce logistics transportation. The results indicate that using the proposed solution in this study, the cost of delivery and carbon emissions can be reduced, while customer satisfaction can be improved. Full article

Modern rail/road transportation systems are critical to global travel and commercial transportation. The improvement of transport systems that are needed for efficient cargo movements possesses further challenges. For instance, diesel-powered trucks and goods trains are widely used in long-haul unimodal transportation of heavy cargo in most landlocked and developing countries, a situation that leads to concerns of greenhouse gases (GHGs) such as carbon dioxide coming from diesel fuel combustion. In this context, it is critical to understand aspects such as the use of some parameters, variables and constraints in the formulation of mathematical models, optimization techniques and algorithms that directly contribute to sustainable transportation solutions. In seeking sustainable solutions to the bulk cargo long-haul transportation problems in Zambia, we conduct a systematic review of various transportation modes and related mathematical models, and optimization approaches. In this paper, we provide an updated survey of various transport models for bulk cargo and their associated optimized combinations. We identify key research challenges and notable issues to be considered for further studies in transport system optimization, especially when dealing with long-haul unimodal or single-mode heavy cargo movement in countries that are yet to implement intermodal and multimodal systems. Full article

This research is a case study on the United Arab Emirates (UAE), exploring multimodal logistics, which involves transporting cargo using multiple modes of transportation, and investigating its challenges and opportunities during the COVID-19 pandemic from 2020 to 2022. Through a mixed-method approach of qualitative interviews and quantitative surveys, this study examines factors influencing multimodal cargo transport and its performance. Five senior executives from the logistics industry were interviewed to identify key variables, and a questionnaire was administered to 120 participants to assess the impact on shipping costs and utilization. This study reveals a significant relationship between geographical and geopolitical risks and increased shipping costs in certain regions, highlighting the need for secure and cost-effective multimodal solutions in these areas. However, shipping costs did not mediate the performance of intermodal transportation at transit hubs during the pandemic. The findings offer valuable insights for transit hubs to enhance the utilization of multimodal cargo transport during uncertain times, ultimately leading to improved logistics performance in similar hub countries. This study’s originality lies in its investigation of the resilience and sustainability dimensions in multimodal logistics during the pandemic, proposing mitigation strategies and enhancing strategic decision making in the logistics industry under volatile business environments. Future research is recommended to expand the model’s results by including data from other logistics corridors and hubs. Full article