The Growing Importance of Sea-Rail Intermodal Transportation
Effective ground transportation modes linkage with the seaport plays a crucial role in facilitating smooth cargo movement from marine transportation mode to the inland areas and vice versa. Unlike road transportation, rail linkage is a cost-effective and environmentally friendly option. Inadequate sea rail connectivity within the seaport hampers cargo movement speed and impacts overall port capacity.
Recent years have seen a significant increase in research into sea-rail intermodal transportation systems (SRITS). As highlighted by the literature, SRITS offers numerous benefits, including high capacity, enhanced safety, low cost, and reduced emissions. Shifting transportation systems from road to rail can alleviate road congestion and minimize greenhouse gas (GHG) emissions (Abu Aisha et al. 2021).
Achieving optimal performance in the intermodal transportation system hinges upon the effectiveness of each component within the chain, encompassing ports, shipping companies, motor carriers, and rail. It is imperative to efficiently integrate these elements in terms of operations, decision making, and information to ensure the system functions at its best.
The increasing demand for efficient cargo movement globally has made it paramount to understand and optimize the efficiency of sea-rail intermodal transportation networks. Many studies have delved into optimizing terminal operations within SRITS, emphasizing operational efficiency and resource optimization. For instance, Yan and Xu (2021) discussed adjusting yard templates and equipment deployment plans to streamline container handling processes and reduce turnaround times, while Grishin et al. (2022) addressed the challenge of unloading vessels and forming trains, aiming to minimize overall delivery time and reduce costs associated with train formation.
Efficient transport infrastructure and seamless connectivity between railway and port operations are essential for SRITS to function smoothly. Zhao et al. (2020) identified key bottlenecks in China’s sea-rail intermodal system and emphasized the importance of integrating railway and port operations to optimize cargo flow and reduce transit times. Fang (2016) highlighted challenges arising from insufficient connection conditions between railway container yards and ports, proposing coordinated efforts among stakeholders to address these challenges and enhance infrastructure connectivity.
Effective logistics management and innovation also play a crucial role in optimizing SRITS. Zhao et al. (2018a) formulated a model to enhance inbound container distribution efficiency, minimizing the total duration that containers spend in coordination areas through optimal allocation and routing strategies. Meng (2018a) provided a comprehensive analysis of the benefits of sea-rail intermodal transport, highlighting its economic, environmental, and operational benefits over traditional shipping methods. Zhao et al. (2022) used advanced technologies and automation solutions to streamline container handling processes and improve terminal efficiency.
In summary, the literature reviewed showcases diverse solutions to address the complex challenges facing SRITS, from optimizing terminal operations and improving transport infrastructure to innovative logistics management and technology integration. However, further research is needed to validate and implement these solutions in real-time scenarios, ensuring the continued optimization and sustainability of SRITS in the evolving global logistics landscape.
Balancing Economic and Environmental Considerations in SRITS
The economic aspect of sustainability plays a pivotal role in the SRITS evaluation and decision-making processes. This aspect encompasses various factors influencing the overall cost-effectiveness and financial viability of transporting cargo between ports and their hinterland.
Economies of scale are a fundamental principle in SRITS, as they minimize the overall expenses of transporting cargo. Abu Aisha et al. (2021) exemplified this principle by proposing an objective function to minimize the total transportation cost of containers from ports to their destinations. Similarly, Liu (2020) conducted a study to reduce logistics transportation costs while ensuring transportation efficiency to meet customer requirements. Yapegue and Lin (2014) adopted a comprehensive approach by minimizing the total logistics cost associated with container transportation, which included transportation expenses, loading and unloading costs, train formation and sorting costs, container storage costs, and auxiliary costs incurred during train movement.
In addition to cost considerations, some studies, such as those by Xie et al. (2022) and Zhang et al. (2021c), explicitly addressed the importance of considering costs alongside environmental criteria. These studies underscored the interconnectedness of economic and environmental sustainability within SRITS, highlighting the need for holistic approaches to decision making that balance financial considerations with environmental impacts.
Procurement optimization within SRITS has also been the focus of research, with studies examining the pivotal decision-making process that impacts the operational efficiency and competitive edge of non-vessel operating common carriers (Liu et al. 2015) and the importance of enterprise alliances in fostering railway–waterway intermodal transportation advancement (Bo et al. 2013). Researchers have also explored pricing strategies within intermodal transport, emphasizing the dynamic pricing dilemma in container sea-rail intermodal transportation amidst uncertain conditions (Di and Hualong 2012).
In summary, the literature reviewed has emphasized the critical role of economic efficiency in SRITS. By minimizing transportation costs and optimizing operational management models, researchers aim to enhance the SRITS financial viability and sustainability while meeting customer requirements and addressing environmental concerns.
Mitigating Environmental Impacts Through SRITS
Policymakers and researchers often emphasize the SRITS superior environmental performance when compared to road transport. The comparative SRITS benefit over road transport in environmental performance is a prominent theme in the literature. This benefit is attributed to the inherent characteristics of sea-rail intermodal container transportation, combining low-carbon emissions with high capacity and alleviating traffic congestion.
Many studies have investigated the environmental benefits of modal shift towards SRITS. For example, Zhang et al. (2021a) evaluated the environmental benefits of increasing SRITS use in port-connecting freight transportation in Shenzhen, China, highlighting the potential for SRITS to contribute to sustainable urban development. Similarly, Abu Aisha et al. (2020) and Fan et al. (2019) emphasized reducing greenhouse gas emissions within SRITS by promoting the use of trains for container transportation.
In addition to modal shift initiatives, infrastructural and technological innovations have emerged as key strategies for enhancing SRITS’s environmental efficiency. Wang et al. (2018) explored the integration of automated guided vehicles (AGVs) and railway tracks to improve port connectivity and reduce emissions associated with inland cargo movement, aligning with broader efforts to optimize supply chain logistics and minimize environmental impacts.
Despite the growing emphasis on environmental sustainability within SRITS, challenges and trade-offs must be addressed. Winebrake et al. (2008) highlighted the complexities of balancing cost, energy consumption, and emissions within intermodal transportation networks. Though SRITS may offer environmental benefits, there are inherent trade-offs and uncertainties that require careful consideration in policy and planning decisions.
In summary, the literature on SRITS’s environmental efficiency reflects a growing recognition of its potential to contribute to sustainable transportation practices. However, achieving meaningful environmental improvements requires a nuanced understanding of the challenges and opportunities inherent in intermodal transportation systems.
Diverse Methodologies for Analyzing SRITS
The methodologies used in analyzing SRITS reflect a diverse array of approaches that address inherent complexities. Mathematical modelling stands as a cornerstone in SRITS analysis; the use of integer linear programming is dominant across studies.
For instance, Yan and Xu (2021) proposed a multi-objective model to plan transfer flow templates within seaport railway terminals, integrating decisions regarding flow volume, yard templates, and equipment deployment. Similarly, Yan et al. (2020b) put emphasis on minimizing container dwell time at sea-rail terminals through an integer programming model, while Zhao et al. (2016) addressed optimal container routing within intermodal transportation networks, treating it as a multimodal multicommodity network flow problem to minimize overall transportation expenses.
Mixed-integer programming emerges as a prevalent methodology in SRITS research. Xie et al. (2022) introduced a bi-objective mixed-integer programming model to optimize logistics cost and time, showcasing the importance of considering multiple objectives in SRITS optimization. Similarly, Yan et al. (2020a) synchronized vessel and train operations using a mixed-integer programming model from a container terminal perspective, accounting for factors such as service time windows and train unloading requirements.
Beyond mathematical models, innovative methodologies such as simulated annealing algorithms and heuristic approaches have been implemented to manage complex operational processes within SRITS. Chang and Zhu (2019) developed a two-phase model to manage storage space allocation, introducing a simulated annealing algorithm and an enhanced heuristic algorithm to achieve balanced workloads and decreased overlap. Similarly, Luo et al. (2018) used a mixed-integer programming model to optimize gantry crane scheduling, placing emphasis on reducing task overflow during loading and unloading operations while optimizing travel distances within the yard.
The identified studies primarily shed light on optimization strategies within SRITS, aiming to address specific challenges and enhance operational efficiency. For instance, Zhao et al. (2020) and Liu (2020) proposed optimization models to reduce transportation expenses, emphasizing environmental sustainability and cost-effectiveness. Similarly, Fan et al. (2019) constructed an energy optimization model to improve the overall SRITS energy efficiency.
In addition to cost optimization, logistical challenges such as empty container movement management were addressed by Zhao et al. (2018c) and Zhao et al. (2018b) who built a nonlinear integer programming model to minimize container relocation and routing costs. The Zhao et al. (2018b) model was validated through two case studies; they showcased its feasibility and assessed how stochastic variables and chance constraints influenced optimal solution and overall cost.
Decision-making frameworks proposed by Han et al. (2020) and Wan et al. (2022) offered structured approaches to evaluating system performance and competitiveness, providing a fuzzy multi-attribute decision model to evaluate port hub competitiveness. Genetic algorithms also emerge as a promising avenue for optimizing container distribution and scheduling within SRITS, as demonstrated by Zhao et al. (2018a) and Yang et al. (2023).
The accurate prediction of SRITS growth also plays a pivotal role in new port facility development and the readiness to meet the rising demand for this mode of transportation. Tang et al. (2022) constructed a grey forecasting model to predict the sea-rail throughput at the Xiamen port, complemented by the Markov chain application to rectify relative error series. Similarly, Li (2013) used a single-variable grey sequence forecast model to anticipate container throughput at Lianyungang Harbour, while Tao (2013) devised analytical tools to evaluate potential shifts in transportation modes owing to subsidy initiatives supporting sea-rail intermodal transport.
In summary, the methodologies used in analyzing SRITS encompass a diverse range of approaches, reflecting the multifaceted nature of SRITS problems and challenges. Mathematical modelling, particularly integer linear formulations and mixed-integer programming, emerges as a predominant tool for optimizing various aspects of SRITS. Additionally, methodologies such as genetic algorithms offer promising avenues for addressing complex operational processes within SRITS. These methodologies primarily put emphasis on optimizing strategies to enhance operational efficiency, reduce transportation expenses, and tackle logistical challenges. The methodologies discussed underscore the complexity of SRITS and the need for continued investigation to enhance efficiency and address emerging challenges.
Navigating the Complexities of SRITS Planning and Operations
Decision-making processes vary significantly based on the time horizon considered. Time horizon refers to the period over which decisions are made and their impacts are felt. This horizon plays a crucial role in shaping the strategies, goals, and considerations involved in the decision-making process.
The planning levels and correlated problems can be summarized as follows:
- Strategic planning issues revolve around investment decisions regarding existing infrastructure.
- Tactical planning challenges involve the optimal use of available infrastructure by selecting services and transportation modes, allocating capacities to orders, and planning frequencies.
- Operational planning problems pertain to real-time decision-making, including order management, responses, and adjustments such as resource allocation based on demand or equipment failures.
In this section, we classify the articles into two distinct groups: those centered on transhipment operation problems and those tackling scheduling issues.
Transhipment Operation Challenges
The literature review on transhipment operations between vessels and trains in seaport rail terminals unveils crucial challenges and avenues for improving rail transportation efficiency within maritime logistics. Although rail and road transport modes facilitate goods movement through port areas, rail operations often fall short of road transport in terms of market share. This disparity underscores the urgency of refining rail operations within rail-sea yards to enable more extensive use of rail transportation, thereby mitigating the adverse externalities associated with road transport such as congestion, accidents, and environmental pollution.
Among the primary activities in the rail-sea supply chain connection, train movement between railway stations and maritime terminals alongside loading, unloading, cargo exchange between the train and ship, and storage of goods assumes paramount importance. However, the literature has largely overlooked the transition between the rail network and maritime terminals, including the processes of train division into wagon groups and subsequent transfer to cargo storage areas. It becomes imperative to shed light on this critical transition and underscore the potential for enhancements in operational efficiency.
Various scholarly endeavours have tackled facets of logistics services complexity at transhipment terminals, aiming to devise decision-making frameworks for effectively assessing service intricacies. For instance, Filina-Dawidowicz and Kostrzewski (2022) delved into the issues of logistics services complexity at transhipment terminals to devise a decision-making method capable of effectively assessing the intricacy of logistics services provided by these terminals.
Cost minimization strategies have also been explored to optimize overall transportation costs, considering factors such as container storage costs and loading/unloading operation costs. Zhao et al. (2020) conducted a study to minimize overall transportation costs by considering various cost components, leading to the consideration of various types of central rail stations to optimize outbound railway container logistics.
Efficiency in transhipment activities linking ships and trains at seaport railway terminals has also been scrutinized. Studies highlighted the significant impact of handling capacity and storage expenses on the effectiveness of transhipment strategies. Yan et al. (2020a) examined transfer plan efficiency; they found that enhancing storage expenses for imported containers resulted in a more efficient transhipment strategy.
Scheduling Challenges
Obstacles in constructing sea-rail intermodal transport systems have been elucidated by Zheng and Cai (2020). They outlined the obstacles involved in constructing sea-rail intermodal transport systems in Shanghai, emphasizing the necessity of comprehensive planning and facility integration to foster rail-sea intermodal transport advancement. While extant research has delved into storage space allocation problems in maritime container terminals, limited attention has been given to obstacles in rail–water intermodal container terminals. Chang and Zhu (2019) presented an integrated problem involving storage space allocation, combining considerations related to container block and slot allocation to optimize the efficiency and effectiveness of rail–water intermodal container terminals.
Within the sea-rail intermodal container terminal domain, challenges surrounding scheduling problems are multifaceted and require nuanced solutions for operational optimization. Recent studies have contributed valuable insights into various aspects of scheduling complexities and their implications for terminal efficiency.
Yang et al. (2023) conducted an in-depth analysis of the interplay between yard cranes, the automated quay crane (AQC), AGV, and the automated rail mounted gantry (ARMG), putting emphasis on optimizing multi-equipment scheduling in mixed operation modes while minimizing overall energy consumption. Building upon this foundation, Liu et al. (2023) delved into the integration of scheduling and path planning within sea-rail intermodal container terminals, proposing a two-level programming model that streamlined horizontal transport machinery, handling equipment, and path mapping for AGVs.
These studies collectively highlight the significance of collaborative scheduling approaches and integrated operational strategies in optimizing scheduling processes and enhancing overall efficiency within sea-rail intermodal container terminals.
Factors Influencing SRITS Performance
Port-rail connectivity is a strategic element of port development in an economic context, as well as the reduction of negative externalities on the community and the environment. Proper rail connectivity not only expands the port hinterland but also promotes capacity growth without affecting the port-city relationship by linking “spatially” fragmented processes without congesting the urban environment around the port (Matamala and Salas 2012).
Infrastructure emerges as a critical determining point of port throughput and operational efficiency, as underscored by Pehlevan and Ricci (2022). Their research emphasized the importance of infrastructure investment and development in enhancing port operations. Conversely, Wan et al. (2022) identified a significant shift in the primary factors that affect a multimodal port hub, with a move away from traditional infrastructure-related factors towards more flexible determining factors such as transportation business efficiency, capacity integration, and service quality.
Chen and Zhang (2021) delineated factors influencing SRITS into internal and external components. Internal factors directly impact system functionality, including infrastructure and resource scheduling, whereas external factors such as geographical location, economic conditions, and transportation policies exert indirect influences. This comprehensive understanding of internal and external factors is essential for devising strategies to optimize system performance and efficiency.
Operational considerations within SRITS, such as handling capacity and storage costs, are also critical factors influencing system performance, as elucidated by Yan et al. (2020a). Their findings emphasized the importance of integ
