Liner Shipping Research Articles

Investigating Fuel Reduction Methods Through Propeller Optimization on Coastal Ro-Ro Liner Vessels

In light of efforts to reduce GHG emissions, liner ship operators in coastal navigation are undertaking numerous activities to contribute to the reduction of fossil fuel consumption during the transitional period toward environmentally friendly propulsion systems without compromising their regular operations. Regular ship overhauls, which include removing fouling from the underwater part of the hull and optimizing propellers, also contribute to this goal and are the focus of this research. The ship propeller, as a key component of the propulsion system, plays a significant role in ensuring reliable, safe, and economical navigation. Proper selection and regular maintenance of the propeller are essential for achieving optimal performance. This paper presents the optimization of a fixed-pitch propeller according to the ISO 484/2 standard to Class I using Metrascan 3D technology. This technology enables a detailed analysis of the propeller geometry, identification of irregularities, correction, and adjustment to achieve better hydrodynamic characteristics. The pitch corrections of the propeller resulted in improved overall vessel performance and fuel consumption, as demonstrated by comparing the consumption before and after the optimization.

Impact of port integration on port service quality in the context of shipping alliance

This paper explores the impact of port integration on the port service quality under a chain structure where the interactions among ports, shippers and shipping lines are considered. A game theoretic framework is proposed to investigate the decisions of ports (the determination of quality-of-service level), shippers (the choice of departure ports) and shipping lines (whether forming an alliance and the corresponding operating arrangement) jointly. Based on the game theoretic framework, the mathematical models are established and the corresponding solution algorithm is designed. The methodology is applied in two representative port clusters with port integration in China. The case-study results demonstrate two important findings. Firstly, both positive and negative impact of port integration on the level of quality-of-service for shippers and for shipping lines is likely to appear. The effect on the port service quality would vary with the difference in the integration scenarios. Secondly, the port integration would stimulate shipping lines to offer more frequent portcalls, indicating that the hinterland shippers would enjoy more opportunities to be served by shipping lines. However, when excessive port integration takes place, the shipping lines would tend to change the strategy of forming an alliance to the strategy of competing.

Evolution and stability of liner shipping networks in Northeast Asia from 2018 to 2022

Ports are severely challenged by the uncertainty of the transportation environment. To evaluate the stability of maritime transportation network in Northeast Asia under port disruption, a network stability analysis model based on node failure rate that considers static and structural, dynamic and functional is proposed in this paper. In this model, the stability of the maritime transportation network is redefined. Based on complex network theory, the statistical topological parameters and evolution of the network are quantitatively analyzed. The active response and passive adaptation process of the maritime transportation network to the failure rate of the port are analyzed. The evaluation results illustrate that the initial stability of the port is proportional to the non-redundant connections and independence of the port. Network location advantages profoundly affect the probability of port failures. The diffusion of port failures in maritime transportation is phased, and the network is adaptive to port disruptions and has high stability. These results contribute to better planning of maritime transportation routes and strategies for port calls by strategic planners of shipping companies.

Liner fleet deployment and empty container repositioning under demand uncertainty: A robust optimization approach

This paper investigates a robust optimization problem concerning the integration of fleet deployment and empty container repositioning in a shipping line network, where a fleet of vessels is dispatched to transport both laden and empty containers, aiming to fulfill a predetermined set of requests over a defined time horizon. The sizes of customer demands are uncertain and are characterized by a budgeted uncertainty set. This study aims to ascertain the vessel types assigned to each shipping route, the routing of laden containers, and the repositioning of empty containers in a manner that minimizes the total cost. Simultaneously, it ensures the feasibility of all transportation plans for any realization of demand within the uncertainty set. We introduce a path-based two-stage robust formulation for addressing the problem. In the first stage, the assignment of vessel types to each shipping route is determined, and the second stage focuses on establishing the routing of laden containers and repositioning of empty containers under a worst-case scenario. We propose the Column-and-Constraint Generation algorithm for solving the proposed robust formulation. To address large-scale size instances, we propose an acceleration technique, i.e., the piece-wise affine policy, which reduces the dimensions of the uncertainty set while maintaining a bounded compromise in solution quality. Comprehensive numerical experiments derived from real-world industries, such as the Shanghai port and CMA CGM, are conducted to validate the proposed formulation and solution methodologies.

Evaluating eco-economic benefits of anchoring and drifting under government sulfur emission policies

Driven by global sulphur strategy, reducing sulfur emissions from the global shipping industry is of great significance for achieving sustainable development goals related to addressing climate change. This study explores the impact of government sulfur emission policies on the choice of mooring modes, include anchoring and drifting, for shipping lines. In this study, a bi-level programming model is constructed and linearized to minimize the total vessel emissions in emission control areas during the planning horizon and the total cost of each type of vessel. The leader of this model is the department that issues regulations and decides on the anchorage to regulate whether marine gas oil must be used. The followers are the shipping lines that execute transportation tasks and choose whether a vessel will anchor or drift. In addition, many numerical experiments and sensitivity analysis are conducted, and data and theoretical support for relevant decision-making are provided.

정기선 해운 연결성이 경제성장에 미치는 영향에 관한 연구: 서아프리카 개발도상국을 중심으로

Purpose: This paper investigates the impact on economic growth of liner shipping connectivity. Establishing an efficient logistics infrastructure plays a pivotal role in developing a national economy. Among various sub-sectors in the logistics system, a great deal of empirical evidence indicates the importance of a maritime transport infrastructure. Research design, data, and methodology: In this regard, this study examines the impact of liner shipping connectivity on gross domestic product (GDP) growth in 14 West African countries. To this end, a dataset for the period from 2006 to 2022 is analyzed in panel data regression models. Results: L iner s hipping c onnectivity i s a ssociated p ositively w ith GDP g rowth i n A frican countries. However, further analysis reveals that the positive impact of liner shipping connectivity is significant for parity in the GDP purchasing power of small-sized economies. Conclusions: The findings in this paper provide valuable implications for policy-making in the establishment of a maritime transport infrastructure and network.

Toward green container liner shipping: joint optimization of heterogeneous fleet deployment, speed optimization, and fuel bunkering

AbstractContainer liner shipping companies, under the international shipping carbon reduction indicators proposed by the International Maritime Organization, must transform two key aspects: technology and operations. This paper defines a green liner shipping problem (GLSP) that integrates the deployment of a heterogeneous fleet, speed determination, and fuel bunkering. The objective is to achieve low‐carbon operations in liner shipping, taking into consideration the diversification of power systems, the use of alternative fuels in ships, and the continuous improvement of alternative fuel bunkering systems. For this purpose, we present a bi‐objective mixed integer nonlinear programming model and develop two methodologies: an epsilon‐constraint approach and a heuristic‐based multi‐objective genetic algorithm. We validate the effectiveness of our model and methods through a case study involving container ships of various sizes deployed on intra‐Asian short sea routes by SITC International Holdings Co., Ltd. The experimental results highlight the crucial role of dual‐fuel (DF) ships in the pursuit of low‐carbon strategies by liner companies, with liquefied natural gas and ammonia DF ships being the most widely used. Additionally, fuel cell (FC) ships, particularly those powered by ammonia and hydrogen, demonstrate significant carbon reduction potential. Furthermore, ships with larger container capacities have a greater cost advantage. For the GLSP, speed determination is an auxiliary decision, and the lowest speed is not necessarily the optimal choice. Decision‐makers must carefully balance competing economic and carbon emission reduction objectives, as deploying more alternative fuel ships may increase fuel bunkering and fuel consumption, resulting in a higher total operating cost.

Product transportation strategy in the presence of limited shipment capacity and purchase timing

ABSTRACT In maritime practice, shippers can order shipment capacity before knowing product demand and then transport those products via liner shipping (LS) after observing demand information, or shippers can transport them when needed via tramp shipping (TS) without pre-ordering shipment. We develop a game-theoretic model to analyse these two transportation modes, LS and TS, in the presence or absence of demand and supply shocks such as those occurring in a pandemic. Interestingly, when vessels have a sufficiently small shipment capacity, the shipper prefers LS (TS) when market uncertainty is high (low) if the following is true: (a) the market size is moderate and there are no such shocks, or (b) the market is not sufficiently large in the case of shocks. This conclusion contradicts the naïve belief that the shipper prefers LS (TS) in the case of low (high) market uncertainty. Additionally, when the market is sufficiently large, the shipper prefers TS only if dead freight rate is low and the tramp vessel’s shipment capacity is moderately greater than that of LS. It follows that, due to high freight charges or low profit, the TS mode may not be the shipper’s best choice, even if the transportation demand is very large.

Modeling the resilience of liner shipping network under cascading effects: Considering distance constraints and transportation time

The Liner Shipping Network (LSN) is a crucial component of the maritime supply chain (MSC) but remains susceptible to disruptions caused by natural disasters or emergencies, given its intricate structure and high level of interdependence. In this study, we present a novel cascading failure model that accounts for the heterogeneity of distances and transportation times between nodes, coupled with actual shipping lines. This comprehensive approach aims to thoroughly evaluate the resilience of the LSN. The study’s results reveal noteworthy variations in the cascading effects across diverse load distribution distances and transportation times. Furthermore, it delves into load distribution strategies to improve the resilience of the LSN and investigates the impact of capacity parameters on cascading effects. The results indicate that the distribution strategy proposed in this paper, integrating transportation time and redundancy levels, effectively suppresses the spread of cascading faults and improves network resilience. The outcomes of this research contribute to a deeper understanding of strategies for enhancing resilience and managing risks in shipping networks, providing valuable insights for management and decision-making in maritime operations.

Dynamic resilience analysis of the liner shipping network: From structure to cooperative mechanism

Liner shipping networks play a vital role in global and regional trade. However, they are susceptible to damage from unexpected interruptions, which can trigger dynamic cascading failures and undermine the system’s resilience. To address this challenge, we propose a novel cascading failure model for liner shipping networks that considers the characteristics of the network structure and port functions. First, we design two load redistribution methods that rely on network topology and employ a cooperative mechanism for coordination. This cooperative mechanism aims to balance the benefits for carriers and shippers by effectively controlling losses. Subsequently, we develop three metrics—network congestion rate, failure rate, and shipper loss—to assess the resilience of the network during cascading failures. To verify the impact of the cooperative mechanism, we apply the proposed methods to the China-Europe liner shipping network. Through simulations involving various port failures and resistance levels, we analyze the effectiveness of the cooperative mechanism. The results demonstrate that redistributing the load to downstream ports within the network effectively mitigates deep cascading failures. Additionally, the implementation of a port cooperative mechanism enhances resilience in the face of uncertainties by safeguarding crucial ports within the network and significantly reducing shipper losses. When port resistance is low, the cooperative mechanism reduces shipper losses by nearly half and lowers the average congestion rate. Although port reserve capacity can resist cascading failures, it falls short in the face of severe disruptions. In such cases, the cooperative mechanism compensates for capacity shortages, enhancing port resilience at a low cost. This study contributes to combating and minimizing cascading congestion in liner shipping networks, offering valuable insights for risk prevention and management strategies for ports and shipping companies. It also has implications for yield management and policy decisions from a network perspective.

Analysis of the Liner Shipping Network Structure of the Asia–Europe Main Trunk Route Using Social Network Analysis

Due to COVID-19, the shipping market has faced uncertainty, and the possibility of changes in port routes has increased. The purpose of this study was to analyze the network of container liner shipping routes between Asia and Europe. In particular, this research focused on a global risky situation—the COVID-19 pandemic. The data examined encompassed Asia–Europe route schedules from January 2018 to October 2021, which exhibited significant fluctuations due to the COVID-19 pandemic originating in 2019. To access this problem, utilizing concepts of centrality from social network analysis (SNA), namely degree centrality and betweenness centrality, this analysis incorporated route capacity as a weighted factor. The findings revealed that the port of Rotterdam held the highest degree of centrality in 2018, 2019, and 2021, while Shanghai claimed the highest degree of centrality in 2020. Singapore exhibited the highest betweenness centrality. Asian ports wielded greater influence during the COVID-19 pandemic compared to European ports. Furthermore, Singapore emerged as a pivotal mediator in the Asia–Europe routes, playing a significant role within the global supply chain. Results showed that the port could be put into an unstable situation. Therefore, the managers of port and shipping companies should be ready to minimize risk. From an academic perspective, it is difficult to integrate and analyze container liner schedules as they are monthly updated. This study therefore analyzed continuous schedules to examine dynamic changes in schedules. By adopting SNA, we presented changes in connectivity over multiple periods. This study addressed questions stakeholders may have had about route changes during the global crisis, contributing to sustainable container transportation. This study provides a general understanding of Asia–Europe container scheduling for decision makers. Using market schedules, this research analyzed the connections, and evaluated and compared each port.

Run, not walk: advanced red queen effect and mutual forbearance effect in multimarket contact

PurposeThis study explores the evolutionary relationship between multimarket contact (MMC) and competitive actions among multinational corporations (MNCs). It aims to enhance the understanding of international market competition by incorporating insights into dynamic competition and parent–subsidiary relationships.Design/methodology/approachA structured content analysis was used to identify the competitive actions of global shipping liners. The dataset includes 8,204 actions identified across nine global arenas. Data were collected from 6,553 monthly news articles on Alphaliner. The period covered is from January 1, 2015, to June 30, 2023.FindingsThe results indicate that a higher degree of MMC leads to greater competitive aggressiveness, supporting the combination of mutual forbearance and the Red Queen effect. Additionally, market importance triggers the mutual forbearance effect, whereas competitive rivalry is weaker for overlapping cross-market contacts. Furthermore, local competitive intensity increases MNCs' contact and echoes the Red Queen effect, especially for subsidiaries facing increasing pressure from local responsiveness.Research limitations/implicationsLimitations include reliance on Alphaliner, potential inaccuracies from proxy variables, and unmeasured headquarters–subsidiary interactions. Future research should explore other industries and extend the study period for broader applicability and generalization.Practical implicationsBy interlacing mutual forbearance with the Red Queen effect within a coopetition framework, managers can devise strategies to balance competition and collaboration, thereby ensuring long-term viability and growth in global markets.Originality/valueThis study extends the concept of MMC to the context of global shipping liners, a previously underexplored sector. Unlike earlier research, this study empirically examines MMC dynamics globally and integrates mutual forbearance and the Red Queen effect.

Decarbonizing the inland container fleet with carbon cap-and-trade scheme

Market-based measures, like the carbon cap-and-trade (CCT) scheme, can effectively reduce emissions and promote green technology by internalizing the external costs in the shipping industry. Under the CCT scheme, the government sets the quantity commitment of carbon emissions for a unit of shipping work, and the excessive quantity must be traded from the inner shipping market. To cope with carbon regulation, carriers can adjust their ship deployment plan and adopt new engine technologies to support low-carbon fuels. This paper proposes a bi-level programming model to investigate the effects of the CCT scheme on the decisions of carriers in a competitive inland container shipping network. At the upper level, carriers adjust their own ship deployment plan and the choice of technology and fuel type. At the lower level, the shippers between different origins and destinations choose the shipping lines with the given ship deployment plans of carriers. Two kinds of equilibrium are considered in the proposed problem: the competitive equilibrium of carriers on freight shipment in the inland shipping network and the trading equilibrium of carbon quotas. The former equilibrium can be captured by a traffic assignment model in a similar transit transportation network, and the latter equilibrium follows the idea of the demand–supply equilibrium of carbon quotas in the traditional market. The two equilibria interact since the freight distribution affects the shipping revenue of each ship and the trading equilibrium is related to the total cost of the ship. The properties of the optimal choice of technology and fuel type are theoretically studied. We find that the optimal choice of fuel technology for each ship assigned to any shipping line at any given CCT must be Pareto optimal in the sense of cost-related and emission-relative measures. Based on the result, the optimal solution of the proposed model can be calculated by a sampling-based method. Numerical examples based on the Yangtze River are further adopted to illustrate the proposed model and conclusions.

Joint planning for fuel switching ships in a liner shipping network with transit time

Joint planning for fuel switching ships in a liner shipping network with transit time

Sustainable truck platooning operations in maritime shipping: A data-driven approach

In liner shipping, stakeholders are increasingly committed to adopting autonomous and environmentally friendly transportation solutions, especially for truck operations managing container transfers. Beyond reducing labor costs, truck platooning technology—which enables autonomous trucks to operate in close formations, thereby significantly decreasing fuel consumption—promises to revolutionize fleets involved in maritime container transport. However, the potential of these benefits hinges on the process of developing and implementing optimization plans that address the specific challenges of container logistics, particularly in integrating truck platooning plans. In response to this need, this study extends the traditional instant-dispatch strategy by proposing a novel, data-driven dispatch strategy. We develop algorithms for both models and conduct extensive experiments focusing on truck operations for sea freight containers. Our findings reveal significant advantages of the data-driven dispatch strategy: it substantially reduces the total costs and fuel consumption associated with truck deliveries compared to the instant-dispatch strategy.

The end of the block exemption for liner shipping

The end of the block exemption for liner shipping

A Unique Perspective on Vulnerability and Route Dynamics in the Global Liner Shipping Network

A Unique Perspective on Vulnerability and Route Dynamics in the Global Liner Shipping Network

Strategy and Impact of Liner Shipping Schedule Recovery under ECA Regulation and Disruptive Events

Strategy and Impact of Liner Shipping Schedule Recovery under ECA Regulation and Disruptive Events

Multi-Scale Higher-Order Dependencies (MSHOD): Higher-Order Interactions Mining and Key Nodes Identification for Global Liner Shipping Network

Liner shipping accounts for over 80% of the global transportation volume, making substantial contributions to world trade and economic development. To advance global economic integration further, it is essential to link the flows of global liner shipping routes with the complex system of international trade, thereby supporting liner shipping as an effective framework for analyzing international trade and geopolitical trends. Traditional methods based on first-order global liner shipping networks, operating at a single scale, lack sufficient descriptive power for multi-variable sequential interactions and data representation accuracy among nodes. This paper proposes an effective methodology termed “Multi-Scale Higher-Order Dependencies (MSHOD)” that adeptly reveals the complexity of higher-order interactions among multi-scale nodes within the global liner shipping network. The key step of this method is to construct high-order dependency networks through multi-scale attributes. Based on the critical role of high-order interactions, a method for key node identification has been proposed. Experiments demonstrate that, compared to other methods, MSHOD can more effectively identify multi-scale nodes with regional dependencies. These nodes and their generated higher-order interactions could have transformative impacts on the network’s flow and stability. Therefore, by integrating multi-scale analysis methods to mine high-order interactions and identify key nodes with regional dependencies, this approach provides robust insights for assessing policy implementation effects, preventing unforeseen incidents, and revealing regional dual-circulation economic models, thereby contributing to strategies for global, stable development.

New clusterization of global seaport countries based on their DEA and FDEA network efficiency scores.

Global seaport network efficiency can be measured using the Liner Shipping Connectivity Index (LSCI) with Gross Domestic Product. This paper utilizes k-means and hierarchical strategies by leveraging the results obtained from Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) to cluster 133 countries based on their seaport network efficiency scores. Previous studies have explored hkmeans clustering for traffic, maritime transportation management, swarm optimization, vessel trajectory prediction, vessels behaviours, vehicular ad hoc network etc. However, there remains a notable absence of clustering research specifically addressing the efficiency of global seaport networks. This research proposed hkmeans as the best strategy for the seaport network efficiency clustering where our four newly founded clusters; low connectivity (LC), medium connectivity (MC), high connectivity (HC) and very high connectivity (VHC) are new applications in the field. Using the hkmeans algorithm, 24 countries have been clustered under LC, 47 countries under MC, 40 countries under HC and 22 countries under VHC. With and without a fuzzy dataset distribution, this demonstrates that the hkmeans clustering is consistent and practical to form grouping of general data types. The findings of this research can be useful for researchers, authorities, practitioners and investors in guiding their future analysis, decision and policy makings involving data grouping and prediction especially in the maritime economy and transportation industry.