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The global maritime system provides the backbone of logistics operations for global supply chains and international trade. This paper aims to develop a unifying global network representation and strategic, system-wide decision model, the Strategic Cargo Routing Model, incorporating both liner and bulk shipping markets to estimate real-world traffic flows and study traffic patterns at the global scale. Specifically, taking a shipper's perspective, containerized and bulk movements are jointly modelled within a mixed-integer linear program that includes inbound, outbound, and transshipment cargo flows at ports. An iterative approach that combines heuristic Gradient Descent and Relax-and-Fix Decomposition methods is proposed for the calibration and solution of the Strategic Cargo Routing Model over a proposed joint liner and bulk services Global Cargo Shipping Network representation. The Global Cargo Shipping Network contains 161 seaports covering 52 countries. It is created from updatable, publicly available, data sources, and all data needed for the network representation are made available. Sufficient network details, as well as data sources and methods for extracting needed inputs, are given to allow others to use and update the network. Using the developed maritime network, mathematical model and calibration-solution methodology, 2018 global maritime traffic flow patterns were estimated. The estimates were found to achieve a 91% fit overall to real-world average annual port throughputs. This strategic model provides support to evaluate future, real-world, worldwide changes, such as increased seaborne trade demand, new routes, shipping infrastructure expansion, and transport policies.

This chapter describes the global maritime container shipping model to allocate container cargo to the global liner shipping network, which is structured, based on containership movement data. In this model, each container of each maritime origin–destination pair is assumed to choose a route to minimise the total transit time, and an user equilibrium network assignment is applied with consideration of the capacity constraint of containerships. This model is not only functioned as a submodel in the lower layer of the entire simulation model, which is mentioned in the previous chapter, but also used as an independent model for some specific simulations on the maritime-related policies. The developed model is confirmed to successfully reproduce actual global container movement as a whole, including transshipment rate and volume at major hub ports.

This paper evaluates ports from the network perspective and analyzes the network of Korean and Chinese ports based on the service pattern of Korean liner shipping companies. It uncovers their structural characteristics and centrality, and explores network centrality through the service pattern of shipping companies and port throughput in 2008 and 2015 using data from nine Korean shipping companies in the Korea–China route by using social network analysis. It reveals the impacts of port network centrality on port competitiveness. The results of analysis firstly indicate that the degree centrality in the throughput flow is changing from the Busan port in Korea to the Shanghai or Qingdao port in China. Second, the export volume of Korea is decreasing, whereas the trade volume of China is increasing. Third, as for the major ports in Korea and China, China may be in a favorable position compared to Korea owing to the increase in the number of service routes, fleets, and service liner shipping companies. Hence, the centrality of the Chinese port has been increasing because the liners intentionally design their service routes with focus on Shanghai port. By referring to the study's findings, port policymakers may better understand the dynamic Korea–China route port network changes to prepare the future port strategies.

Route network patterns have been investigated globally by many researchers, with the majority of studies only focussing on the liner shipping industry for long-distance services, and with less attention paid to the feeder services industry. The feeder services industry is important to enhance the economic sustainability and viability of feeder service providers and trade partners. The primary objectives of this paper were to study the focal economy points of feeder services in Malaysia and to demonstrate the type of network patterns practised by Malaysian feeder service providers. Accordingly, an empirical study was conducted through the cooperation of eight Malaysian feeder service providers via face-to-face interviews and port visits to understand the patterns of each shipping route deployed. Moreover, to demonstrate the network patterns of each feeder service provider, a route network model was designed. The main finding from this study found that Port Klang was the primary feeder hub port and focal economy point in Malaysia due to the highest number of feeder vessels entering the port. Furthermore, the type of route patterns applied by Malaysian feeder service providers were determined and categorised based on three models: 75% (basic model), 17.5% (butterfly model), and 7.5% (chain model) collected data consisting of 40 service routes. This paper will assist future and existing feeder service providers in their planning and execution of the optimal service routes regarding their efficiency in servicing existing and future customers and promoting strategic and tactical allocation of shipping services to specific ports.

The plans for the Maritime Silk Road affect the existing liner services. The purpose of this paper is to quantify using the terms of network theory the impact of these plans and to project the effects on the fleet, utilization, port throughput, and carbon footprint. The analysis is based on publicly available information and reasonable fleet deployment assumptions. It also considers current and alternative routing schedules from Shanghai to Europe. The impact of existing and expected emission control areas (ECAs) along the port chains is also considered.

A cross section panel model is applied to estimate the effect that the Herfindahl-Hirschman Index (HHI) has on container freight rates for a sample of six major container liner shipping routes during 2009 to 2011. The estimated coefficient of HHI is non-positive and statistically insignificant, indicating that higher concentration level does not lead to high price and the container liner shipping market is contestable for the period under consideration. The suggestion that efficiency can be achieved without actual competition in a contestable market is highly significant for policy makers.

This chapter gives a comprehensive introduction to shipping services. The reader will learn about the development of seaborne trade and its structure, the container and container trade, the maritime transportation and its three modes, and the shipping service and design of liner operations.

This chapter mainly presents a critical review of the existing and related literature on the study of liner ship fleet planning. As liner shipping is one of three modes of maritime transportation, the literature review in this chapter includes related research on other two modes, namely industrial and tramp shipping. The chapter consists of five parts: the first describes previous studies on fleet size and mix problems, the second is devoted to fleet deployment problems, the third reviews the literature on ship fleet planning problems, the fourth highlights the weaknesses in the existing literature and the need for this current research, and the final part summarizes the contents of this chapter.

This chapter presents a liner ship fleet planning problem with an individual chance-constrained service level. This chapter provides a tangible methodology to deal with the liner ship fleet deployment problem aimed at minimizing the total cost while maintaining a service level for the whole shipping network under an uncertain container demand. The problem is first formulated as a joint chance-constrained programming model, and the sample average approximation method and mixed-integer programming are used to deal with it. Finally, a numerical example of a liner shipping network is carried out to verify the applicability of the proposed model and solution algorithm. It is found that the service level has significant effect on the total cost.

Understanding how container routing stands to be impacted by different scenarios of liner shipping network perturbations such as natural disasters or new major infrastructure developments is of key importance for decision-making in the liner shipping industry. The variety of actors and processes within modern supply chains and the complexity of their relationships have previously led to the development of simulation-based models, whose application has been largely compromised by their dependency on extensive and often confidential sets of data. This study proposes the application of optimisation techniques less dependent on complex data sets in order to develop a quantitative framework to assess the impacts of disruptive events on liner shipping networks. We provide a categorization of liner network perturbations, differentiating between systemic and external and formulate a container assignment model that minimises routing costs extending previous implementations to allow feasible solutions when routing capacity is reduced below transport demand. We develop a base case network for the Southeast Asia to Europe liner shipping trade and review of accidents related to port disruptions for two scenarios of seismic and political conflict hazards. Numerical results identify alternative routing paths and costs in the aftermath of port disruptions scenarios and suggest higher vulnerability of intra-regional connectivity.

This paper addresses the design of container liner shipping service networks by explicitly taking into account empty container repositioning. Two key and interrelated issues, those of deploying ships and containers are usually treated separately by most existing studies on shipping network design. In this paper, both issues are considered simultaneously. The problem is formulated as a two-stage problem. A genetic algorithm-based heuristic is developed for the problem. Through a number of numerical experiments that were conducted it was shown that the problem with the consideration of empty container repositioning provides a more insightful solution than the one without.

The explosion of global container trade in the last two decades has significantly influenced the port geography of Latin America & the Caribbean (LAC), leading to a concentration of container traffic at selected ports. Theory suggests that, as port systems become mature, they tend to deconcentration, partly due to the emergence of secondary ports. Previous research has examined the region’s dominant ports, but an unanswered research question is how the evolution of this port system is influencing and being influenced by the actions of those ports currently occupying a secondary rank in the LAC port hierarchy. The methodology is based primarily on analysis of time series data on container movements between 1997 and 2012, revealing patterns of cargo flows and transhipment location choices. The institutional context of devolution processes and new investments in the region provides additional insight into the performance of selected ports. From a theoretical perspective, this analysis is situated within the context of recent institutional approaches that examine the port’s ability to act through critical moments and junctures, in order to deepen understanding of which of the various factors influencing port system deconcentration are the most sensitive to successful institutional adaptations. Results show that the manufacturing of strategic locations can be successful and may have driven the emergence of secondary ports in the LAC system. This finding demonstrates how path dependence can be challenged by new developments, the identification and success of which are nevertheless contingent on factors such as the first mover advantage, port planning regimes and diversification of port roles. The paper identifies some of the key factors influencing the transition of a port system from concentration at a few dominant ports to a deconcentrated system of primary and secondary ports, which can be applied to other port systems in future research.

The literature has always looked at co-operation in the shipping industry with some benevolence. While co-operation benefits the transport industry as it reduces operators, guarantees higher profits and regulates supply, does it also increase the consumers’ surplus, or does it cause lower benefits and eventually inefficiency? In fact, liner shipping is the sole industry that is heavily exempted from antitrust regulations, both in Europe and North America. The paper, moving from the recent decision of the EU regarding the maritime liner sector, and from the EU Commission's Consultation Paper on the review of Regulation 4056/86, aims at investigating in what ways antitrust rules can “monitor and control” the market in a proper way according to the emerging trends of integration in the maritime logistics sector.

One of the biggest concerns in liner operations is punctuality of containerships. Managing the time factor has become a crucial issue in today's liner shipping operations. A statistic in 2015 showed that the overall punctuality for containerships only reached an on-time performance of 73%. However, vessel punctuality is affected by many factors such as the port and vessel conditions and knock-on effects of delays. As a result, this paper develops a model for analyzing and predicting the arrival punctuality of a liner vessel at ports of call under uncertain environments by using a hybrid decision-making technique, the Fuzzy Rule-Based Bayesian Network (FRBBN). In order to ensure the practicability of the model, two container vessels have been tested by using the proposed model. The results have shown that the differences between prediction values and real arrival times are only 4.2% and 6.6%, which can be considered as reasonable. This model is capable of helping liner shipping operators (LSOs) to predict the arrival punctuality of their vessel at a particular port of call. © 2017 The Korean Association of Shipping and Logistics, Inc.