This chapter is a historical case study of Maersk Line, the world’s leading container carrier. Maersk Line’s global leadership was achieved within a relatively short time period and was the result of Mærsk Mc-Kinney Møllers decision in 1973 to enter container shipping—the biggest investment in the history of the AP Moller companies. When Maersk Line managed to achieve global leadership in a period of just about 25 years, the company’s own country offices were particularly important. They allowed the interconnection of three types of networks: The physical network of ships and routes, the digital network of information and communication systems and the human network of Maersk employees. The interaction between the vessels, the systems and the people is still at the core of the company today and central to its continued development.
The liner-shipping network design problem is to create a set of nonsimple cyclic sailing routes for a designated fleet of container vessels that jointly transports multiple commodities. The objective is to maximize the revenue of cargo transport while minimizing the costs of operation. The potential for making cost-effective and energy-efficient liner-shipping networks using operations research (OR) is huge and neglected. The implementation of logistic planning tools based upon OR has enhanced performance of airlines, railways, and general transportation companies, but within the field of liner shipping, applications of OR are scarce. We believe that access to domain knowledge and data is a barrier for researchers to approach the important liner-shipping network design problem. The purpose of the benchmark suite and the paper at hand is to provide easy access to the domain and the data sources of liner shipping for OR researchers in general. We describe and analyze the liner-shipping domain applied to network design and present a rich integer programming model based on services that constitute the fixed schedule of a liner shipping company. We prove the liner-shipping network design problem to be strongly NP-hard. A benchmark suite of data instances to reflect the business structure of a global liner shipping network is presented. The design of the benchmark suite is discussed in relation to industry standards, business rules, and mathematical programming. The data are based on real-life data from the largest global liner-shipping company, Maersk Line, and supplemented by data from several industry and public stakeholders. Computational results yielding the first best known solutions for six of the seven benchmark instances is provided using a heuristic combining tabu search and heuristic column generation.
In this research, two crucial optimization problems of berth allocation and yard assignment in the context of bulk ports are studied. We discuss how these problems are interrelated and can be combined and solved as a single large scale optimization problem. More importantly we highlight the differences in operations between bulk ports and container terminals which highlights the need to devise specific solutions for bulk ports. The objective is to minimize the total service time of vessels berthing at the port. We propose an exact solution algorithm based on a branch and price framework to solve the integrated problem. In the proposed model, the master problem is formulated as a set-partitioning problem, and subproblems to identify columns with negative reduced costs are solved using mixed integer programming. To obtain sub-optimal solutions quickly, a metaheuristic approach based on critical-shaking neighborhood search is presented. The proposed algorithms are tested and validated through numerical experiments based on instances inspired from real bulk port data. The results indicate that the algorithms can be successfully used to solve instances containing up to 40 vessels within reasonable computational time.
In transportation of goods in large container ships, shipping industries need to minimize the time spent at ports to load/unload containers. An optimal stowage of containers on board minimizes unnecessary unloading/reloading movements, while satisfying many operational constraints. We address the basic container stowage planning problem (CSPP). Different heuristics and formulations have been proposed for the CSPP, but finding an optimal stowage plan remains an open problem even for small-sized instances. We introduce a novel formulation that decomposes CSPPs into two sets of decision variables: the first defining how single container stacks evolve over time and the second modeling port-dependent constraints. Its linear relaxation is solved through stabilized column generation and with different heuristic and exact pricing algorithms. The lower bound achieved is then used to find an optimal stowage plan by solving a mixed-integer programming model. The proposed solution method outperforms the methods from the literature and can solve to optimality instances with up to 10 ports and 5,000 containers in a few minutes of computing time.
Having a well-designed liner shipping network is paramount to ensure competitive freight rates, adequate capacity on trade-lanes, and reasonable transportation times. The most successful algorithms for liner shipping network design make use of a two-phase approach, where they first design the routes of the vessels, and then flow the containers through the network in order to calculate how many of the customers’ demands can be satisfied, and what the imposed operational costs are. In this article, we reverse the approach by first flowing the containers through a relaxed network, and then design routes to match this flow. This gives a better initial solution than starting from scratch, and the relaxed network reflects the ideas behind a physical internet of having a distributed multi-segment intermodal transport. Next, the initial solution is improved by use of a variable neighborhood search method, where six different operators are used to modify the network. Since each iteration of the local search method involves solving a very complex multi-commodity flow problem to route the containers through the network, the flow problem is solved heuristically by use of a fast Lagrange heuristic. Although the Lagrange heuristic for flowing containers is 2–5% from the optimal solution, the solution quality is sufficiently good to guide the variable neighborhood search method in designing the network. Computational results are reported, showing that the developed heuristic is able to find improved solutions for large-scale instances from LINER-LIB, and it is the first heuristic to report results for the biggest WorldLarge instance.
In order to enhance sustainability in maritime shipping, shipping companies spend good efforts in improving the operational energy efficiency of existing ships. Accurate fuel consumption prediction model is a prerequisite of such operational improvements. Existing grey-box models (GBMs) are found with significant performance potential for ship fuel consumption prediction, although having a limitation of separating weather directions. Aiming to overcome this limitation, we propose a novel genetic algorithm-based GBM (GA-based GBM), where ship fuel consumption is modelled in a procedure based on basic principles of ship propulsion and the unknown parameters in this model are estimated with a GA-based procedure. Real ship operation data from a crude oil tanker over a 7-year sailing period are used to demonstrate the accuracy and reliability of the proposed model. To highlight the contribution of this work, we compare the proposed model against the latest GBM. The results show that the fitting performance of the proposed model is remarkably better, especially for oblique weather directions. The proposed model can be employed as a basis of ship energy efficiency management programs to reduce fuel consumption and greenhouse gas (GHG) emissions of a ship. This is beneficial to achieve the goal of sustainable shipping.
This paper aims to conduct an updated literature survey on the Market-Based Measures (MBMs) currently being proposed by various member states and organizations at the International Maritime Organization (IMO) or by the scientific and grey literature as a cost-effective solution to reduce greenhouse gas (GHG) emissions from ships. The paper collects, summarizes, and categorizes the different proposals to provide a clear understanding of the existing discussions on the field and also identifies the areas of prior investigation in order to prevent duplication and to avoid the future discussion at the IMO to start from scratch. Relevant European Union (EU) action on MBMs is also described. Furthermore, the study identifies inconsistencies, gaps in research, conflicting studies, or unanswered questions that form challenges for the implementation of any environmental policy at a global level for shipping. Finally, by providing foundational knowledge on the topic of MBMs for shipping and by exploring inadequately investigated areas, the study addresses concrete research questions that can be investigated and resolved by the scientific and shipping community.
The purpose of this paper is to investigate a multiple ship routing and speed optimization problem under time, cost and environmental objectives. A branch and price algorithm as well as a constraint programming model are developed that consider (a) fuel consumption as a function of payload, (b) fuel price as an explicit input, (c) freight rate as an input, and (d) in-transit cargo inventory costs. The alternative objective functions are minimum total trip duration, minimum total cost and minimum emissions. Computational experience with the algorithm is reported on a variety of scenarios.
This article seeks to provide a sociological understanding both of the logistics service field and of the impact the COVID-19 pandemic has had on this field. To address this issue, we analyze the case of logistics services used by the shipping industry to manage maritime safety, namely statutory and classification services. These services verify the compliance of shipping vessels with private and public safety norms. We develop the Bourdieusian concept of nomos, according to three dimensions: A normative framework, a legitimate vision, and structural divisions. The findings highlight a complex set of industrial and regulatory norms which give rise to complementary and sometimes overlapping obligations. Nomos materializes as some actors, typically IACS member societies, benefit from an uncontested legitimacy to deliver such services, whereas other actors are excluded for a variety of reasons.
In tramp shipping, a preliminary route is required for voyage planning at the pre-fixture stage (before a chartering contract is agreed). Such routes are conventionally designated by using pilot charts or software considering long-term statistical weather. However, it has been experienced by tramp operators that such route solutions often poorly estimated sailing distances for long journeys and thereby cause inappropriate cost estimation and bad voyage plan. To fill this gap, a data-driven methodology is proposed in this paper to establish a practical route library with the consideration of ship sizes, load conditions and seasonality. In this method, it first requires a dividing of ship trajectories into local sea passage and open sea passage. The voyage trajectories made of AIS points are then simplified to pattern nodes based on a speed-weighted geolocation method. Afterwards, the KMeans algorithm is deployed to properly classify these pattern nodes, identifying the most representative nodes (routes) in open sea passages. Simultaneously, the connection points are identified by DBSCAN algorithm, representing local sea passages. Combining the representative routes in open sea passages and the connection points in local sea passages, the most navigated routes between two ports are obtained. Finally, case studies are conducted for the Pacific Ocean and the Atlantic Ocean respectively using global AIS data from tanker vessels to demonstrate the feasibility and effectiveness of this methodology. The proposed route library is capable of providing reliable route references to support the decision-making at the pre-fixture stage.