One objective for countries in the European common market is to optimize the performance of their multimodal logistics chains. The attainment of this goal requires the continuous development of container ports' performance, better customer satisfaction and - at the same time - to deter the occurrence of waste and bottleneck. Many regions in Europe are shifting from a single-port to a multi-port gateway situation; their ports frequently have overlapping hinterlands and are therefore increasingly facing competition and rivalry between each other. This paper examines container ports located in six countries: Denmark, Finland, Iceland, Norway, Sweden and the UK. It focuses on sensitivities to the inclusion of country-specific measurements on logistics service delivery performance outcomes on port efficiency. Port efficiency is measured with Data Envelopment Analysis (DEA). The results suggest that: (1) efficiency measurements for Danish, Finnish, Swedish and British ports are heavily influenced by whether logistics service delivery outcomes are included or not; (2) Icelandic and Norwegian ports appear to be not sensitive to whether logistics service delivery outcomes are included or not; (3) on average, the container ports located in countries that are directly called by deep-sea transcontinental container liners are over-performers and under-performers with regard to technical efficiency and scale efficiency, respectively. We further apply a second-stage regression analysis to explain the impact of country-specific contextual factors on DEA-based efficiency scores.
The objective of this article is to provide a review of literature dealing with empty container repositioning. This review is interlinked with a qualitative data analysis based on semi-structured interviews with representatives of ocean carriers, which are key actors determining empty container repositioning. Empirical evidence obtained from fieldwork in the Czech Republic, albeit limited, is used to illustrate empty repositioning management by ocean carriers in the Central and Eastern Europe (CEE) landlocked hinterlands, which have been neglected in research with a specific geographic scope. By addressing the research questions and conducting the analysis, the authors determine whether empty container repositioning is a problem only concerning equipment allocation by an ocean carrier or requires a collaborative resolution involving various parties engaged in container movements in landlocked hinterlands. This article confirms that most existing literature dealing with empty container repositioning ignores the actual dynamics of landlocked hinterlands as well as business practitioners' perspective. The authors' analysis of the empirical research complements and challenges the reviewed research studies. Based on the analysis, ocean carriers seem to be unwilling to revise their actual container management strategies focused on maritime repositioning, disregarding the potential and importance of intermodal repositioning approaches based on market collaboration. Regarding further research directions, the authors suggest the research replicability and its extension.
Over the recent decades, there has been an increasing focus on energy-efficient operation of vessels. It has become part of the political agenda, where regulation is the main driver, but the maritime industry itself has also been driven towards more energy-efficient operation of the vessels, due to increasing fuel costs. Improving the energy efficiency on board vessels is not only a technical issue - factors such as awareness of the problem, knowledge skills and motivation are also important parameters that must be considered.
The paper shows how training in energy-efficient operation and awareness can affect the energy consumption of vessels. The study is based on navigational, full-mission simulator tests conducted at the International Maritime Academy SIMAC. A full-mission simulator is an image of the world allowing the students to obtain skills through learning-by-doing in a safe environment. Human factors and technical issues were included and the test sessions consisted of a combination of practical simulator exercises and reflection workshops.
The result of the simulator tests showed that a combination of installing technical equipment and raising awareness - making room for reflections-on and in-action - has a positive effect on energy consumption. The participants, on average, saved approximately 10% in fuel.
The literature on liner shipping includes many models on containership speed optimization, fleet deployment, fleet size and mix, network design and other problem variants and combinations. Many of these models, and in fact most models at the tactical planning level, assume a fixed revenue for the ship operator and as a result they typically minimize costs. This treatment does not capture a fundamental characteristic of shipping market behavior, that ships tend to speed up in periods of high freight rates and slow down in depressed market conditions. This paper develops a simple model for a fixed route scenario which, among other things, incorporates the influence of freight rates, along with that of fuel prices and cargo inventory costs into the overall decision process. The objective to be maximized is the line’s average daily profit. Departing from convention, the model is also able to consider flexible service frequencies, to be selected from a broader set than the standard assumption of one call per week. It is shown that this may lead to better solutions and that the cost of forcing a fixed frequency can be significant. Such cost is attributed either to additional fuel cost if the fleet is forced to sail faster to accommodate a frequency that is higher than the optimal one, or to lost income if the opposite is the case. The impact of the line’s decisions on CO2 emissions is also examined and illustrative runs of the model are made on three existing services.
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 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.
The selection of alternative energy sources for shipping can effectively mitigate the problems of high energy consumption and severe environmental problems caused by shipping. However, it is usually difficult for decision makers to select the most sustainable alternative energy source for shipping among multiple alternatives due to the complexity of considering different aspects of performances and the lack of information. This study developed a novel multi-criteria decision-making method that combines Dempster-Shafer theory and a trapezoidal fuzzy analytic hierarchy process for alternative energy source selection under incomplete information conditions. According to the developed method, nuclear power has been recognized as the most sustainable alternative energy source for shipping, followed by liquefied natural gas (LNG) and wind power, and sensitivity analysis reveals that the weights of the criteria have significant on the sustainability sequence of the three alternative energy sources for shipping. The developed method can be popularized for selecting the most sustainable alternative energy source despite incomplete information.
Seaborne trade is the lynchpin in almost every international supply chain, and about 90% of non-bulk cargo worldwide is transported by container. In this survey we give an overview of data-driven optimization problems in liner shipping. Research in liner shipping is motivated by a need for handling still more complex decision problems, based on big data sets and going across several organizational entities. Moreover, liner shipping optimization problems are pushing the limits of optimization methods, creating a new breeding ground for advanced modelling and solution methods. Starting from liner shipping network design, we consider the problem of container routing and speed optimization. Next, we consider empty container repositioning and stowage planning as well as disruption management. In addition, the problem of bunker purchasing is considered in depth. In each section we give a clear problem description, bring an overview of the existing literature, and go in depth with a specific model that somehow is essential for the problem. We conclude the survey by giving an introduction to the public benchmark instances LINER-LIB. Finally, we discuss future challenges and give directions for further research.
Carriers in liner shipping markets frequently make public announcements of general rate increase (GRI) intentions, based on which EU authorities have concerns as to whether this harms market competition. This paper aims to empirically investigate how well the GRI system works from an industrial competition perspective, which will indirectly indicate whether carriers are able to manipulate spot rates following GRI announcements. Taking the Far East–North Europe trade between 2009 and 2013 as an example, the paper first reveals the gradual increase of GRI frequency and size, which reflects carriers’ attempts to restore profitability against overcapacity. However, out of all the GRI events only 28.6% were observed to be successful. Since these GRI successes must be the results of either price collusion (if any) and/or normal rate change by carriers in response to fundamental market developments, the effective collusion, if it exists, is actually lower than 28.6%. Next, we identify eight factors influencing GRI successes. To further assess their impact, we applied an ordered logit regression analysis, which, based on four of the factors involved, yields good predictability for GRI success. The four factors, in sequence of explanation power, are the total capacity of GRI carriers, the idling fleet size, the spot rate level, and the average ship-loading factor. Clearly the latter three factors are market fundamentals, which are unlikely to be influenced by an individual carrier in the short term. In actual fact, the conclusion reached is that there is little evidence that carriers can manipulate and distort spot rates through GRIs.
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.