The paper proposes a methodology for freight corridor performance monitoring that is suitable for sustainability assessments. The methodology, initiated by the EU-funded project SuperGreen, involves the periodic monitoring of a standard set of transport chains along the corridor in relation to a number of Key Performance Indicators (KPIs). It consists of decomposing the corridor into transport chains, selecting a sample of typical chains, assessing these chains through a set of KPIs, and then aggregating the chain-level KPIs to corridor-level ones using proper weights. A critical feature of this methodology concerns the selection of the sample chains and the calculation of the corresponding weights. After several rounds of development, the proposed methodology suggests a combined approach involving the use of a transport model for sample construction and weight calculation followed by stakeholder refinement and verification. The sample construction part of the methodology was tested on GreCOR, a green corridor project in the North Sea Region, using the Danish National Traffic Model as the principal source of information for both sample construction and KPI estimation. The results show that, to the extent covered by the GreCOR application, the proposed methodology can effectively assess the performance of a freight transport corridor. Combining the model-based approach for the sample construction with the study-based approach for the estimation of chain-level indicators exploits the strengths of each method and avoids their weaknesses. Possible improvements are also suggested by the paper.
This study introduces a state-of-the-art volatility forecasting method for container shipping freight rates. Over the last decade, the container shipping industry has become very unpredictable. The demolition of the shipping conferences system in 2008 for all trades calling a port in the European Union (EU) and the global financial crisis in 2009 have affected the container shipping freight market adversely towards a depressive and non-stable market environment with heavily fluctuating freight rate movements. At the same time, the approaches of forecasting container freight rates using econometric and time series modelling have been rather limited. Therefore, in this paper, we discuss contemporary container freight rate dynamics in an attempt to forecast for the Far East to Northern Europe trade lane. Methodology-wise, we employ autoregressive integrated moving average (ARIMA) as well as the combination of ARIMA and autoregressive conditional heteroscedasticity (ARCH) model, which we call ARIMARCH. We observe that ARIMARCH model provides comparatively better results than the existing freight rate forecasting models while performing short-term forecasts on a weekly as well as monthly level. We also observe remarkable influence of recurrent general rate increases on the container freight rate volatility.
In this paper speed optimization of an existing liner shipping network is solved by adjusting the port berth times. The objective is to minimize fuel consumption while retaining the customer transit times including the transhipment times. To avoid too many changes to the time table, changes of port berth times are only accepted if they lead to savings above a threshold value. Since the fuel consumption of a vessel is a non-linear convex function of the speed, it is approximated by a piecewise linear function. The developed model is solved using exact methods in less than two minutes for large instances. Computational experiments on real-size liner shipping networks are presented showing that fuels savings in the magnitude 2–10% can be obtained. The work has been carried out in collaboration with Maersk Line and the tests instances are confirmed to be representative of real-life networks.
Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.
Most of the operational problems in container terminals are strongly interconnected. In this paper, we study the integrated Berth Allocation and Quay Crane Assignment Problem in seaport container terminals. We will extend the current state-of-the-art by proposing novel set partitioning models. To improve the performance of the set partitioning formulations, a number of variable reduction techniques are proposed. Furthermore, we analyze the effects of different discretization schemes and the impact of using a time-variant/invariant quay crane allocation policy. Computational experiments show that the proposed models significantly improve the benchmark solutions of the current state-of-art optimal approaches.
This paper presents a detailed BC, NOx and SO2 emission inventory for ships in the Arctic in 2012 based on satellite AIS data, ship engine power functions and technology stratified emission factors. Emission projections are presented for the years 2020, 2030 and 2050. Furthermore, the BC, SO2 and O3 concentrations and the deposition of BC are calculated for 2012 and for two arctic shipping scenarios – with or without arctic diversion routes due to a possible polar sea ice extent in the future.
In 2012, the largest shares of Arctic ships emissions are calculated for fishing ships (45% for BC, 38% for NOx, 23% for SO2) followed by passenger ships (20%, 17%, 25%), tankers (9%, 13%, 15%), general cargo (8%, 11%, 12%) and container ships (5%, 7%, 8%). In 2050, without arctic diversion routes, the total emissions of BC, NOx and SO2 are expected to change by +16%, −32% and −63%, respectively, compared to 2012. The results for fishing ships are the least certain, caused by a less precise engine power – sailing speed relation.
The calculated BC, SO2, and O3 surface concentrations and BC deposition contributions from ships are low as a mean for the whole Arctic in 2012, but locally BC additional contributions reach up to 20% around Iceland, and high additional contributions (100–300%) are calculated in some sea areas for SO2. In 2050, the arctic diversion routes highly influence the calculated surface concentrations and the deposition of BC in the Arctic. During summertime navigation contributions become very visible for BC (>80%) and SO2 (>1000%) along the arctic diversion routes, while the O3 (>10%) and BC deposition (>5%) additional contributions, respectively, get highest over the ocean east of Greenland and in the High Arctic.
The geospatial ship type specific emission results presented in this paper have increased the accuracy of the emission inventories for ships in the Arctic. The methodology can be used to estimate shipping emissions in other regions of the world, and hence may serve as an input for other researchers and policy makers working in this field.
This paper evaluates the influence of forecast horizon and observation fit on the robustness and performance of a specific freight rate forecast model used in the liner shipping industry. In the first stage of the research, a forecast model used to predict container freight rate development is presented by exploring the relationship between individual company’s rates and aggregated market rates, and thus assists in dealing with uncertainty and market volatility for a given business situation. In the second stage, a design of experiment approach is applied to highlight the influence of the forecast horizon and observation fit and their interactions on the forecast model’s performance. The results underline the complicated nature of creating a suitable forecast model by balancing business needs, a desire to fit a good model and achieve high accuracy. There is strong empirical evidence from this study; that a robust model is preferable, that overfitting is a true danger, and that a balance must be achieved between forecast horizon and the number of observations used to fit the model. In addition, methodological guidance has also been provided on how to test, design, and choose the superior model for business needs.
Different procedures for estimation of the extreme global wave hydroelastic responses in ships are discussed. Firstly, stochastic procedures for application in detailed numerical studies (CFD) are outlined. The use of the First Order Reliability Method (FORM) to generate critical wave episodes of short duration, less than 1 minute, with prescribed probability content is discussed for use in extreme response predictions including hydroelastic behaviour and slamming load events. The possibility of combining FORM results with Monte Carlo simulations is discussed for faster but still very accurate estimation of extreme responses. Secondly, stochastic procedures using measured time series of responses as input are considered. The Peak-over-Threshold procedure and the Weibull fitting are applied and discussed for the extreme value predictions including possible corrections for clustering effects.
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.