Emission charge and liner shipping network configuration ‐ an economic investigation of the Asia‐Europe route

This paper models shipping lines’ operational costs and CO2 emissions under alternative geographic network configurations when an emission charge is imposed on operations from Asia to Europe. Our modeling results suggest that shipping firms’ network configuration is influenced by emission charge, fuel price, port loading and unloading cost, and demand pattern of cargo transport across different markets. Total emission will be reduced by an EU emission charge scheme. However, if the charge is above a threshold, carriers will reconfigure shipping networks to minimize their costs including emission charge payments. This will offset part of the emission reduction achieved by the emission scheme. As a result, a higher charge does not always lead to a higher emission reduction. In addition, the performance of major ports along the Asia-Europe routes will be influenced in different ways, leading to conflicting views from regional governments. These findings reveal possible market distortions associated with regional emission systems, and highlight the complex effects of international environmental policies when market dynamics are considered

Influential Article Review - Examining the Bunkering Choice Determinants The Case of the Port of Antwerp

This paper examines transportation. We present insights from a highly influential paper. Here are the highlights from this paper: From a European, regional and local perspective, as well as from the perspective of port authorities, it is important that waterborne transport becomes sustainable. As possible solutions to comply with new types of legislation (SECA-zones), shipping companies consider amongst others the use of liquefied natural gas (LNG) and low sulphur fuel. An important aspect in the choice of fuel are the current bunker strategies of the shipping companies. Therefore, this research deals with the bunker market and wants to increase the insight into the strategy of the shipping companies, why they bunker in Antwerp or in another port (e.g. Rotterdam). Which criteria are the most important: the price per tonne, the quality of the fuel, or another characteristic (e.g. calling pattern)?The research question is answered with a discrete choice experiment, evaluating the preferences of the shipping lines. A multinomial logit model is chosen for this experiment because of the low expected number of respondents. The research is further expanded with more in-depth interviews with bunkering decision makers of various shipping companies. For our overseas readers, we then present the insights from this paper in Spanish, French, Portuguese, and German

Bunker Consumption Optimization in Liner Shipping: A Metaheuristic Approach

Taking into account increasing volumes of the international seaborne trade, liner shipping companies have to ensure efficiency of their operations in order to remain competitive. The bunker consumption cost constitutes a substantial portion of the total vessel operating cost and directly affects revenues of liner shipping companies. “Slow steaming” became a common strategy among ocean carriers to decrease vessel sailing speeds and reduce bunker consumption costs. However, decreasing vessel sailing speeds may require deployment of more vessels on a given shipping route to provide the agreed service frequency at each port of call. Several bunker consumption optimization methods were developed in the past to capture those conflicting decisions. This paper describes existing bunker consumption optimization methods, outlines their drawbacks, and proposes a new metaheuristic approach. Numerical experiments demonstrate efficiency of the suggested metaheuristic in terms of solution quality and computational time. DOI: 10.17762/ijritcc2321-8169.15065

An artificial neural network based decision support system for energy efficient ship operations

Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data -‘Noon Data’ - which provides information on a ship’s daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects

Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization

Ship routing and scheduling problem is considered to meet the demand for various products in multiple ports within the planning horizon. The ports have restricted operating time, so multiple time windows are taken into account. The problem addresses the operational measures such as speed optimisation and slow steaming for reducing carbon emission. A Mixed Integer Non-Linear Programming (MINLP) model is presented and it includes the issues pertaining to multiple time horizons, sustainability aspects and varying demand and supply at various ports. The formulation incorporates several real time constraints addressing the multiple time window, varying supply and demand, carbon emission, etc. that conceive a way to represent several complicating scenarios experienced in maritime transportation. Owing to the inherent complexity, such a problem is considered to be NP-Hard in nature and for solutions an effective meta-heuristics named Particle Swarm Optimization-Composite Particle (PSO-CP) is employed. Results obtained from PSO-CP are compared using PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to prove its superiority. Addition of sustainability constraints leads to a 4–10% variation in the total cost. Results suggest that the carbon emission, fuel cost and fuel consumption constraints can be comfortably added to the mathematical model for encapsulating the sustainability dimensions

Optimization in container liner shipping

We will give an overview of several decision problem encountered in liner shipping. We will cover problems on the strategic, tactical and operational planning levels as well as problems that can be considered at two planning levels simultaneously. Furthermore, we will shortly discuss some related problems in terminals, geographical bottlenecks for container ships and provide an overview of operations research methods used in liner shipping problems. Thereafter, the decision problems will be illustrated using a case study for six Indonesian ports