Integrated maritime fuel management with stochastic fuel prices and new emission regulations

Gu, Yewen; Wallace, Stein W.; Wang, Xin

doi:10.1080/01605682.2017.1415649

Cited by 19 publications

(8 citation statements)

References 30 publications

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“…Obviously, the set is actually a sample from an underlying unknown distribution (or process), but it is beyond the goals of this paper to discuss the relationship between this empirical distribution and the true one. In this way we are in line with the literature (Yang and Zhou 2014, Zockaie et al 2016, Gu et al 2019.…”

Section: Scenarios Generationsupporting

confidence: 92%

On scenario construction for stochastic shortest path problems in real road networks

Zhang

Wallace

Guo

et al. 2020

Preprint

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Stochastic shortest path computations are often performed under very strict time constraints, so computational efficiency is critical. A major determinant for the CPU time is the number of scenarios used. We demonstrate that by carefully picking the right scenario generation method for finding scenarios, the quality of the computations can be improved substantially over random sampling for a given number of scenarios. We study a real case from a California freeway network with 438 road links and 24 5-minute time periods, implying 10,512 random speed variables, correlated in time and space, leading to a total of 55,245,816 distinct correlations. We find that (1) the scenario generation method generates unbiased scenarios and strongly outperforms random sampling in terms of stability (i.e., relative difference and variance) whichever origin-destination pair and objective function is used; (2) to achieve a certain accuracy, the number of scenarios required for scenario generation is much lower than that for random sampling, typically about 6-10 times lower for a stability level of 1%; and (3) different origin-destination pairs and different objective functions could require different numbers of scenarios to achieve a specified stability.

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Section: Scenarios Generationsupporting

confidence: 92%

On scenario construction for stochastic shortest path problems in real road networks

Zhang

Wallace

Guo

et al. 2020

Preprint

Self Cite

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“…Furthermore, carbon taxes and trading schemes for greenhouse gas emission are being discussed and implemented on the market regulation side [4]. Shipping companies are also developing associated procedures [5], and management plans to maintain international competitiveness and reduce emissions by reducing fuel consumption, which accounts for nearly 50-60% of the total operating expenses [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Development of a Fuel Consumption Prediction Model Based on Machine Learning Using Ship In-Service Data

Kim

Jung

Park

2021

JMSE

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As interest in eco-friendly ships increases, methods for status monitoring and forecasting using in-service data from ships are being developed. Models for predicting the energy efficiency of a ship in real time need to effectively process the operational data and be optimized for such an application. This paper presents models that can predict fuel consumption using in-service data collected from a 13,000 TEU class container ship, along with statistical and domain-knowledge methods to select the proper input variables for the models. These methods prevent overfitting and multicollinearity while providing practical applicability. To implement the prediction model, either an artificial neural network (ANN) or multiple linear regression (MLR) were applied, where the ANN-based models showed the best prediction accuracy for both variable selection methods. The goodness of fit of the models based on ANN ranged from 0.9709 to 0.9936. Furthermore, sensitivity analysis of the draught under normal operating conditions indicated an optimal draught of 14.79 m, which was very close to the design draught of the target ship, and provides the optimal fuel consumption efficiency. These models could provide valuable information for ship operators to support decision making to maintain efficient operating conditions.

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“…Hence, rather than one universal speed, the shipping company now needs to make two separate speed decisions for a single voyage which crosses the border of the METS areas. Similar applications of speed differentiation can also be found in the cases of the Emission Control Area in e.g., Fagerholt et al (2015), Gu and Wallace (2017) and Gu et al (2018b). The shipping company's aim is to minimize its total cost from both tactical and operational levels.…”

Section: Problem Description and Assumptionsmentioning

confidence: 83%

Can an Emission Trading Scheme really reduce CO2 emissions in the short term? Evidence from a maritime fleet composition and deployment model

Wallace

Wang

2019

Transportation Research Part D: Transport and Environment

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Global warming has become one of the most popular topics on this planet in the past decades, since it is the challenge that needs the efforts from the whole mankind. Maritime transportation, which carries more than 90% of the global trade, plays a critical role in the contribution of green house gases (GHGs) emission. Unfortunately, the GHGs emitted by the global fleet still falls outside the emission reduction scheme established by the Kyoto Protocol. Alternative solutions are therefore strongly desired. Several marketbased measures are proposed and submitted to IMO for discussion and evaluation. In this paper, we choose to focus on one of these measures, namely Maritime Emissions Trading Scheme (METS). An optimization model integrating the classical fleet composition and deployment problem with the application of ETS (global or regional) is proposed. This model is used as a tool to study the actual impact of METS on fleet operation and corresponding CO 2 emission. The results of the computational study suggest that in the short term the implementation of METS may not guarantee further emission reduction in certain scenarios. However, in other scenarios with low bunker price, high allowance cost or global METS coverage, a more significant CO 2 decrease in the short term can be expected.Recently, the voice for implementing a market-based measure (MBM) in the shipping sector has become strong. An MBM addresses the negative externalities of a market, such as pollution, through market mechanisms. Different from command-and-control measures (e.g., legislations), a MBM offers economic incentives rather than fixed rules to achieve a more cost-effective and sustainable pollution control (Miola et al., 2011). In terms of shipping, an MBM can help to internalise the external costs of the fleet emission by making the ship owner or operator pay for the CO 2 emitted from their ships, which finally creates an incentive for them to cut the emissions (Psaraftis, 2012).Although seven MBM proposals have been submitted to the IMO for discussion and evaluation, it seems that only two of them, namely the Emissions Trading Scheme (ETS) and the GHG Fund, are favoured and widely studied in the literature (Shi, 2016). The ETS is a type of cap-and-trade system. In such a system, first a legally binding limit on total emissions (cap) during a certain period is set by the authority. Then the equivalent amount of emission allowances are issued by the administration and assigned to or purchased by different concerned parties. In the system, participants can choose to submit the allowance for their own emissions or trade with other players for cash based on the market price of the allowance. However, if all the issued allowances are consumed, no more emissions will be allowed due to the legal limit. Such a market-based cap-and-trade system will help achieve the emission reduction target with certainty as well as minimal costs. However, the uncertainty of the allowance price may cause problems, for example cost volatility and investment risks ...

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Integrated maritime fuel management with stochastic fuel prices and new emission regulations

Cited by 19 publications

References 30 publications

On scenario construction for stochastic shortest path problems in real road networks

On scenario construction for stochastic shortest path problems in real road networks

Development of a Fuel Consumption Prediction Model Based on Machine Learning Using Ship In-Service Data

Can an Emission Trading Scheme really reduce CO2 emissions in the short term? Evidence from a maritime fleet composition and deployment model

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