Speed optimization of a container ship on a given route considering voluntary speed loss and emissions

Li, Xiaohe; Sun, Baozhi; Guo, Chunyu; Du, Wei; Li, Yanjun

doi:10.1016/j.apor.2019.101995

Cited by 83 publications

(36 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They develop a bi-objective optimization model to minimize fuel consumption and maximize operating cost reduction simultaneously. Li et al [41] extend the work of Li et al [40] by considering voluntary speed loss and GHG emissions. In order to address a ship sailing speed and trim optimization problem over a voyage, Du et al [14] propose three viable countermeasures within an effective two-phase optimal solution framework.…”

Section: Literature Reviewmentioning

confidence: 89%

Ship Speed Optimization Considering Ocean Currents to Enhance Environmental Sustainability in Maritime Shipping

et al. 2020

View full text Add to dashboard Cite

Enhancing environmental sustainability in maritime shipping has emerged as an important topic for both firms in shipping-related industries and policy makers. Speed optimization has been proven to be one of the most effective operational measures to achieve this goal, as fuel consumption and greenhouse gas (GHG) emissions of a ship are very sensitive to its sailing speed. Existing research on ship speed optimization does not differentiate speed through water (STW) from speed over ground (SOG) when formulating the fuel consumption function and the sailing time function. Aiming to fill this research gap, we propose a speed optimization model for a fixed ship route to minimize the total fuel consumption over the whole voyage, in which the influence of ocean currents is taken into account. As the difference between STW and SOG is mainly due to ocean currents, the proposed model is capable of distinguishing STW from SOG. Thus, in the proposed model, the ship’s fuel consumption and sailing time can be determined with the correct speed. A case study on a real voyage for an oil products tanker shows that: (a) the average relative error between the estimated SOG and the measured SOG can be reduced from 4.75% to 1.36% across sailing segments, if the influence of ocean currents is taken into account, and (b) the proposed model can enable the selected oil products tanker to save 2.20% of bunker fuel and reduce 26.12 MT of CO2 emissions for a 280-h voyage. The proposed model can be used as a practical and robust decision support tool for voyage planners/managers to reduce the fuel consumption and GHG emissions of a ship.

show abstract

Section: Literature Reviewmentioning

confidence: 89%

Ship Speed Optimization Considering Ocean Currents to Enhance Environmental Sustainability in Maritime Shipping

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Instead, Wong et al [54] developed two continuous utility-based decision support models for ship liners sustainability that need to determine the optimal speed for slow steaming operations. Focusing on involuntary and voluntary speed loss, Li et al [55] developed a speed optimization model that considers a single container ship, which can help make reasonable speed optimization decisions and improve ship operations efficiency. Other works proposed different approaches to deal with a vessel facing harsh weather conditions during its journey.…”

Section: Speed Optimization Problemsmentioning

confidence: 99%

Smart Steaming: A New Flexible Paradigm for Synchromodal Logistics

2021

View full text Add to dashboard Cite

Slow steaming, i.e., the possibility to ship vessels at a significantly slower speed than their nominal one, has been widely studied and implemented to improve the sustainability of long-haul supply chains. However, to create an efficient symbiosis with the paradigm of synchromodality, an evolution of slow steaming called smart steaming is introduced. Smart steaming is about defining a medium speed execution of shipping movements and the real-time adjustment (acceleration and deceleration) of traveling speeds to pursue the entire logistic system’s overall efficiency and sustainability. For instance, congestion in handling facilities (intermodal hubs, ports, and rail stations) is often caused by the common wish to arrive as soon as possible. Therefore, smart steaming would help avoid bottlenecks, allowing better synchronization and decreasing waiting time at ports or handling facilities. This work aims to discuss the strict relationships between smart steaming and synchromodality and show the potential impact of moving from slow steaming to smart steaming in terms of sustainability and efficiency. Moreover, we will propose an analysis considering the pros, cons, opportunities, and risks of managing operations under this new policy.

show abstract

“…However, they only studied part of the voyage, and the data came from test ships. Li et al [11] used Kwon's method to estimate involuntary speed loss, and established ship speed optimizations with and without voluntary speed loss for a single voyage. However, the speed studied in [11] refers to the ship speed in still water.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [11] used Kwon's method to estimate involuntary speed loss, and established ship speed optimizations with and without voluntary speed loss for a single voyage. However, the speed studied in [11] refers to the ship speed in still water. In summary, the published formulas and methods have helped the researchers achieve certain research results.…”

Section: Introductionmentioning

confidence: 99%

A Practical Estimation Method of Inland Ship Speed Under Complex and Changeful Navigation Environment

et al. 2021

View full text Add to dashboard Cite

The complexity and changefulness of inland navigation environment in space and time makes it hard to guarantee the applicability and accuracy of existing ship speed models. In this paper, a novel method for inland ship speed modelling under complex and changeful navigation environment is proposed. Firstly, an unsupervised machine learning algorithm, Density-Based Spatial Clustering of Application with Noise (DBSCAN), is utilized to cluster the environmental data including water level, water speed, wind speed and wind direction, to get the segment division information, which greatly helps reduce the influence of other uncertain environmental factors on the speed model. Then, Generalized Regression Neural Network (GRNN) is tailored and employed to build the ship speed estimation model with multiple input variables. Finally, a detailed case study of a ship sailing in the Yangtze River trunk line is conducted to validate the proposed methods. The results show that the ship speed model established based on machine learning methods works effectively in speed estimation and analysis. Moreover, compared with other regression methods and neural networks, the proposed GRNN model has the best performance in ship speed modelling.INDEX TERMS Complex navigation environment, inland ship, speed modeling, DBSCAN, GRNN.

show abstract

Speed optimization of a container ship on a given route considering voluntary speed loss and emissions

Cited by 83 publications

References 19 publications

Ship Speed Optimization Considering Ocean Currents to Enhance Environmental Sustainability in Maritime Shipping

Ship Speed Optimization Considering Ocean Currents to Enhance Environmental Sustainability in Maritime Shipping

Smart Steaming: A New Flexible Paradigm for Synchromodal Logistics

A Practical Estimation Method of Inland Ship Speed Under Complex and Changeful Navigation Environment

Contact Info

Product

Resources

About