A heuristic algorithm based on leaf photosynthate transport

Yu, Bin; Shan, Weiwei; Guo, Zeng-Yuan; Wang, Yunpeng

doi:10.1177/0037549717733063

Cited by 15 publications

(9 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yu et al (2015) developed a bi-level programming model to solve the design problem for bus line distribution in multi-modal transport networks. Traffic equilibrium can also be found in lots of literature (Bin et al, 2017;Wardrop and Whitehead, 1952a,b;Yang et al, 2012;Zhang and Yang, 2015;Yildirimoglu et al, 2015;Chen et al, 2017;Zhang et al, 2017).…”

Section: Literature Reviewsmentioning

confidence: 88%

Allocation method for transit lines considering the user equilibrium for operators

Yao

Chen

Zhang

et al. 2019

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

show abstract

Section: Literature Reviewsmentioning

confidence: 88%

Allocation method for transit lines considering the user equilibrium for operators

Yao

Chen

Zhang

et al. 2019

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

show abstract

“…The metric is used as input data to train the network respectively. [21][22][23] The instance verification…”

Section: Machine-learning Approachmentioning

confidence: 99%

Prediction on soil-ground vibration induced by high-speed moving train based on artificial neural network model

Fang

Yao

Xia

2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Vibration prediction plays a vital role in the environmental assessment of new railway lines and the vibration analysis of new buildings near railway lines. We use theoretical analysis, numerical simulation, and field experiment, which can help predict vibration with high accuracy. These methods have their own advantages, but are time-consuming or costlier. This article outlines a fast, high-accuracy prediction method combining the advantages of numerical simulation, experiment, and artificial neural network. The method takes artificial neural network as one of the supervised machine learnings, and it is founded upon a fully validated three-dimensional finite element model. First, establish a three-dimensional finite element model using ANSYS software and generate series of samples by changing field conditions. Then, create an artificial neural network and train it by the samples obtained from FEM and experiment. Finally, use the artificial neural network that has been trained to predict the level of vibration on certain conditions. The result states that the predicted method can control the maximum error below 6.41% and the average error below 2.29% when it is used to predict acceleration vibration level. An advantage of this method is that it spends zero time on predicting vibration level. A key benefit from this decreased prediction time is that it potentially reduces the detailed vibration analyses required for a new train line so that it can reduce project costs. In addition, it could also consider the effect of soil parameters.

show abstract

“…A series of methods were proposed. [3][4][5][6][7][8] Yang et al 9 introduced a new method in 2014. Some effective methods on route choice and operation area can be found in these literatures.…”

Section: Literature Reviewmentioning

confidence: 99%

Fleet route selection prediction problem based on support vector machine

Guan

Shen²,

et al. 2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Bulk shipping transport is an important part of ocean transportation. One of the most important aspects for bulk ship owners is to make choices about the ship's operational area for the next planning period. In this article, based on the Baltic Supermax Index and historical decision data of different companies, the support vector machine model is used to predict the dry bulk carrier route selection, which provides a solution to this problem. The numerical results show that the model and the algorithm proposed in the paper can well work and can achieve good precision. Via comparative analysis, we prove that the model we proposed in this article has better performance than some other common methods in the research area. The proposed model would support dry bulk shipping company in route choice so as to balance the profit and potential opportunity when making choice for ships' route selection.

show abstract

A heuristic algorithm based on leaf photosynthate transport

Cited by 15 publications

References 50 publications

Allocation method for transit lines considering the user equilibrium for operators

Allocation method for transit lines considering the user equilibrium for operators

Prediction on soil-ground vibration induced by high-speed moving train based on artificial neural network model

Fleet route selection prediction problem based on support vector machine

Contact Info

Product

Resources

About