Using Improved Entropy‐Cloud Model to Select High‐Speed Railway Express Freight Train Service Sites

Huang, Wencheng; Shen, Bin

doi:10.1155/2017/7824835

Cited by 10 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HSR has a large capacity, high punctuality and high safety with low energy consumption for operation. Huang and Shuai [3] developed a model for the selection of HSR station as hubs for express freight transportation. They considered a number of influencing factors such as GDP, population and number of large enterprises.…”

Section: Literature Reviewmentioning

confidence: 99%

Freight transport using high-speed railways

Watson¹,

Ali²,

Bayyati³

2019

Int. J. TDI

View full text Add to dashboard Cite

There has been an encouraging awareness of the need for more sustainable transportation particularly for freight transport that has resulted from the continuing increases in global trade and business internationalisation. This is mainly driven by the related issues concerning greenhouse gas emissions and dependency on fossil fuel. The expansion in the worldwide high-speed railway (HSR) systems has provided a reliable opportunity for freight transportation. The HSR has the ability to move goods in a more efficient, safe and cost-effective way, which can improve trade links in Europe and the rest of the world. Many countries have developed HSR systems, but such systems are mostly used for passenger transport despite their haulage capacity. This research investigates the future possibility of shifting freight from airlines onto HSR, by analysing the operational and technical constraints associated with freight traffic. Most of the data were gathered from online sources and related research publications concerning a selected set of worldwide HSR systems. The outcome of this research may contribute to the development of suitable solutions that may improve the economic, environmental and social sustainability of transport systems.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Freight transport using high-speed railways

Watson¹,

Ali²,

Bayyati³

2019

Int. J. TDI

View full text Add to dashboard Cite

show abstract

“…e rapid development of the Chinese economy is mirrored by the burgeoning freight transport and logistics. Internet technology is being integrated with data mining, resulting in various transport projects and platforms that synthetize social information with the diverse big data on freight services and serve the enterprises with freight demand [1][2][3][4][5][6][7][8]. Currently, long-distance freight transport is shifting towards multimodal transport, the combination of multiple freight transport modes [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Path Design and Planning and Investment and Construction Mode of Multimodal Transport Network Based on Big Data Analysis

Wang

2022

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

Currently, long-distance freight transport is shifting towards multimodal transport, the combination of multiple freight transport modes. Multimodal transport enables enterprises with the same logistics function to operate on the same level of the supply chain. Through horizontal cooperation, these enterprises can give play to their advantages, make up their deficiencies, improve service levels, reduce cost input, and thereby enhance market status. Therefore, multimodal transport is an intensive development model that promotes the alliance between giants. The reasonable path design and planning (PDP) and investment and construction mode (ICM) of the multimodal transport network help freight demanders, as well as multimodal freight transport platforms, obtain the maximum profit. Therefore, this paper explores the PDP and ICM of the multimodal transport network based on big data analysis. Firstly, the influencing factors and behavioral features of multimodal transport were deeply examined, drawing on the logit model and the big data on multiple freight services, namely, railway transport, highway transport, waterway transport, and airway transport. After classifying the freights, the authors analyzed the modeling and decision-making of path design and optimization (PDO) for multimodal transport network. The proposed model was proved effective through experiments. This paper theoretically explores the goals, principles, and needs of path selection in the modern transportation industry. In a realistic sense, the research findings help decision-makers optimize their decisions on the multimodal transport network and operate the network at the minimum transport cost.

show abstract

“…However, the accuracy and operability of PFDMOEAMs established in the two studies are not high due to the poor objectivity and insufficient convenience of the MADM methods mentioned above. Compared with fuzzy AHP, fuzzy integral and other subjective evaluation methods, the entropy weight and technique for order preference by similarity to ideal solution (entropy-TOPSIS) method is more objective and accurate, because its multi-objective evaluation in the OSS module comes entirely from the mining of the inherent laws of historical data, without involving subjective reasoning [15]. In addition, it is simple in principle,…”

mentioning

confidence: 99%

A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2

et al. 2019

View full text Add to dashboard Cite

As a Kansei engineering design expert system, the product form design multi-objective evolutionary algorithm model (PFDMOEAM) contains various methods. Among them, the multi-objective evolutionary algorithm (MOEA) is the key to determine the performance of the model. Due to the deficiency of MOEA, the traditional PFDMOEAM has limited innovation and application value for designers. In this paper, we propose a novel PFDMOEAM with an improved strength Pareto evolutionary algorithm 2 (ISPEA2) as the core and combining the elliptic Fourier analysis (EFA) and the entropy weight and technique for order preference by similarity to ideal solution (entropy-TOPSIS) methods. Based on the improvement of the original operators in SPEA2 and the introduction of a new operator, ISPEA2 outperforms SPEA2 in convergence and diversity simultaneously. The proposed model takes full advantage of this superiority, and further combines the EFA method's high accuracy and degree of multi-method integration, as well as the entropy-TOPSIS method's good objectivity and operability, so it has excellent comprehensive performance and innovative application value. The feasibility and effectiveness of the model are verified by a case study of a car form design. The simulation system of the model is developed, and the simulation results demonstrate that the model can provide a universal and effective tool for designers to carry out multi-objective evolutionary design of product form. Appl. Sci. 2019, 9, 2944 2 of 26 apply aggregation on the objectives to transform multiple objectives into a single objective rather than directly using MOEA in the MOO module [4]. Hsiao and Tsai [5] integrated the multiple objectives into a single value by using the linear weighting method, and then adopted a genetic algorithm (GA) to get the optimal product form based on the prediction model constructed by a fuzzy neural network (FNN). Guo et al. [6] also integrated multiple objectives into a single objective by using the linear weighting method, and obtained the optimal design by employing GA on the basis of the prediction model established by using a back propagation neural network (BPNN). This kind of study is simple and easy to implement, but because it limits the search space and excludes the consideration of all possible solutions, it has limited practical value for designers and consumers [7]. The second kind of study is to use MOEA directly in the MOO module. Although the application is more complicated, it has become a mainstream trend because it can better meet the performance requirements of PFDMOEAM. Yang [8] proposed a PFDMOEAM with a non-dominated sorting genetic algorithm II (NSGA-II) as MOEA in the MOO module, and used support vector regression (SVR) to provide multi-objective fitness functions required by NSGA-II. Su et al. [9] also constructed a PFDMOEAM with NSGA-II as MOEA. The difference is that fuzzy neural network (FNN) is used to provide fitness functions for NSGA II. Shieh et al.[10] also adopted NSGA-II as MOEA and combined with quantificat...

show abstract

Using Improved Entropy‐Cloud Model to Select High‐Speed Railway Express Freight Train Service Sites

Cited by 10 publications

References 44 publications

Freight transport using high-speed railways

Freight transport using high-speed railways

Path Design and Planning and Investment and Construction Mode of Multimodal Transport Network Based on Big Data Analysis

A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2

Contact Info

Product

Resources

About