Benefit evaluation for ocean engineering based on grey Euclid model of game compromise

Tu, Yuqiang

doi:10.1007/s10586-018-2230-z

Cited by 2 publications

(6 citation statements)

References 10 publications

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“…It is also applied to the comprehensive evaluation of investment projects after the correlation with economic factors, which provides a strong basis for the decision-making of environmental investment [8]. The grey Euclid model of game compromise is used by Tu (2018) to evaluate the building and analysis of marine engineering item of a construction company, and the risk factors of project personnel management and environment are analyzed [9]. It shows that more and more experts and scholars have carried out evaluation research on investment projects and proposed more evaluation models, but the research on benefit prediction model of investment projects is still in the exploratory stage.…”

Section: Evaluation Of Invested Projectmentioning

confidence: 99%

Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects

Zhong

Xiao-ling

2020

PLoS ONE

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To determine the influence of the weight of the economic effectiveness evaluation criteria of the major investments of listed enterprises, and provide new management ideas for the development of the follow-up enterprises, firstly, the financial benefit evaluation system of investment projects is analyzed and constructed, and the specific evaluation process is analyzed. Then, on this basis, the evaluation index is refined; the basic structure of BP neural network (BPNN) is introduced, and genetic algorithm is used to improve BP neural network. The cost-benefit analysis model is constructed based on the improved BPNN. The listed company A is taken as an example to analyze its development data in recent years, and then the data of 10 listed companies are taken as the research object. Matlab simulation software is used to train and verify the improved BPNN model, analyze and predict the weight value of the financial benefit index of the investment projects of these 10 companies, and then determine the index to improve the financial benefit of the investment projects. Under the analysis of the development data of listed company A in the past 10 years, it is found that the indicators of the listed company's profitability per share, debt risk operation ability, development, and growth ability in the past 10 years are in relatively stable state. The principal component analysis of its 20 secondary sub-indexes is conducted based on the four primary indicators: profitability, debt risk, operational capacity, and development and growth. A total of eight principal components including return on equity (ROE), return on assets (ROA), (total asset turnover) TATO, turnover of account receivable (AR), asset-liability ratio, interest protection multiple, income growth rate, and year-on-year rate of increase for complete assets are extracted. The average error between the final output value, the actual value, and the expected value is 0.0304 and 0.0169, respectively. The weight coefficient of the monetary benefits evaluation indicator of investment items is calculated, and the computed results show that year-on-year rate of increase for complete assets, TOTA, ROA, turnover of total capital, and ROE are important indexes in the financial benefit evaluation of investment projects. It indicates that to improve the financial benefit of investment projects of listed enterprises, it is necessary to enhance the year-over-year growth degree of total properties and ROA.

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Section: Evaluation Of Invested Projectmentioning

confidence: 99%

Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects

Zhong

Xiao-ling

2020

PLoS ONE

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“…Zhao et al [37] Masinia et al [38] Salo et al [39] Jin et al [40] Prostean et al [41] Balks et al [42] Lin Yanshuo [43] Gatzer t et al [44] Rolik et al [45] Dimitrios et al [46] Xu et al [47] Tu et al [48] Frq Policy risk…”

Section: Snmentioning

confidence: 99%

“…Technology development risk Lin Yanshuo [43] Gatzer t et al [44] Rolik et al [45] Dimitrios et al [46] Xu et al [47] Tu et al [48] Frq Lin Yanshuo [43] Gatzer t et al [44] Rolik et al [45] Dimitrios et al [46] Xu et al [47] Tu et al [48] Frq Lin Yanshuo [43] Gatzer t et al [44] Rolik et al [45] Dimitrios et al [46] Xu et al [47] Tu et al [48] Frq Lin Yanshuo [43] Gatzer t et al [44] Rolik et al [45] Dimitrios et al [46] Xu et al [47] Tu et al [48] Frq Lin Yanshuo [43] Gatzer t et al [44] Rolik et al [45] Dimitrios et al [46] Xu et al [47] Tu et al [48] Frq Lin Yanshuo [43] Gatzer t et al [44] Rolik et al [45] Dimitrios et al [46] e [ Lin Yanshuo [43] Gatzer t et al [44] Rolik et al [45] Dimitrios et al [46] Xu et al [47] Tu et al [48] Frq Lin Yanshuo [43] Gatzer t et al [44] Rolik et al [45] Dimitrios et al [46] Xu et al…”

Section: Snmentioning

confidence: 99%

“…Technology development risk Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Rolik et al [45] Dimitrios et al [46] e [ Tu et al [48] Frq Rolik et al [45] Dimitrios et al [46] e [ Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Rolik et al [45] Dimitrios et al [46] e [ Lin Yanshuo [43] Gatzer t et al [44] Rolik et al [45] Dimitrios et al [46] Xu et al [47] Tu et al [48] Frq Rolik et al [45] Dimitrios et al [46] Xu et al [47] Tu et al [48] Frq Rolik et al [45] Dimitrios et al [46] Xu et al [47] Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq…”

Section: Snmentioning

confidence: 99%

“…Supply chain risk Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Tu et al [48] Frq Rolik et al [45] Dimitrios et al [46] Xu et al [47] e [ Tu et al [48] Frq…”

Section: Snmentioning

confidence: 99%

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Investment Evaluation and Partnership Selection Model in the Offshore Wind Power Underwater Foundations Industry

Cheng

2021

JMSE

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With a plan to achieve a target of 5.7 GW offshore wind power capacity in 2025, Taiwan anticipates building a 36-billion USD industry, which makes Taiwan a center of attention in the global marketplace of civil engineering construction. Aimed at Taiwan’s underwater foundations industries, this study is the first to develop an investment evaluation model (IEM) by applying FPR to obtain risk factor weights and calculate the overall investment risk value with a numerical scoring method. In a context where no precedent exists for reference, this study provides auxiliary and supportive tools to help builders to make the decision, based on objective indicators, whether to undertake an investment. To date, no research has been conducted to introduce a reasonable mathematical model that discusses the issue of partner selection in the field of offshore wind power. This study is the first paper to construct a SWARA-FTOPSIS partner selection model, which enables underwater foundations builders to take specific Taiwanese characteristics into account in their selection of the best partners to meet transportation, construction, and installation requirements. Finally, the study uses the case of the Taipower Offshore Wind Power Project (2nd phase) to verify the feasibility of this model.

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Benefit evaluation for ocean engineering based on grey Euclid model of game compromise

Cited by 2 publications

References 10 publications

Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects

Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects

Investment Evaluation and Partnership Selection Model in the Offshore Wind Power Underwater Foundations Industry

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