A Novel Method for Evaluating Dredging Productivity Using a Data Envelopment Analysis‐Based Technique

Lai, Hsin-Hung; Chang, Kuei-Hu; Lin, Chien‐Liang

doi:10.1155/2019/5130835

Cited by 7 publications

(7 citation statements)

References 21 publications

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“…Charnes et al [12] initially introduced the DEA method as a mathematical programming method that can evaluate the relative efficiency of decision-making units (DMUs) (first-mode CCR model). DEA is a method that uses multiple inputs to produce multiple outputs to measure the relative efficiencies of a group of DMUs.…”

Section: Dea Methodsmentioning

confidence: 99%

“…On the other hand, the integrated fuzzy data envelopment analysis (FDEA) and fuzzy-multi attribute decision-making (F-MADM) were used to evaluate and select the safest airlines in Iran [11]. Some scholars used the novel DEA-based method to evaluate the dredging productivity of the national army and found that it can effectively address the complicated MADM problem of dredging productivity [12]. A complete picture of major airlines' operations can be achieved by exhaustively examining their efficiency in European airspace using the novel input/output parameters of the Data Envelope Analysis (DEA) [13].…”

Section: Introductionmentioning

confidence: 99%

“…Section 3 proposes a novel productivity evaluation method based on novel integration and DEA. Section 4 adapts the data from the case of Lai (2019) [12] to verify the effectiveness of the proposed method. Lastly, Section 5 provides conclusions and future research directions.…”

Section: Introductionmentioning

confidence: 99%

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Combining Novel Integration and the DEA Technique to Compute Dredging Productivity

Lai¹,

Hsieh²

2023

Preprint

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Construction productivity entails a wide range of work combinations. When human resources are scarce, it is critical to replace manpower with machinery, and calculating machinery productivity is crucial. Traditional labor productivity methods, however, cannot address dredging complex multi-attribute decision-making (MADM) problems. Aiming to address the limitations of the traditional labor productivity method, this paper extends the data envelopment analysis (DEA) and proposes a new dredging productivity evaluation method. The proposed method can solve the problem of evaluating various combinations of factors (single-input, multiple-input, sin-gle-output, and multiple-output) and the problem suggesting that the efficiency of the DEA method'scalculation results is equal to 1. The effectiveness of the proposed method was verified using reservoir dredging examples. The simulation results show that the proposed method has broad applicability, can accurately evaluate the related dredging performance issues, and provide directions for construction managers to improve labor productivity.

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Section: Dea Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combining Novel Integration and the DEA Technique to Compute Dredging Productivity

Lai¹,

Hsieh²

2023

Preprint

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“…e fuzzy-set-based methods and data envelopment analysis are commonly used approaches to solve complex MADM problems, such as [1]. us, many authors used a fuzzy-set-based approach to handle supply-chain-related issues under fuzzy information environments [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Enhanced Supplier Selection Method Used for Handling Hesitant Fuzzy Linguistic Information

Chang

2022

Mathematical Problems in Engineering

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Today’s competitive businesses have been shifted from the company-to-company competition model to the supply chain-to-supply chain competition model. The selection of the most suitable supplier determines customer satisfaction and enterprise competitive advantage. However, the typical supplier selection approaches did not consider the ordered weights between the evaluations of attribute values, resulting in distorted assessment result. Moreover, experts often uncertainly decide the exact value of the evaluation attribute’s rating, have linguistic term sets equivocation, or give ambiguous information, which increase the difficulty of the supplier evaluation process. To deal with the aforementioned problem, we have proposed a novel enhanced supplier selection method for handling hesitant fuzzy linguistic information. To verify the approach, by taking network security system assessment as an example to explain the use of the proposed novel enhanced supplier selection method, the calculation result is compared with the result of the arithmetic average and symbolic methods. The results show that the proposed novel enhanced supplier selection method is more accurate and reasonable and can better reflect real situations.

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“…Due to the developed sensor technology, more operational data have been gained for analyzing dredging performance. In the literature review, machine learning methods were recently adopted to model the complex and dynamical construction process of CSD (cutter suction dredger) for their excellent learning and mining ability [8]. Generally, the learning-based prediction models can be mainly divided into two main types based on the depth of structure: shallow learning models and deep learning models [9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers

et al. 2021

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Dredging is a basic construction for waterway improvement, harbor basin maintenance, land reclamation, environmental protection dredging, and deep-sea mining. The dredging process of cutter suction dredgers is so complex that the operational data show strong characteristics of dynamic, nonlinearity, and time delay, which make it difficult to predict the productivity accurately via basic principles models. In this paper, we propose a novel integrating PCA-LSTM model to improve the productivity prediction of cutter suction dredger. Firstly, multiple variables are reduced in dimension and selected by PCA method based on the working mechanism of cutter suction dredger. Then the productivity is predicted via mud concentration in long short-term memory network with relevant operational time-series data. Finally, the proposed method is successfully applied to an actual case study in China. Also, it performs well in the cross-validation and comparative study for several important characteristics: (i) it involves the operational parameters based on the mechanism analysis; and (ii) it is a deep-learning-based approach that can deal with operation series data with a special memory mechanism. This study provides a heuristic idea for integrating the data-driven method and supervision of human knowledge for application in practical engineering.

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A Novel Method for Evaluating Dredging Productivity Using a Data Envelopment Analysis‐Based Technique

Cited by 7 publications

References 21 publications

Combining Novel Integration and the DEA Technique to Compute Dredging Productivity

Combining Novel Integration and the DEA Technique to Compute Dredging Productivity

A Novel Enhanced Supplier Selection Method Used for Handling Hesitant Fuzzy Linguistic Information

A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers

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