A hybrid framework integrating rough-fuzzy best-worst method to identify and evaluate user activity-oriented service requirement for smart product service system

Chen, Zhihua; Ming, Xinguo; Zhou, Tongtong; Yuan, Cheng; Sun, Poly Z. H.

doi:10.1016/j.jclepro.2020.119954

Cited by 59 publications

(21 citation statements)

References 51 publications

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“…While, in this paper, a new fuzzy-based MCDM method is employed, the FBWM is the extension of the basic Best-Worst method with consideration of the fuzziness of decision makers, which reduces the number of pairwise comparisons among criteria and improves the consistency of pairwise comparisons [23]. Because of the merits, the FBWM has been employed in many issues, such as the evaluation on service requirement of smart product service system [24], the selection of sustainable suppliers [25], national-scale landslide susceptibility mapping [26], and a strategy canvas drawing [27]. The fuzzy comprehensive evaluation method is also a commonly used MCDM method, which is based on the fuzzy set.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Evaluation of Electric Power Grid Enterprise Related to Electricity Transmission and Distribution Tariff Regulation Employing a Hybrid MCDM Model

Fang

et al. 2021

Mathematics

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In China, a new-round marketization reform of electricity industry is in progress, and the electricity transmission and distribution tariff reform is the core and important task. Currently, the electricity transmission and distribution tariff regulation has gone to the second round in China, and the electric power grid enterprises are facing a closed-loop regulatory system and an increasingly strict regulatory environment. Therefore, it is urgent to evaluate the risk of electric power grid enterprise that is related to electricity transmission and distribution tariff regulation, which can aid the electricity regulators and electric power grid enterprise operators to manage risk and promote the sustainable development of electric power industry. In this paper, a hybrid novel multi-criteria decision making (MCDM) method combining the fuzzy Best-Worst method (FBWM) and improved fuzzy comprehensive evaluation method based on a vague set is proposed for the risk evaluation of electric power grid enterprise related to electricity transmission and distribution tariff regulation. The risk evaluation index system is built. Subsequently, the FBWM is utilized to determine the optimal weights of electric power grid enterprise risk criteria, and the improved fuzzy comprehensive evaluation method that is based on vague set is employed to rank the comprehensive risk grade of electric power grid enterprise related to electricity transmission and distribution tariff regulation. The risk of a province-level electric power grid enterprise that is located in Northern China is empirically evaluated using the proposed MCDM method, and the result indicates that the overall risk of this province-level electric power grid enterprise belongs to ‘High’ grade, but it is very close to ‘Very High’ grade. The results indicate that the proposed hybrid novel MCDM method in this paper is effective and practical. Meanwhile, it provides a new view for the risk evaluation of electric power grid enterprise that is related to electricity transmission and distribution tariff regulation.

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Section: Literature Reviewmentioning

confidence: 99%

Risk Evaluation of Electric Power Grid Enterprise Related to Electricity Transmission and Distribution Tariff Regulation Employing a Hybrid MCDM Model

Fang

et al. 2021

Mathematics

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“…These characteristics refer to the smart capabilities of service components (e.g. perceptible, communicable, diagnosable, predictable and optimizable) (Chen et al, 2020b) and the complex interaction (e.g. material flow, function correlation, data flow, information flow and wisdom flow) between the service components (Song et al, 2015;Zheng et al, 2018).…”

Section: Research Gapsmentioning

confidence: 99%

“…The domain of physical SCP operation is implemented to realize the specific SCP's function that supports both the cyber service and physical service. The domain of cyber PSS operation is established to achieve smart functions (Chen et al, 2020b) such as real-time perception, interactive connection, dynamic monitor, informative diagnostic, accurate prediction, optimal decision-making, and smart prescription, etc. It includes four sub-domains, namely, SCP data management domain, SCP digital twin domain, data analytic domain and cyber service domain.…”

Section: Sps Components Identification With Cps-based Smart Product Service Blueprintmentioning

confidence: 99%

“…smart sensing, Internet of Things (IoT), cyber-physical system (CPS), digital-twin (DT), virtual-/ augmented-reality (VR/AR), artificial intelligence (AI)) enable most of the components in a smart PSS to be perceptible, communicable, diagnosable, interpretable, predictable, controllable and optimizable (Chen et al, 2020a;Siow et al, 2018;Rymaszewska et al, 2017). These critical characteristics empower the smart PSS present higher potential to agilely satisfy the dynamic requirements or needs of various multiple stakeholders (Chowdhury et al, 2018;Chen et al, 2020b) and flexibly response to the rapid changes in the external environment (Saunila et al, 2019). Although the potential of smart PSS to meet personalized requirements has been widely acknowledged (Valencia et al, 2015;Lerch and Gotsch, 2015;Chang et al, 2019), it does not imply that smart PSS would inherently have this capability.…”

Section: Introductionmentioning

confidence: 99%

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Modularization of smart product service: A framework integrating smart product service blueprint and weighted complex network

Chen

Ming

Vareilles

et al. 2020

Computers in Industry

Self Cite

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Modularization of smart product service (SPS) emerges as a priority strategy to agilely satisfy the dynamic customization requirement and to flexibly response to the rapid market change. Compared with the traditional product service, the SPS have more complicated interactions between the service components due to the novel characteristics caused by the application of smart technologies. The SPS modularization presents great differences from the identification of service component, correlation evaluation and module partition with the traditional product service modularization. However, most the existing research mainly focuses on the context of traditional product service, while containing scant study of smart product service. Therefore, this study proposes a hybrid framework for SPS modularization. In the framework, a cyber-physical product service blueprint is firstly proposed to represent the SPS operation process and identify the SPS components. Then, a rough-fuzzy correlation matrix is presented to determine the comprehensive interdependence between all pairs of SPS components with fully considering the hybrid decision uncertainties involved in the evaluation process, i.e., intrapersonal linguistic vagueness and interpersonal preference diversity. After that, the complex network theory is used to construct the SPS network and a modified Girvan-Newman algorithm is adopted for the SPS module partition. Finally, an illustrative modularization case of smart gearbox maintenance service and some caparisons with other methods demonstrate the feasibility and validity of the proposed approach.

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“…It is worth noting that Kansei engineering is more concerned with human emotional factors. Reviewing the literature indicates that the majority of emotional evaluations of the aircraft cockpit are carried out with conventional perceptual engineering research methods, including the Delphi expert consultation (Gbededo and Liyanage, 2020), focus groups (Axon et al ., 2020), quality function deployment (QFD; Pandey, 2020), and fuzzy evaluation (Chen et al ., 2020). Because these methods rely heavily on the human selection and expert experience for selecting evaluation indicators and preparing the judgment matrix.…”

Section: Introductionmentioning

confidence: 99%

Evaluating aircraft cockpit emotion through a neural network approach

Chen

Chu

et al. 2020

AIEDAM

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Studies show that there are shortcomings in applying conventional methods for the emotional evaluation of the aircraft cockpit. In order to resolve this problem, a more efficient cockpit emotion evaluation system is established in the present study to simply and quickly obtain the cockpit emotion evaluation value. To this end, the neural network is applied to construct an emotional model to evaluate the emotional prediction of the interior design of the aircraft cockpit. Moreover, several technologies and the Kansei engineering method are applied to acquire the cockpit interior emotional evaluation data for typical aircraft models. In this regard, the radical basis function neural network (RBFNN), Elman neural network (ENN), and the general regression neural network (GRNN) are applied to construct the sentimental prediction evaluation model. Then, the three models are comprehensively compared through factors such as the model evaluation criteria, network structure, and network parameters. Obtained experimental results indicate that the GRNN not only has the highest classification accuracy but also has the highest stability in comparison to the other two neural networks, so that it is a more appropriate method for the emotional evaluation of the aircraft cockpit. Results of the present study provide decision supports for the emotional evaluation of the cockpit interior space.

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A hybrid framework integrating rough-fuzzy best-worst method to identify and evaluate user activity-oriented service requirement for smart product service system

Cited by 59 publications

References 51 publications

Risk Evaluation of Electric Power Grid Enterprise Related to Electricity Transmission and Distribution Tariff Regulation Employing a Hybrid MCDM Model

Risk Evaluation of Electric Power Grid Enterprise Related to Electricity Transmission and Distribution Tariff Regulation Employing a Hybrid MCDM Model

Modularization of smart product service: A framework integrating smart product service blueprint and weighted complex network

Evaluating aircraft cockpit emotion through a neural network approach

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