A Dynamic Decision Making Method Based on GM(1,1) Model with Pythagorean Fuzzy Numbers for Selecting Waste Disposal Enterprises

Li, Peng; Liu, Ju; Wei, Cuiping

doi:10.3390/su11205557

Cited by 11 publications

(6 citation statements)

References 35 publications

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“…In Equation ( 6), parameter -a is the development coefficient with a range of (−2,2), which reflects the dynamic relationship between the behavioral variable of the system and its background value, and when −a ≤ 0.3, GM(1,1) can be used for medium-and long-term prediction; b is the grey action, which is not only the concrete embodiment of the connotation extension of the grey system, but also an important symbol to distinguish the grey modeling from the input-output modeling in the black box [32,33].…”

Section: Establish and Solve The Grey Differential Equationmentioning

confidence: 99%

Prediction of China’s Grain Consumption from the Perspective of Sustainable Development—Based on GM(1,1) Model

Zhang

Bao

et al. 2022

Sustainability

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Being the largest producer and consumer of grain in the world, China occupies an extremely important position in the world grain market. The grain security of China is confronted with such problems as shortages of water and soil resources, a fragile ecological environment, and infrastructure constraints. The prediction and analysis of China’s grain consumption is conducive to establishing a resource-saving grain production mode, a sustainable grain supply and demand system, and a national grain security guarantee system at a higher level. In order to judge the future development trend of China’s grain accurately, guide grain production, stabilize grain expectation, and serve the relevant decision making of grain security, the GM(1,1) prediction model of China’s grain consumption has been constructed in this paper. Prediction research has been conducted with the grain consumption structure as the entry point. The model has high prediction accuracy and can be used for medium- and long-term prediction of China’s grain consumption after testing. The prediction results show that China’s grain consumption will continue to increase from 2022 to 2031, which is consistent with the factors of population change, urbanization promotion, consumption structure upgrading, and so on, in the country. Among the different types of consumption, the change in eating consumption will be small, the growth in feeding consumption and squeezing (soybean) consumption will slow down, industrial consumption will increase steadily, and seed consumption will be basically stable.

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Section: Establish and Solve The Grey Differential Equationmentioning

confidence: 99%

Prediction of China’s Grain Consumption from the Perspective of Sustainable Development—Based on GM(1,1) Model

Zhang

Bao

et al. 2022

Sustainability

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“…Although FS is widely used in the evaluation process to express expert's uncertainty, it only provides a single membership degree, neglecting the non-membership degree and hesitancy degree involving in the subjective evaluation (Zheng et al. , 2019; Li et al. , 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Because of the uncertainty and incompleteness of the evaluation judgment, it is difficult for experts to express their authentic insights with crisp values (Li et al, 2020). Although FS is widely used in the evaluation process to express expert's uncertainty, it only provides a single membership degree, neglecting the non-membership degree and hesitancy degree involving in the subjective evaluation (Zheng et al, 2019;Li et al, 2019). Experts may feel hesitant to express their opinions due to time pressure or limited available information (Li et al, 2020), while IVIFS proposed by Atanassov and Gargov (1989) enables tackling the uncertainty in linguistic evaluations effectively with membership, non-membership and hesitancy degree interval, and it has been used in many researches (Ye, 2011;Mahmoudi et al, 2019).…”

Section: The Application Of Qfd In Ssc Managementmentioning

confidence: 99%

Prioritizing risk factors in sustainable supply chain using fuzzy Kano and interval-valued intuitionistic fuzzy QFD

Xie

Xiang

et al. 2022

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PurposeThe purpose of the paper is to develop a hybrid method to prioritize risk factors (RFs) of sustainable supply chain (SSC) considering sustainable customer requirements (CRs) and uncertain evaluation.Design/methodology/approachIn the proposed method, fuzzy Kano model (FKM) is applied to prioritize sustainable CRs considering customer satisfaction (CS) and objective weight of each CR, the interval-valued intuitionistic fuzzy (IVIF) set theory is integrated with quality function deployment (QFD) to translate the sustainable CRs into RFs of SSC under uncertain environment and the IVIF cross-entropy is used to conduct objective analysis to prioritize RFs. Finally, a case in air-conditioner-manufacturing company is presented to demonstrate the proposed method.FindingsA case study of SSC risk management, the comparative analysis and associated discussions are conducted to illustrate the feasibility and effectiveness of the proposed method. The results obtained from the case study shows that RF5 (market share reduction) is the most important RF in the SSC. Compared with the existing methods, the proposed method can integrate sustainable CRs into SSC's RFs, handle uncertain information effectively and obtain objective importance of RFs.Originality/valueTheoretically, the paper develops a customer-oriented model based on the FKM, QFD, IVIF sets and entropy theory to prioritize RFs of SSC under uncertain environment. The model enables to integrate sustainable CRs into RFs managements and is efficient to deal with the subjectivity and conduct objective analysis to prioritize RFs. In practice, the systematic and correct RFs' priorities analysis provides reliable decision support for the managers to take measures to avoid or mitigate the critical RFs.

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“…Wang et al [46] added reciprocity preference theory to the traditional principal-agent model and established an incentive mechanism based on the optimal government share ratio. Peng Li et al proposed a dynamic decision-making method to evaluate the performance of enterprises in PPP projects [47].…”

Section: Literature Reviewmentioning

confidence: 99%

Incentive Mechanism for Municipal Solid Waste Disposal PPP Projects in China

Gui

2020

Sustainability

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In municipal solid waste disposal public–private-partnership (PPP) projects, economic benefits, as well as social and ecological benefits, are critical in sustaining sustainability development. However, private investors may make more efforts for economic benefits than for social and ecological benefits out of self-interest. Because the government does not have the same information that the investors have, information asymmetry leads to opportunistic behavior. To solve these problems, principal–agent models were established to analyze the incentive mechanism for encouraging investors to adopt a positive attitude toward both economic benefits and social and ecological benefits, inhibiting investors’ opportunistic behavior. In particular, numerical simulation was carried out to analyze the relationships between related parameters (c1,c2,ct,a,β,k,λ,p). The results show that the investors with higher comprehensive abilities are more willing to make efforts for social and ecological benefits. An increase in incentive and governance intensity would help to encourage investors to make more efforts for project benefits and to lower the level of opportunistic behavior adopted by investors. The complexity of the task and the fuzziness of the perception of effort input results aggravate the uncertainty and risk of the projects due to information asymmetry. Therefore, real-time and positive incentives are important. In order to ensure the social and ecological benefits of a project, performance standards should be set according to the actual situation; investors prefer fixed compensation with lower risk, but fixed compensation does not have incentive effect, and the proportion should not be too high.

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A Dynamic Decision Making Method Based on GM(1,1) Model with Pythagorean Fuzzy Numbers for Selecting Waste Disposal Enterprises

Cited by 11 publications

References 35 publications

Prediction of China’s Grain Consumption from the Perspective of Sustainable Development—Based on GM(1,1) Model

Prediction of China’s Grain Consumption from the Perspective of Sustainable Development—Based on GM(1,1) Model

Prioritizing risk factors in sustainable supply chain using fuzzy Kano and interval-valued intuitionistic fuzzy QFD

Incentive Mechanism for Municipal Solid Waste Disposal PPP Projects in China

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