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Application of Probabilistic Preference Theory in Modelling Complex Systems

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Volume 2021 |Article ID 9965473 | https://doi.org/10.1155/2021/9965473

Peng Li, Zhiwei Xu, "Evaluation of Nursing Homes Using a Novel PROMETHEE Method for Probabilistic Linguistic Term Sets", Complexity, vol. 2021, Article ID 9965473, 11 pages, 2021. https://doi.org/10.1155/2021/9965473

Evaluation of Nursing Homes Using a Novel PROMETHEE Method for Probabilistic Linguistic Term Sets

Academic Editor: Zeshui Xu
Received02 Apr 2021
Revised25 Apr 2021
Accepted26 Apr 2021
Published07 May 2021

Abstract

Aging has become a serious social problem in China. Traditional informal long-term care is hard to sustain because of the reduction in family size and elders’ children migration to big cities. The institution offering services for the disabled elders has been a tendency. There exists a strange phenomenon: some nursing homes are difficult to enter for most disabled elders, while the other ones must search for elders to maintain operation. Therefore, for the evaluation of nursing homes, two problems should be considered: (1) selecting suitable nursing homes for disabled elders; (2) obtaining the key factors influencing the selection of elders and helping nursing homes improve their services based on the key factors. First, we propose a new DEMATEL (Decision-Making Trial and Evaluation Laboratory) method for PLTSs to solve the second problem. Then, we present a novel PROMETHEE (Preference Ranking Organization Methods for Enrichment Evaluations) method to rank the alternatives and make a sensitivity analysis for criteria. Finally, we illustrate our proposed methods to an evaluation problem in Zhenjiang City by a case study. Based on the case study, we can obtain that our proposed methods are effective and practicable.

1. Introduction

With the development of society and medical level, China is entering an aging society. By the end of 2019, there are more than 253 million old people aged 60 or over. The population of disabled elders is more than 40 million. With the reducing size of families and adult children moving to cities, many disabled elders live alone and lack long-term care [1]. Traditional informal long-term care may lead to some problems for disabled elders, such as psychological loneliness [2] and reduction in household income [3]. Therefore, it is necessary for disabled elders to seek long-term care from nursing homes [4]. There have been numerous nursing homes in every city. The service levels of different nursing homes are multifarious. On one hand, some nursing homes are very popular that most disabled elders must wait for several years to enter them. On the other hand, many nursing homes’ occupancy rate is very low. To cope with the contradiction, there are two key problems that need to be solved. (1) How to help disabled elders choose suitable nursing homes? (2) What are the factors of concern for disabled elders and how to improve these factors? For the first problem, we can use a multiple criteria decision-making (MCDM) method to solve. With respect to the second one, in this paper, we use a Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to obtain the key factors for evaluation system.

For MCDM problems, there have been a large number of researches. In many cases, decision-makers (DMs) usually use linguistic information [5] to express their viewpoints. There have been lots of studies for linguistic information [6, 7]. To aggregate the information of different DMs easily, Pang et al. [8] proposed the definition of probabilistic linguistic term set (PLTS). Many studies for PLTSs have emerged from theory to application. As for aggregating rules for PLTSs, Pang et al. [8] first proposed the basic rules for PLTSs. Liao et al. [9] proposed some new operational rules based on disparity degrees. Li and Wei [10] put forward a series of new rules based on evidence theory. With regard to the application of PLTSs, Lin et al. [11] put forward a novel best worst method for PLTS and applied it to evaluate IoT platforms. Li et al. [12] proposed a new case-based reasoning method for PLTS and solved the evaluation of poverty-stricken families. Lin et al. [13] proposed some clustering algorithms for PLTSs. Lin et al. [13] proposed an ELECTREE method for PLTS. Lin et al. [14] proposed a new score function for PLTS and applied it to select children English educational organization.

DEMATEL method is an effective way to obtain the key factors influencing the evaluation system. Cause-effect interactions for different criteria (factors) can be obtained by processing a comprehensive direct influencing matrix. DEMATEL method has been expanded to different uncertain information, such as fuzzy numbers [15], grey numbers [16], hesitant fuzzy linguistic term set [17], and PLTS [18]. DEMATEL method has been applied to many research areas, such as supply chain management [19], new energy [20], and business ecosystem [21].

To solve the second key problem mentioned in the first paragraph, we will use the DEMATEL method to analyze the key factors influencing the choices of disabled elders. Because the evaluation information is expressed by PLTSs, we should extend the traditional DEMATEL method to probabilistic linguistic environment. Furthermore, in order to address the first key problem, we will propose a new PROMETHEE method to help disabled elders select suitable nursing homes for them. We choose the PROMETHEE method because it is easy to make a sensitivity analysis for criteria weights.

In this paper, we will propose a new DEMATEL method for PLTS to make an analysis of key factors influencing the evaluation system. Then, we will put forward a novel PROMETHEE method to rank the alternatives and make a sensitivity analysis for criteria. The main contributions and innovation points of this paper can be concluded as follows:(1)Propose a new DEMATEL method for PLTSs by transforming PLTSs into TFNs based on WOWA operators, which will help nursing homes obtain the concern factors of elders and can improve their services precisely(2)Propose a novel PROMETHEE II method for PLTSs, which will help elders select the most suitable nursing homes(3)Propose an approach to sensitivity analysis of criteria weights using a stability interval (WSI) method for PLTSs, which can help DMs find the variation range of criteria weights if the ranking results are stable

This paper is organized as follows. Section 2 reviews some basic definitions of PLTSs, TFNs, and WOWA operator. Section 3 proposes a novel DEMATEL method for PLTSs to obtain key factors for the evaluation system. Section 4 presents a PROMETHEE II method for PLTS and makes a sensitivity analysis for criteria. Section 5 applies our methods to an evaluation problem for nursing homes in Zhenjiang City. Section 6 makes a summary for this paper.

2. Preliminaries

In this section, we will review the basic definitions for PLTSs, TFNs, and WOWA operator.

2.1. PLTS

In real life, DMs may use linguistic information, such as “high” and “low,” to express their opinions for evaluating some objects. A typical linguistic term set (LTS) can be described as , where is a positive integer and is called granularity of LTS .

It is easy to find that LTS can describe the subjectivity of DM. However, in many cases, there are many DMs participating in the decision process. Traditional LTS cannot express the information conveniently in this situation. To address this issue, Pang et al. [8] proposed the definition of PLTS, which can effectively describe the information of many DMs using LTSs.

Definition 1. (see [8]). Let be an LTS; then a PLTS can be defined aswhere is the linguistic term associated with probability and is the number of all different linguistic terms in .

Example 1. Given an LTS , then and are both PLTSs.
From Example 1, we can give some explanations: (1) for PLTS , 40 percent of the DMs give evaluations using , 40 percent give of the DMs give evaluations using , and 20 percent of the DMs give evaluations using ; (2) for PLTS , 40 percent of the DMs give evaluations using , 40 percent of the DMs give evaluations using , and 20 percent of the DMs give up their opinions for some reasons. We can find that PLTSs can effectively describe the linguistic information of many DMs.

2.2. Triangular Fuzzy Number (TFN)

TFN uses three elements to describe uncertain information. It is convenient to use TFNs to express some uncertain linguistic information [12]. The definition of TFN can be seen as in Definition 2.

Definition 2. (see [22]). A three tuple is defined as a TFN if it satisfiesGiven three TFNs , , and , then the following operational rules hold [22]:(1)(2)(3),

Definition 3. (see [18, 22]). Let be a TFN; then its defuzzified centroid can be defined as

2.3. WOWA Operator

The definition of the WOWA operator is defined as follows.

Definition 4. (see [23]). Let be a weighting vector of numbers satisfying and . Then, mapping , which has an associated weighting vector such that and , is called a WOWA operator ifwhere is the i-th largest element in and is called comprehensive weight and can be obtained bywhere is monotone increasing function and can be seen in the paper proposed by Li et al. [18].
For simplicity, we call and importance weighting vector and position weighting vector, respectively. We can obtain the position weighting vector by the following mathematical programming [24]:where parameter can be given by DMs.

3. Obtaining Key Factors for Evaluation System by DEMATEL Method for PLTSs

When evaluating nursing homes, ranking alternatives are important but not the only target. Obtaining the key factors for the evaluation system is another target because it can help DMs to find the reasons leading to the decision results. DEMATEL method is an effective method to seek key factors and obtain criteria weights [18]. In the traditional DEMATEL method, DMs need to make a comparison between two criteria and give a comprehensive direct influencing matrix and then a total influencing matrix. The information for PLTSs cannot be used directly in the DEMATEL method. Therefore, we need to transform PLTSs into TFNs.

Given a PLTS , we can use the WOWA method to transform it into a TFN. We assume that a PLTS is . It is worth noting that, in traditional PLTS, the subscripts of linguistic terms may be not continuous. We need to add the missing linguistic terms with their probabilities equal to 0. We can transform the linguistic term () into TFN by the following rules [18, 25]:(1)If , then , , and (2)If , then and (3)If , then and

We can write as a numerical set . Then, we can transform PLTS into a TFN based on the following rules [18]:Rule 1: if , then TFN Rule 2: if , then TFN Rule 3: If and , then

Based on Rules 1–3, PLTS can be transformed into a TFN . We then obtain key factors for evaluation system by the DEMATEL method as follows.

3.1. Establish a Comprehensive Direct Influencing Matrix

For the evaluation problem, experts express their opinions by making pair comparisons for criteria using LTS . We aggregate the information from these experts and obtain a comprehensive direct influencing matrix .

3.2. Transform the Matrix into TFN Matrix

Because we cannot illustrate PLTS matrix to process the DEMATEL method, so we transform matrix into TFN matrix based on Rules 1–3.

3.3. Obtain Total Influencing Matrix

Normalize TFN matrix to , where . Compute the total influencing matrix based on

3.4. Make an Analysis for Relationship of Criteria

Based on total influencing matrix , compute the defuzzified centroid matrix . Calculate sums of rows and columns of matrix as follows:

Set a threshold [26]. In the defuzzified centroid matrix , if the element , we can say criterion has influence on .

Make an analysis based on the values of , which indicates net effect of criterion to the evaluation system. If , then criterion is called net cause factor. On the contrary, if , then criterion is called result factor.

Furthermore, we can obtain the criteria weights by the following [27]:

4. A Novel PROMETHEE II Method for PLTS and Sensitivity Analysis for Criteria

In this section, we will propose a novel PROMETHEE II method for PLTS and make a sensitivity analysis for criteria based on the WSI method.

4.1. A PROMETHEE II Method for PLTS

For evaluating nursing homes problems, criterion set is , alternative set (nursing homes) is , and the criteria weights set is . Experts give a decision matrix , where is a PLTS and indicates the value alternative with respect to criterion .Step 1. Transform PLTS matrix into TFN informationTo make the decision process easy to compute, we firstly transform PLTS matrix into TFN matrix based on Rules 1–3 in Section 3.Step 2. Obtain the defuzzified centroid matrix Compute the defuzzified centroid matrix of TFN matrix as , whereStep 3. Determine the positive flow and negative flow For criterion , the preference degree for alternative over can be obtained bywhere is a nondecreasing preference function. There are mainly six types of preference functions to choose [28]. In this paper, in order to compute simply, we choose the following preference function:Then, we can obtain the overall preference value of alternative over aswhere is the criterion weight for and can be obtained by equation (9).The positive flow and negative flow for alternative can be calculated by (14) and (15), respectively:Step 4. Compute the net flow and rank the alternatives

The net flow for alternative can be calculated by (16):

Rank the alternatives according to the values of net flow for all alternatives. The larger the values of net flow of the alternative, the higher the priority of the alternative.

4.2. Sensitivity Analysis for Criteria Using WSI Method

Accurate criteria weights are very important to make a reasonable decision. WSI method [29] is an effective method to make a sensitivity analysis for criteria. For criterion , its weight is , and we will see how the weight value can be modified without changing the ranking result. The new criteria weights are defined as follows:

Based on the PROMETHEE II method, we assume that

We give the following definition:

Then, we can get the weight stability interval of the criterion as follows:where and are the lower and upper bounds of the weight stable interval of criterion .

5. A Case Study

In this section, we will use our proposed methods to solve the evaluation of nursing homes in Zhenjiang City, Jiangsu Province. This section will include four parts: (1) decision problem description, (2) obtaining key factors for evaluation system and criteria weights based on DEMATEL, (3) ranking alternatives based on PROMETHEE II method for PLTS, and (4) further discussions and sensitivity analysis using WSI method.

5.1. Decision Problem Description

In recent years, due to the influence of the fertility policy, the number of the elderly populations in China began to increase continuously. China has become an aging population country. In such a population environment, the pension service industry began to develop. There are a variety of different pension models for the elderly to choose. As part of the pension pattern, institutional pensions are defined as institutions that provide centralized housing and care services for the elderly, such as nursing homes.

Recently, we walked into a service community for the elderly in Zhenjiang City, Jiangsu Province. In the early stage of preparation, we collected the relevant information of caregivers in four local nursing homes. In the process of investigation, we provided the relevant materials of caregivers in four nursing homes to the elderly in the community and invited them to make a comprehensive evaluation of caregivers in four nursing homes . There are four criteria [30] considered in the evaluation process: price acceptability , sustainability of service , responsibility , and service quality .


C1C2C3C4

C10{s−2(0.2), s−1(0.2), s1(0.4), s2(0.2)}{s−2(0.2), s−1(0.5), s2(0.3)}{s−2(0.3), s−1(0.3), s0(0.4)}
C2{s−2(0.2), s−1(0.3), s0(0.5)}0{s−2(0.2), S1(0.6), s2(0.2)}{s−1(0.2), S1(0.6), s2(0.2)}
C3{s−1(0.3), s0(0.2), s1(0.2), s2(0.3)}{s0(0.4), s1(0.2), S2(0.4)}0{s−2(0.2), s−1(0.6), s2(0.2)}
C4{s−2(0.3), s−1(0.1), s0(0.6)}{s0(0.5), s1(0.3), s2(0.2)}{s0(0.3), s1(0.6), s2(0.1)}0


C1C2C3C4

C1<0.000, 0.000, 0.000><0.000, 0.620, 1.000><0.000, 0.775, 1.000><0.000, 0.225, 0.750>
C2<0.000, 0.282, 0.750><0.000, 0.000, 0.000><0.000, 0.800, 1.000><0.000, 0.805, 1.000>
C3<0.000, 0.606, 1.000><0.250, 0.757, 1.000><0.000, 0.000, 0.000><0.000, 0.800, 1.000>
C4<0.000, 0.339, 0.750><0.250, 0.782, 1.000><0.250, 0.804, 1.000><0.000, 0.000, 0.000>


C1C2C3C4

C1<0.000, 0.191, 3.267><0.000, 0.472, 4.000><0.000, 0.529, 4.000><0.000, 0.357, 3.733>
C2<0.000, 0.306, 3.467><0.000, 0.351, 3.750><0.000, 0.583, 4.000><0.000, 0.540, 3.783>
C3<0.000, 0.403, 3.733><0.083, 0.585, 4.250><0.000, 0.411, 4.000><0.000, 0.563, 4.017>
C4<0.000, 0.322, 3.467><0.090, 0.562, 4.000><0.083, 0.589, 4.000><0.000, 0.332, 3.533>


C1C2C3C4

C11.1531.4911.5101.363
C21.2571.3671.5281.441
C31.3791.6401.4701.527
C41.2631.5511.5581.288

5.2. Obtaining Key Factors for Evaluation System and Criteria Weights Based on DEMATEL
(1)We invite ten experts to make a comparison between two criteria using LTSs. By aggregating the information, we obtain the comprehensive direct influencing matrix as shown in Table 1.(2)Based on Rules 1–3, we obtain the TFN matrix as shown in Table 2.(3)Based on equation (6), we obtain the total influencing matrix as shown in Table 3.(4)We can compute the defuzzified centroid matrix as shown in Table 4.

We can obtain the threshold . Influence relation between criteria can be seen in Figure 1.

The values of , , and can be seen in Table 5.



C15.5165.05210.5680.4640.232
C25.5936.04811.641−0.4540.255
C36.0156.06512.080−0.0500.265
C45.6595.62011.2790.0400.248

We can obtain the cause-effect relationship of criteria as shown in Figure 2.

It can be seen from Figure 2 that criteria price acceptability and service quality are cause factors influencing the evaluation system, while and sustainability of service and responsibility are effect ones that are affected by the evaluation system. In other words, price acceptability and service quality are the most concerned factors of elders in four ones. Nursing homes should lower the service price and improve service quality.

5.3. Ranking Alternatives Based on PROMETHEE II Method for PLTS

Based on the aggregation of the experts’ opinions, the decision matrix can be seen in Table 6.Step 1. Based on Rules 1–3, we can transform PLTS matrix into TFN decision matrix as shown in Table 7.Step 2. We can obtain the defuzzified centroid matrix as shown in Table 8.Step 3. Obtain the positive flow and negative flow as shown in Tables 9 and 10, respectively.Step 4. Compute the net flow of four alternatives as follows:


C1C2C3C4

X1{s−1(0.4), s0(0.2), S1(0.4)}{s−1(0.2), s1(0.8)}{s−2(0.6), s1(0.4)}{s−2(0.2), s−1(0.4), s0(0.2), s2(0.2)}
X2{s−1(0.2), s0(0.4), s1(0.2), s2(0.2)}{s−1(0.4), S1(0.4), s2(0.2)}{s−2(0.2), s−1(0.6), s0(0.2)}{s−1(0.4), s1(0.6)}
X3{s−2(0.3), s−1(0.3), s0(0.4)}{s−1(0.4), s0(0.2), s1(0.2), s2(0.2)}{s−1(0.2), s0(0.3), s1(0.4), s2(0.1)}{s−2(0.4), s0(0.6)}
X4{s−1(0.3), s1(0.5), s2(0.2)}{s0(0.4), s1(0.4), s2(0.2)}{s−1(0.2), s0(0.2), s1(0.4), s2(0.2)}{s0(0.2), s1(0.6), s2(0.2)}

The ranking result is .


C1C2C3C4

X1<0.000, 0.507, 1.000><0.000, 0.683, 1.000><0.000, 0.623, 1.000><0.000, 0.620, 1.000>
X2<0.000, 0.628, 1.000><0.000, 0.755, 1.000><0.000, 0.307, 0.750><0.000, 0.624, 1.000>
X3<0.000, 0.225, 0.750><0.000, 0.628, 1.000><0.000, 0.690, 1.000><0.000, 0.372, 0.750>
X4<0.000, 0.777, 1.000><0.250, 0.757, 1.000><0.000, 0.628, 1.000><0.250, 0.807, 1.000>


C1C2C3C4

X10.5020.5610.5410.540
X20.5430.5850.3520.541
X30.3250.5430.5630.374
X40.5920.6690.5430.686


C1C2C3C4

X11111
X22202
X30030
X43323


C1C2C3C4

X12222
X21131
X33303
X40010

We make a comparison between our proposed method and the method proposed by Pang et al. [8] as shown in Table 11.


Decision-making methodRanking orderOptimal alternative

Pang et al. [8] method
Proposed method

As can be seen from Table 11, the ranking results of the two methods are different. The main reason may be lie in the different decision-making mechanisms for the two methods. The method proposed by Pang et al. [8] uses the traditional TOPSIS method, while our method uses a specific outranking method. The advantages of the method to the other one are simple calculation and convenience in sensitivity analysis using the WSI method.

5.4. Further Discussions and Sensitivity Analysis Using WSI Method

In Section 3, the values parameter can make an influence on the final decision results. We will choose different values of to discuss the ranking results and sensitivity analysis situations. The criteria weights and ranking results under different values of can be seen in Figure 3 and Table 12, respectively.


Ranking orderOptimal alternative

0.1X4 > X1 > X2 > X3X4
0.2X4 > X2 > X1 > X3X4
0.3X4 > X2 > X1 > X3X4
0.4X4 > X2 > X1 > X3X4
0.5X4 > X2 > X3 > X1X4
0.6X4 > X2 > X3 > X1X4
0.7X4 > X2 > X3 > X1X4
0.8X4 > X2 > X3 > X1X4
0.9X4 > X2 > X3 > X1X4
1.0X4 > X2 > X3 > X1X4

We choose , and to make a sensitivity analysis using the WSI method as shown in Table 13.


C1C2C3C4

(0.000, 1.308)(0.000, 1.340)(0.777, 1.357)(0.556, 1.326)
(0.000, 1.000)(0.000, 1.000)(0.000, 0.428)(0.000, 0.581)
C1C2C3C4
(0.000, 1.258)(0.000, 1.258)(0.907, 1.361)(0.000, 1.258)
(0.034, 1.000)(0.063, 1.000)(0.000, 0.333)(0.054, 1.000)
C1C2C3C4
(0.949, 1.312)(0.000, 1.056)(0.808, 1.027)(0.974, 1.325)
(0.001, 0.276)(0.212, 1.000)(0.243, 0.405)(0.000, 0.264)

As can be seen from Table 13, the smaller the value of parameter , the larger stable the weight intervals. In fact, the parameter can reflect the confidences of DMs. If DMs have enough confidence on their judgments, the variation of weights will be very small. This is also true of reality.

6. Conclusions

China has been one of the most serious aging countries in the world. With the liberalization of the family planning policy, more and more families have two children. Many families have the 4-2-2 family structure, which means a couple should support four elders and raise two children. It is difficult for the couple to spend enough time to support the four elders. Furthermore, many young people migrate to big cities to bring home the bacon. Traditional informal preserving pattern for the elders based on families is not realizable for many families. The institution offering services for the elders is a new tendency. A few nursing homes’ supply falls short of demand that leads to the fact that most elders cannot enjoy their care services. While most private nursing homes operate hardly and have to look for customers. In order to resolve the contradiction, it is necessary to evaluate nursing homes, which will not only help elders to select suitable nursing homes but also find key concern point for elders. Therefore, in this paper, the evaluation process has two phases: seeking key factors and ranking results. We illustrate the DEMATEL method for PLTSs to analyze the key factors influencing evaluation process and obtain criteria weights. Then, we propose a novel PROMETHEE method to rank the alternatives and make a sensitivity analysis for criteria. Finally, we applied our methods to solve the nursing homes evaluation problem in Zhenjiang City to illustrate the effectiveness and practicability of our methods.

Future research will focus on clustering disabled elders to different categories based on case-based reasoning method, which will help government to give different financial support for certain disabled elders [31].

Data Availability

No data were used to support this study.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This paper was supported by the Ministry of Education Foundation of Humanities and Social Sciences of China (no. 19YJA630039).

References

  1. M. Li and H. Dai, “Determining the primary caregiver for disabled older adults in Mainland China: spouse priority and living arrangements,” Journal of Family Therapy, vol. 41, no. 1, pp. 126–141, 2019. View at: Publisher Site | Google Scholar
  2. N. B. Coe and C. H. Van Houtven, “Caring for mom and neglecting yourself? The health effects of caring for an elderly parent,” Health Economics, vol. 18, no. 9, pp. 991–1010, 2009. View at: Publisher Site | Google Scholar
  3. A. Heitmueller, “The chicken or the egg?” Journal of Health Economics, vol. 26, no. 3, pp. 536–559, 2007. View at: Publisher Site | Google Scholar
  4. L. Arnault, J.-F. Dartigues, C. Helmer, K. Pérès, and J. Wittwer, “Do partners of severely disabled elderly protect against nursing home entry? evidence from a French cohort,” The Journal of the Economics of Ageing, vol. 15, Article ID 100207, 2020. View at: Publisher Site | Google Scholar
  5. L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning-I,” Information Sciences, vol. 8, no. 3, pp. 199–249, 1975. View at: Publisher Site | Google Scholar
  6. D. Pamučar, S. Sremac, Ž. Stević, G. Ćirović, and D. Tomić, “New multi-criteria LNN WASPAS model for evaluating the work of advisors in the transport of hazardous goods,” Neural Computing and Applications, vol. 31, pp. 5045–5068, 2019. View at: Publisher Site | Google Scholar
  7. E. K. Zavadskas, R. Bausys, and I. Mazonaviciute, “Safety evaluation methodology of urban public parks by multi-criteria decision making,” Landscape and Urban Planning, vol. 189, pp. 372–381, 2019. View at: Publisher Site | Google Scholar
  8. Q. Pang, H. Wang, and Z. Xu, “Probabilistic linguistic term sets in multi-attribute group decision making,” Information Sciences, vol. 369, pp. 128–143, 2016. View at: Publisher Site | Google Scholar
  9. H. Liao, L. Jiang, B. Lev, and H. Fujita, “Novel operations of PLTSs based on the disparity degrees of linguistic terms and their use in designing the probabilistic linguistic ELECTRE III method,” Applied Soft Computing, vol. 80, pp. 450–464, 2019. View at: Publisher Site | Google Scholar
  10. P. Li and C. Wei, “An emergency decision-making method based on D-S evidence theory for probabilistic linguistic term sets,” International Journal of Disaster Risk Reduction, vol. 37, Article ID 101178, 2019. View at: Publisher Site | Google Scholar
  11. M. Lin, C. Huang, Z. Xu, and R. Chen, “Evaluating IoT platforms using integrated probabilistic linguistic MCDM method,” IEEE Internet of Things Journal, vol. 7, no. 11, pp. 11195–11208, 2020. View at: Publisher Site | Google Scholar
  12. P. Li, J. Liu, Y. Yang, and C. Wei, “Evaluation of poverty-stricken families in rural areas using a novel case-based reasoning method for probabilistic linguistic term sets,” Computers & Industrial Engineering, vol. 147, Article ID 106658, 2020a. View at: Publisher Site | Google Scholar
  13. M. Lin, Z. Chen, H. Liao, and Z. Xu, “ELECTRE II method to deal with probabilistic linguistic term sets and its application to edge computing,” Nonlinear Dynamics, vol. 96, no. 3, pp. 2125–2143, 2019. View at: Publisher Site | Google Scholar
  14. M. Lin, Z. Chen, Z. Xu, X. Gou, and F. Herrera, “Score function based on concentration degree for probabilistic linguistic term sets: an application to TOPSIS and VIKOR,” Information Sciences, vol. 551, pp. 270–290, 2021. View at: Publisher Site | Google Scholar
  15. B. A. Addae, L. Zhang, P. Zhou, and F. Wang, “Analyzing barriers of smart energy city in accra with two-step fuzzy DEMATEL,” Cities, vol. 89, pp. 218–227, 2019. View at: Publisher Site | Google Scholar
  16. L. Cui, H. K. Chan, Y. Zhou, J. Dai, and J. J. Lim, “Exploring critical factors of green business failure based on Grey-Decision Making Trial and Evaluation Laboratory (DEMATEL),” Journal of Business Research, vol. 98, pp. 450–461, 2019. View at: Publisher Site | Google Scholar
  17. F. Zhou, X. Wang, M. K. Lim, Y. He, and L. Li, “Sustainable recycling partner selection using fuzzy DEMATEL-AEW-FVIKOR: a case study in small-and-medium enterprises (SMEs),” Journal of Cleaner Production, vol. 196, pp. 489–504, 2018. View at: Publisher Site | Google Scholar
  18. P. Li, J. Liu, and C. Wei, “Factor relation analysis for sustainable recycling partner evaluation using probabilistic linguistic DEMATEL,” Fuzzy Optimization and Decision Making, vol. 19, no. 4, pp. 471–497, 2020b. View at: Publisher Site | Google Scholar
  19. K.-P. Lin, M.-L. Tseng, and P.-F. Pai, “Sustainable supply chain management using approximate fuzzy DEMATEL method,” Resources, Conservation and Recycling, vol. 128, pp. 134–142, 2018. View at: Publisher Site | Google Scholar
  20. D. Wei, H. Liu, and K. Shi, “What are the key barriers for the further development of shale gas in China? A grey-DEMATEL approach,” Energy Reports, vol. 5, pp. 298–304, 2019. View at: Publisher Site | Google Scholar
  21. L. J. Aaldering, J. Leker, and C. H. Song, “Analyzing the impact of industry sectors on the composition of business ecosystem: a combined approach using ARM and DEMATEL,” Expert Systems with Applications, vol. 100, pp. 17–29, 2018. View at: Publisher Site | Google Scholar
  22. Y.-M. Wang, K.-S. Chin, G. K. K. Poon, and J.-B. Yang, “Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean,” Expert Systems with Applications, vol. 36, no. 2, pp. 1195–1207, 2009. View at: Publisher Site | Google Scholar
  23. V. Torra, “The weighted OWA operator,” International Journal of Intelligent Systems, vol. 12, no. 2, pp. 153–166, 1997. View at: Publisher Site | Google Scholar
  24. Y. M. Wang and C. Parkan, “A minimax disparity approach for obtaining OWA operator weights,” Information Sciences, vol. 175, no. 1-2, pp. 20–29, 2005. View at: Publisher Site | Google Scholar
  25. H. Liu and R. M. Rodríguez, “A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multicriteria decision making,” Information Sciences, vol. 258, pp. 220–238, 2014. View at: Publisher Site | Google Scholar
  26. J. Shieh, H. Wu, and K. Huang, “A DEMATEL method in identifying key success factors of hospital service quality,” Knowledge-Based Systems, vol. 3, no. 3, pp. 220–238, 2010. View at: Publisher Site | Google Scholar
  27. W. Song and J. Cao, “A rough DEMATEL-based approach for evaluating interaction between requirements of product-service system,” Computers & Industrial Engineering, vol. 110, pp. 277–282, 2017. View at: Publisher Site | Google Scholar
  28. N. A. V. Doan and Y. De Smet, “An alternative weight sensitivity analysis for PROMETHEE II rankings,” Omega, vol. 80, pp. 166–174, 2018. View at: Publisher Site | Google Scholar
  29. B. Mareschal, “Weight stability intervals in multicriteria decision aid,” European Journal of Operational Research, vol. 33, no. 1, pp. 54–64, 1988. View at: Publisher Site | Google Scholar
  30. P. Li and N. Wang, “A novel multi-period two-sided matching method on solving long-term care problem for disabled elders with probabilistic linguistic information,” IEEE Access, vol. 8, pp. 149497–149509, 2020. View at: Publisher Site | Google Scholar
  31. M. Lin, H. Wang, Z. Xu, Z. Yao, and J. Huang, “Clustering algorithms based on correlation coefficients for probabilistic linguistic term sets,” International Journal of Intelligent Systems, vol. 33, no. 12, pp. 2402–2424, 2018. View at: Publisher Site | Google Scholar

Copyright © 2021 Peng Li and Zhiwei Xu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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