A new interval type-2 trapezoid fuzzy multi-attribute group decision-making method and its application to the evaluation of sponge city construction

The concept of sponge city receives more and more attention by Chinese government, and the evaluation of sponge city construction is an important aspect. To cope with the complexity and uncertainty of the evaluation process, this paper adopts interval type-2 trapezoidal fuzzy numbers (IT2TFNs) to express decision-making information and develops an approach for evaluating sponge city construction. To do these, two prioritized-guided interval type-2 trapezoidal fuzzy Hamacher operators are first defined to infuse IT2TFNs offered by experts, which can cope with the situation where there is prioritization among experts/attributes. In order to further consider the interactions among experts/attributes, two generalized-Shapley interval type-2 trapezoidal fuzzy prioritized Hamacher Choquet integral operators are presented. To measure the discrimination degree between IT2TFNs, a new interval type-2 trapezoidal fuzzy cross-entropy is defined. After that, cross-entropy based models for obtaining the optimal fuzzy measure on the expert/attribute set are constructed to handle the situation where the weighting information is interactive and partly known. Furthermore, an interval type-2 trapezoidal fuzzy multi-attribute group decision-making approach is developed. Finally, a practical example about the evaluation of residential land design plans in sponge city is provided to illustrate the utilization of the new method, and comparison analysis is provided.

[1]  Hak-Keung Lam,et al.  Membership-Function-Dependent Stabilization of Event-Triggered Interval Type-2 Polynomial Fuzzy-Model-Based Networked Control Systems , 2020, IEEE Transactions on Fuzzy Systems.

[2]  Robert Ivor John,et al.  Interval type-2 fuzzy sets improved by Simulated Annealing for locating the electric charging stations , 2021, Inf. Sci..

[3]  L. A. ZADEH,et al.  The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..

[4]  M. Grabisch The application of fuzzy integrals in multicriteria decision making , 1996 .

[5]  Jing Wang,et al.  An interval neutrosophic linguistic multi-criteria group decision-making method and its application in selecting medical treatment options , 2017, Neural Computing and Applications.

[6]  Huayou Chen,et al.  Approaches Based on Interval Type-2 Fuzzy Aggregation Operators for Multiple Attribute Group Decision Making , 2016, Int. J. Fuzzy Syst..

[7]  Yang Yu,et al.  Comprehensive performance evaluation of LID practices for the sponge city construction: A case study in Guangxi, China. , 2019, Journal of environmental management.

[8]  Ying Peng,et al.  Comprehensive cross-entropy and entropy measures and their applications under interval type-2 fuzzy environment , 2019, J. Intell. Fuzzy Syst..

[9]  Kasım Kiracı,et al.  Aircraft selection by applying AHP and TOPSIS in interval type-2 fuzzy sets , 2020, Journal of Air Transport Management.

[10]  Mooyoung Han,et al.  The effect of successive low-impact development rainwater systems on peak flow reduction in residential areas of Shizhuang, China , 2019, Environmental Earth Sciences.

[11]  Huidong Wang,et al.  A New Interval Type-2 Fuzzy VIKOR Method for Multi-attribute Decision Making , 2018, Int. J. Fuzzy Syst..

[12]  Lin Li,et al.  2-tuple Linguistic Intuitionistic Preference Relation and Its Application in Sustainable Location Planning Voting System , 2017, J. Intell. Fuzzy Syst..

[13]  Wei Bo,et al.  Fuzzy comprehensive evaluation in site selection for regulating storage tank of sponge city based on TFN-AHP , 2018, 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD).

[14]  Ting-Yu Chen,et al.  Signed distanced-Based TOPSIS Method for Multiple Criteria Decision Analysis Based on Generalized Interval-Valued Fuzzy numbers , 2011, Int. J. Inf. Technol. Decis. Mak..

[15]  Pranab K. Muhuri,et al.  Energy efficient multi-objective scheduling of tasks with interval type-2 fuzzy timing constraints in an Industry 4.0 ecosystem , 2020, Eng. Appl. Artif. Intell..

[16]  Fanyong Meng,et al.  Interval‐Valued Intuitionistic Fuzzy Multiattribute Group Decision Making Based on Cross Entropy Measure and Choquet Integral , 2013, Int. J. Intell. Syst..

[17]  İlker Gölcük,et al.  An interval type-2 fuzzy reasoning model for digital transformation project risk assessment , 2020, Expert Syst. Appl..

[18]  Zeshui Xu,et al.  Prioritized Measure-Guided Aggregation Operators , 2014, IEEE Transactions on Fuzzy Systems.

[19]  Zhiming Zhang,et al.  A novel approach to multi attribute group decision making based on trapezoidal interval type-2 fuzzy soft sets , 2013 .

[20]  Véronique Cerezo,et al.  SUP&R DSS: A sustainability-based decision support system for road pavements , 2019, Journal of Cleaner Production.

[21]  Peide Liu,et al.  Some trapezoidal interval type-2 fuzzy Heronian mean operators and their application in multiple attribute group decision making , 2018, J. Intell. Fuzzy Syst..

[22]  R. Singh,et al.  Detailed Sponge City Planning Based on Hierarchical Fuzzy Decision-Making: A Case Study on Yangchen Lake , 2017 .

[23]  Peide Liu,et al.  Interval type-2 fuzzy multi-attribute decision-making approaches for evaluating the service quality of Chinese commercial banks , 2020, Knowl. Based Syst..

[24]  Ting-Yu Chen,et al.  The extended QUALIFLEX method for multiple criteria decision analysis based on interval type-2 fuzzy sets and applications to medical decision making , 2013, Eur. J. Oper. Res..

[25]  Shyi-Ming Chen,et al.  Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method , 2010, Expert Syst. Appl..

[26]  Fanyong Meng,et al.  A new multiple attribute decision making method for selecting design schemes in sponge city construction with trapezoidal interval type-2 fuzzy information , 2020, Applied Intelligence.

[27]  Oscar Castillo,et al.  A comprehensive review on type 2 fuzzy logic applications: Past, present and future , 2020, Eng. Appl. Artif. Intell..

[28]  Shuxin Yang,et al.  Some new signed distances and similarity measures of interval type-2 trapezoidal fuzzy numbers and comparative study , 2018, J. Intell. Fuzzy Syst..

[30]  Yao Wang,et al.  An integrated framework to select resilient and sustainable sponge city design schemes for robust decision making , 2020 .

[31]  S. Orlovsky Decision-making with a fuzzy preference relation , 1978 .

[32]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[33]  Ting-Yu Chen A signed-distance-based approach to importance assessment and multi-criteria group decision analysis based on interval type-2 fuzzy set , 2012, Knowledge and Information Systems.

[34]  Ting-Yu Chen,et al.  Multiple criteria decision analysis using prioritised interval type-2 fuzzy aggregation operators and its application to site selection , 2017 .

[35]  Xiaohong Chen,et al.  The Fruit Fly Optimization Algorithms for Patient-Centered Care Based on Interval Trapezoidal Type-2 Fuzzy Numbers , 2019, Int. J. Fuzzy Syst..

[36]  Selman Karagoz,et al.  Interval type-2 Fuzzy ARAS method for recycling facility location problems , 2021, Appl. Soft Comput..

[37]  Young Hoon Joo,et al.  Design of Interval Type-2 Fuzzy-Based Sampled-Data Controller for Nonlinear Systems Using Novel Fuzzy Lyapunov Functional and its Application to PMSM , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[38]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[39]  Jean-Luc Marichal,et al.  The influence of variables on pseudo-Boolean functions with applications to game theory and multicriteria decision making , 2000, Discret. Appl. Math..

[40]  Zeshui Xu,et al.  Hesitant Fuzzy Linguistic Preference Utility Set and Its Application in Selection of Fire Rescue Plans , 2018, International journal of environmental research and public health.

[41]  Tong Wu,et al.  An interval type-2 fuzzy clustering solution for large-scale multiple-criteria group decision-making problems , 2016, Knowl. Based Syst..

[42]  Saeed Panahian Fard,et al.  Interval type-2 fuzzy neural networks version of the Stone-Weierstrass theorem , 2011, Neurocomputing.

[43]  L. Shapley A Value for n-person Games , 1988 .

[44]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[45]  Zhiming Zhang,et al.  Trapezoidal interval type-2 fuzzy aggregation operators and their application to multiple attribute group decision making , 2018, Neural Computing and Applications.

[46]  Dengbao Yao,et al.  Interval type-2 fuzzy information measures and their applications to attribute decision-making approach , 2017, J. Intell. Fuzzy Syst..

[47]  Yan Zhang,et al.  Multi-criteria decision making method based on possibility degree of interval type-2 fuzzy number , 2013, Knowl. Based Syst..

[49]  Jianhua Ma,et al.  Novel green supplier selection method by combining quality function deployment with partitioned Bonferroni mean operator in interval type-2 fuzzy environment , 2019, Inf. Sci..

[50]  Yanbing Gong,et al.  MULTI-ATTRIBUTE DECISION MAKING METHOD BASED ON BONFERRONI MEAN OPERATOR AND POSSIBILITY DEGREE OF INTERVAL TYPE-2 TRAPEZOIDAL FUZZY SETS , 2016 .

[51]  Amit Kumar,et al.  A note on “A novel approach to multi attribute group decision making based on trapezoidal interval type-2 fuzzy soft sets” , 2017 .

[52]  M. Sam Mannan,et al.  Supporting risk management decision making by converting linguistic graded qualitative risk matrices through interval type-2 fuzzy sets , 2020 .

[53]  Honghai Wang,et al.  TRAPEZOIDAL INTERVAL TYPE-2 FUZZY MACLAURIN SYMMETRIC MEAN OPERATORS AND THEIR APPLICATIONS TO MULTIPLE ATTRIBUTE GROUP DECISION MAKING , 2018 .

[54]  Jianzhou Wang,et al.  Intelligent multivariable air-quality forecasting system based on feature selection and modified evolving interval type-2 quantum fuzzy neural network. , 2021, Environmental pollution.

[55]  Jurgita Antucheviciene,et al.  A new soft computing approach for green supplier selection problem with interval type-2 trapezoidal fuzzy statistical group decision and avoidance of information loss , 2020, Soft Comput..

[56]  Jerry M. Mendel,et al.  Interval Type-2 Fuzzy Logic Systems Made Simple , 2006, IEEE Transactions on Fuzzy Systems.

[57]  Ronald R. Yager,et al.  Prioritized aggregation operators , 2008, Int. J. Approx. Reason..

[58]  Alev Taskin Gumus,et al.  Individual credit ranking by an integrated interval type-2 trapezoidal fuzzy Electre methodology , 2020, Soft Computing.

[59]  Ronald R. Yager,et al.  On Prioritized Multiple-Criteria Aggregation , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).