Hesitant Fuzzy Thermodynamic Method for Emergency Decision Making Based on Prospect Theory

Due to the timeliness of emergency response and much unknown information in emergency situations, this paper proposes a method to deal with the emergency decision making, which can comprehensively reflect the emergency decision making process. By utilizing the hesitant fuzzy elements to represent the fuzziness of the objects and the hesitant thought of the experts, this paper introduces the negative exponential function into the prospect theory so as to portray the psychological behaviors of the experts, which transforms the hesitant fuzzy decision matrix into the hesitant fuzzy prospect decision matrix (HFPDM) according to the expectation-levels. Then, this paper applies the energy and the entropy in thermodynamics to take the quantity and the quality of the decision values into account, and defines the thermodynamic decision making parameters based on the HFPDM. Accordingly, a whole procedure for emergency decision making is conducted. What is more, some experiments are designed to demonstrate and improve the validation of the emergency decision making procedure. Last but not the least, this paper makes a case study about the emergency decision making in the firing and exploding at Port Group in Tianjin Binhai New Area, which manifests the effectiveness and practicability of the proposed method.

[1]  Thomas L. Saaty,et al.  An innovative orders-of-magnitude approach to AHP-based mutli-criteria decision making: Prioritizing divergent intangible humane acts , 2011, Eur. J. Oper. Res..

[2]  Jianfeng Zhou.  Petri net modeling for the emergency response to chemical accidents , 2013 .

[3]  Bernard Roy,et al.  Problems and methods with multiple objective functions , 1971, Math. Program..

[4]  Francisco Herrera,et al.  A position and perspective analysis of hesitant fuzzy sets on information fusion in decision making. Towards high quality progress , 2016, Inf. Fusion.

[5]  Xin Zhang,et al.  Research on the multi-attribute decision-making under risk with interval probability based on prospect theory and the uncertain linguistic variables , 2011, Knowl. Based Syst..

[6]  Zeshui Xu,et al.  The TODIM analysis approach based on novel measured functions under hesitant fuzzy environment , 2014, Knowl. Based Syst..

[7]  Fan Min,et al.  Semi-greedy heuristics for feature selection with test cost constraints , 2016 .

[8]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[9]  Zeshui Xu,et al.  Approaches to multiple attribute group decision making based on intuitionistic fuzzy power aggregation operators , 2011, Knowl. Based Syst..

[10]  Zeshui Xu,et al.  Hesitant fuzzy information aggregation in decision making , 2011, Int. J. Approx. Reason..

[11]  Qingtian Zeng,et al.  E-Net Modeling and Analysis of Emergency Response Processes Constrained by Resources and Uncertain Durations , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[12]  Kuo-Chen Hung,et al.  An efficient fuzzy weighted average algorithm for the military UAV selecting under group decision-making , 2011, Knowl. Based Syst..

[13]  Han Liu,et al.  Rule-based systems: a granular computing perspective , 2016, Granular Computing.

[14]  Zeshui Xu,et al.  Approaches to Multi-Stage Multi-Attribute Group Decision Making , 2011, Int. J. Inf. Technol. Decis. Mak..

[15]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[16]  Xin Qian,et al.  Evaluation of Emergency Planning for Water Pollution Incidents in Reservoir Based on Fuzzy Comprehensive Assessment , 2010 .

[17]  Enrique Herrera-Viedma,et al.  A Mobile Decision Support System for Dynamic Group Decision-Making Problems , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[18]  Guohua Chen,et al.  Fuzzy-based methodology for performance assessment of emergency planning and its application , 2009 .

[19]  Enrique Herrera-Viedma,et al.  Integrating experts' weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors , 2016, Decis. Support Syst..

[20]  Juan Miguel Sánchez-Lozano,et al.  Evaluating military training aircrafts through the combination of multi-criteria decision making processes with fuzzy logic. A case study in the Spanish Air Force Academy , 2015 .

[21]  Bruno Apolloni,et al.  A neurofuzzy algorithm for learning from complex granules , 2016 .

[22]  Hugo Pastijn,et al.  Selecting land mine detection strategies by means of outranking MCDM techniques , 2002, Eur. J. Oper. Res..

[23]  Shyi-Ming Chen,et al.  Multicriteria linguistic decision making based on hesitant fuzzy linguistic term sets and the aggregation of fuzzy sets , 2014, Inf. Sci..

[24]  Eric Piatyszek,et al.  A model-based approach for a systematic risk analysis of local flood emergency operation plans: a first step toward a decision support system , 2012, Natural Hazards.

[25]  Yi Peng,et al.  Evaluation of clustering algorithms for financial risk analysis using MCDM methods , 2014, Inf. Sci..

[26]  Darren Lumbroso,et al.  An assessment of flood emergency plans in England and Wales, France and the Netherlands , 2011 .

[27]  Shyi-Ming Chen,et al.  Fuzzy decision making and fuzzy group decision making based on likelihood-based comparison relations of hesitant fuzzy linguistic term sets , 2015, J. Intell. Fuzzy Syst..

[28]  Ying-Ming Wang,et al.  A prospect theory-based interval dynamic reference point method for emergency decision making , 2015, Expert Syst. Appl..

[29]  Giuseppe D’Aniello,et al.  Enforcing situation awareness with granular computing: a systematic overview and new perspectives , 2016 .

[30]  Witold Pedrycz,et al.  Granular Computing and Decision-Making: Interactive and Iterative Approaches , 2015 .

[31]  Mingli Song,et al.  A study of granular computing in the agenda of growth of artificial neural networks , 2016, Granular Computing.

[32]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[33]  Edmundas Kazimieras Zavadskas,et al.  Proposing a new integrated model based on sustainability balanced scorecard (SBSC) and MCDM approaches by using linguistic variables for the performance evaluation of oil producing companies , 2014, Expert Syst. Appl..

[34]  Poong Hyun Seong,et al.  Development of automated operating procedure system using fuzzy colored petri nets for nuclear power plants , 2004 .

[35]  Na Chen,et al.  Correlation coefficients of hesitant fuzzy sets and their applications to clustering analysis , 2013 .

[36]  J K Ford,et al.  Emergency response training: strategies for enhancing real-world performance. , 2000, Journal of hazardous materials.

[37]  Zeshui Xu,et al.  Multiplicative Consistency of hesitant fuzzy Preference Relation and its Application in Group Decision Making , 2014, Int. J. Inf. Technol. Decis. Mak..

[38]  Paraskevi S. Georgiadou,et al.  Multi-objective evolutionary emergency response optimization for major accidents. , 2010, Journal of hazardous materials.

[39]  N. Malys,et al.  Comparative analysis of MCDM methods for the assessment of sustainable housing affordability , 2016 .

[40]  G. Tzeng,et al.  Evaluating sustainable fishing development strategies using fuzzy MCDM approach , 2005 .

[41]  Shaligram Pokharel,et al.  A model for planning locations of temporary distribution facilities for emergency response , 2015 .

[42]  Zeshui Xu,et al.  Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information , 2013, Knowl. Based Syst..

[43]  Enrique Herrera-Viedma,et al.  A statistical comparative study of different similarity measures of consensus in group decision making , 2013, Inf. Sci..

[44]  Georg Peters,et al.  DCC: a framework for dynamic granular clustering , 2016 .

[45]  Shyi-Ming Chen,et al.  Fuzzy decision making based on likelihood-based comparison relations of hesitant fuzzy linguistic term sets and hesitant fuzzy linguistic operators , 2015, Inf. Sci..

[46]  Yanyan Huang,et al.  Modeling and simulation method of the emergency response systems based on OODA , 2015, Knowl. Based Syst..

[47]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[48]  Dan Wang,et al.  Improving emergency response collaboration and resource allocation by task network mapping and analysis , 2014 .

[49]  A. Tversky,et al.  Advances in prospect theory: Cumulative representation of uncertainty , 1992 .

[50]  Marie Schmidt,et al.  Nonparametrics Statistical Methods Based On Ranks , 2016 .

[51]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[52]  V. Torra,et al.  A framework for linguistic logic programming , 2010 .

[53]  Yao Zhang,et al.  Risk decision analysis in emergency response: A method based on cumulative prospect theory , 2014, Comput. Oper. Res..

[54]  Reza Zanjirani Farahani,et al.  Combination of MCDM and covering techniques in a hierarchical model for facility location: A case study , 2007, Eur. J. Oper. Res..

[55]  Pawan Lingras,et al.  Granular meta-clustering based on hierarchical, network, and temporal connections , 2016 .

[56]  Fuzzy Multiple Criteria Decision Making Method Based on Prospect Theory , 2008, 2008 International Conference on Information Management, Innovation Management and Industrial Engineering.

[57]  Fritz Gehbauer,et al.  Optimized resource allocation for emergency response after earthquake disasters , 2000 .

[58]  Mahdi Zarghami,et al.  Application of risk-based multiple criteria decision analysis for selection of the best agricultural scenario for effective watershed management. , 2016, Journal of environmental management.

[59]  Thomas L. Saaty,et al.  Rank from comparisons and from ratings in the analytic hierarchy/network processes , 2006, Eur. J. Oper. Res..

[60]  Witold Pedrycz,et al.  A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts , 2013, Eur. J. Oper. Res..

[61]  Andrzej Skowron,et al.  Interactive granular computing , 2016 .

[62]  N. K. Kwak,et al.  An MCDM model for media selection in the dual consumer/industrial market , 2005, Eur. J. Oper. Res..

[63]  Najmeh Vaez,et al.  RANDAP: An integrated framework for reliability analysis of detailed action plans of combined automatic-operator emergency response taking into account control room operator errors , 2013 .

[64]  Yejun Xu,et al.  Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making , 2016, Knowl. Based Syst..

[65]  Zeshui Xu,et al.  On multi-period multi-attribute decision making , 2008, Knowl. Based Syst..

[66]  Zeshui Xu,et al.  Distance and similarity measures for hesitant fuzzy sets , 2011, Inf. Sci..

[67]  Edy Portmann,et al.  Granular computing as a basis of human–data interaction: a cognitive cities use case , 2016, Granular Computing.

[68]  Enrique Herrera-Viedma,et al.  Consensus reaching model in the complex and dynamic MAGDM problem , 2016, Knowl. Based Syst..

[69]  P. Perrot,et al.  A To Z Of Thermodynamics , 1998 .

[70]  Zeshui Xu,et al.  Managing multi-granularity linguistic information in qualitative group decision making: an overview , 2016 .

[71]  Xiao Zhang,et al.  Multiple attribute decision making considering aspiration-levels: A method based on prospect theory , 2013, Comput. Ind. Eng..