Phương pháp trò chơi không hợp tác mờ q-rung orthopair cho các vấn đề ra quyết định nhóm chiến lược cạnh tranh dựa trên mô hình xác định trọng số chuyên gia động lai hỗn hợp
Tóm tắt
Cách lựa chọn chiến lược tối ưu để cạnh tranh với các đối thủ là một trong những vấn đề nóng nhất trong lĩnh vực ra quyết định đa thuộc tính (MADM). Tuy nhiên, hầu hết các phương pháp MADM không chỉ bỏ qua các đặc điểm của hành vi đối thủ mà còn chỉ đưa ra kết quả xếp hạng chiến lược đơn giản mà không thể phản ánh tính khả thi của từng chiến lược. Để khắc phục những nhược điểm này, một phương pháp trò chơi ma trận không hợp tác hai người dựa trên mô hình xác định trọng số chuyên gia động lai hỗn hợp được đề xuất để giải quyết các vấn đề ra quyết định nhóm chiến lược cạnh tranh phức tạp trong môi trường mờ q-rung orthopair. Ban đầu, một mô hình tính toán trọng số chuyên gia động mới, xem xét thông tin đánh giá cá nhân khách quan và chủ quan cùng một lúc, được thiết kế bằng cách tích hợp những ưu điểm của thang phân tích độ tin cậy và một thước đo khoảng cách Hausdorff cho các tập hợp mờ q-rung orthopair (q-ROFSs). Trọng số chuyên gia thu được từ mô hình trên có thể thay đổi với thông tin đánh giá chủ quan do các chuyên gia cung cấp, gần hơn với thực tiễn. Tiếp theo, một trò chơi ma trận mờ không hợp tác hai người được thiết lập để xác định các chiến lược hỗn hợp tối ưu cho các đối thủ, có thể trình bày tính khả thi cụ thể và mức độ phân kỳ của từng chiến lược cạnh tranh và ít bị ảnh hưởng bởi số lượng chiến lược. Cuối cùng, một ví dụ minh họa, một số phân tích so sánh và phân tích độ nhạy được thực hiện để xác thực tính hợp lý và hiệu quả của phương pháp quan đề xuất. Kết quả thực nghiệm cho thấy rằng phương pháp quan đề xuất như một phương pháp CSGDM có hiệu suất cao, độ phức tạp tính toán thấp và ít gánh nặng tính toán.
Từ khóa
Tài liệu tham khảo
Khanzadi M, Turskis Z, Amiri GG, Chalekaee A (2017) A model of discrete zero-sum two-person matrix games with grey numbers to solve dispute resolution problems in construction. J Civ Eng Manag 23(6):824–835. https://doi.org/10.3846/13923730.2017.1323005
Ding XF, Liu HC (2019) A new approach for emergency decision-making based on zero-sum game with Pythagorean fuzzy uncertain linguistic variables. Int J Intell Syst 34:1667–1684. https://doi.org/10.1002/int.22113
Seyedesfahani MM, Biazaran M, Gharakhani M (2011) A game theoretic approach to coordinate pricing and vertical co-op advertising in manufacturer-retailer supply chains. Eur J Oper Res 211(2):263–273. https://doi.org/10.1016/j.ejor.2010.11.014
Ale SB, Brown JS, Sullivan AT (2013) Evolution of cooperation: combining kin selection and reciprocal altruism into matrix games with social dilemmas. PLoS One 8(5):e63761. https://doi.org/10.1371/journal.pone.0063761
Zhou L, Xiao F (2019) A new matrix game with payoffs of generalized Dempster-Shafer structures. Int J Intell Syst 34(9):2253–2268. https://doi.org/10.1002/int.22164
Zhou JL, Shia YB, Sun ZY (2015) A hybrid fuzzy FTA-AHP method for risk decision-making in accident emergency response of work system. J Intell Fuzzy Syst 29(4):1381–1393. https://doi.org/10.3233/IFS-141512
Xue YX, You JX, Lai XD, Liu HC (2016) An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information. Appl Soft Comput 38:703–713. https://doi.org/10.1016/j.asoc.2015.10.010
Sha X, Yina C, Xu Z, Zhang S (2021) Probabilistic hesitant fuzzy TOPSIS emergency decision-making method based on the cumulative prospect theory. J Intell Fuzzy Syst 40(3):1–17. https://doi.org/10.3233/JIFS-201119
Neumann JV, Morgenstern O (1944) Theory of games and economic behavior. New York, Princeton University Press
Zadeh LA, Bellman RE (1970) Decision-making in a fuzzy environment. Manag Sci 17(4):B141–B164. https://doi.org/10.1287/mnsc.17.4.B141
Zimmermann HJ (1978) Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst 1(1):45–55. https://doi.org/10.1016/0165-0114(78)90031-3
Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96. https://doi.org/10.1016/S0165-0114(86)80034-3
Yager RR (2013) Pythagorean fuzzy subsets. In: 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS). IEEE 57–61
Yager RR (2016) Generalized orthopair fuzzy sets. IEEE Trans Fuzzy Syst 25(5):1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005
Yang Z, Ouyang T, Fu X, Peng X (2020) A decision-making algorithm for online shopping using deep-learning-based opinion pairs mining and q-rung orthopair fuzzy interaction Heronian mean operators. Int J Intell Syst 35(5):783–825. https://doi.org/10.1002/int.22225
Liu P, Wang Y (2020) Multiple attribute decision making based on q-rung orthopair fuzzy generalized Maclaurin symmetic mean operators. Inf Sci 518(2020):181–210. https://doi.org/10.1016/j.ins.2020.01.013
Zeng SZ, Hu YJ, Xie XY (2021) Q-rung orthopair fuzzy weighted induced logarithmic distance measures and their application in multiple attribute decision making. Eng Appl Artif Intell 100(7):104167. https://doi.org/10.1016/j.engappai.2021.104167
Riaz M, Farid H, Karaaslan F, Hashmi MR (2020) Some q-rung orthopair fuzzy hybrid aggregation operators and TOPSIS method for multi-attribute decision-making. J Intell Fuzzy Syst 39(1):1227–1241. https://doi.org/10.3233/JIFS-192114
Garg H, Gwak J, Mahmood T, Ali Z (2020) Power aggregation operators and VIKOR methods for complex q-rung orthopair fuzzy sets and their applications. Mathematics 8(4):538. https://doi.org/10.3390/math8040538
Liu P, Ali Z, Mahmood T (2021) Generalized complex q-rung orthopair fuzzy Einstein averaging aggregation operators and their application in multi-attribute decision making. Complex Intell Syst 7(1):511–538. https://doi.org/10.1007/s40747-020-00197-6
Garg H, Ali Z, Mahmood T (2020) Generalized dice similarity measures for complex q-Rung Orthopair fuzzy sets and its application. Complex Intell Syst 7:667–686. https://doi.org/10.1007/s40747-020-00203-x
Mahmood T, Ali Z (2021) Entropy measure and TOPSIS method based on correlation coefficient using complex q-rung orthopair fuzzy information and its application to multiple attribute decision making. Soft Comput 25:1249–1275. https://doi.org/10.1007/s00500-020-05218-7
Riaz M, Hamid MT, Afzal D, Pamucar D, Chu YM (2021) Multi-criteria decision making in robotic agri-farming with q-rung orthopair m-polar fuzzy sets. PLoS One 16(2):e0246485. https://doi.org/10.1371/journal.pone.246485
Naeem K, Riaz M, Karaaslan F (2021) Some novel features of Pythagorean m-polar fuzzy sets with applications. Complex Intell Syst 7:459–475. https://doi.org/10.1007/s40747-020-00219-3
Zhang ZM, Wu C (2014) A decision support model for group decision making with hesitant multiplicative preference relations. Inf Sci 282:136–166. https://doi.org/10.1016/j.ins.2014.05.057
Zhang ZM, Wang C, Tian XD (2015) A decision support model for group decision making with hesitant fuzzy preference relations. Knowledge-Based Syst 86:77–101
Wu ZB, Xu JP (2016) Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations. Omega 65:28–40. https://doi.org/10.1016/j.omega.2015.12.005
Xu Y, Rui D, Wang H (2017) A dynamically weight adjustment in the consensus reaching process for group decision-making with hesitant fuzzy preference relations. Int J Syst Sci 48(5–8):1311–1321
Liu S, Yu W, Liu L, Hu Y, Li Y (2019) Variable weights theory and its application to multi-attribute group decision making with intuitionistic fuzzy numbers on determining decision maker’s weights. PLoS One 14(3):e0212636. https://doi.org/10.1371/journal.pone.0212636
Zhang ZC (2006) A new method for the problem of multi-attribute decision making. J Wuhan Univ Technol 28(6):117–120
Tycab C (2020) Pythagorean fuzzy linear programming technique for multidimensional analysis of preference using a squared-distance-based approach for multiple criteria decision analysis. Expert Syst Appl 164:113908. https://doi.org/10.1016/j.eswa.2020.113908
Hao ZN, Xu ZS, Zhao H, Zhang R (2021) The context-based distance measure for intuitionistic fuzzy set with application in marine energy transportation route decision making. Appl Soft Comput 101:107044. https://doi.org/10.1016/j.asoc.2020.107044
Sarkar B, Biswas A (2021) Pythagorean fuzzy AHP-TOPSIS integrated approach for transportation management through a new distance measure. Soft Comput 7:1–17. https://doi.org/10.1007/s00500-020-05433-2
Liu P, Wang P (2018) Some q-rung orthopair fuzzy aggregation operators and their applications to multiple-attribute decision making. Int J Intell Syst 33(4):259–280. https://doi.org/10.1002/int.21927
Nadler SB (1978) Hyperspaces of sets. New York, Marcel Dekker, Inc.
Li DF (2012) A fast approach to compute fuzzy values of matrix games with payoffs of triangular fuzzy numbers. Eur J Oper Res 223(2):421–429. https://doi.org/10.1016/j.ejor.2012.06.020
Hung WL, Yang MS (2004) Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance. Pattern Recognit Lett 25(14):1603–1611. https://doi.org/10.1016/j.patrec.2004.06.006
Gou XJ, Xu ZS, Liao HC, Herrera F (2018) Multiple criteria decision making based on distance and similarity measures under double hierarchy hesitant fuzzy linguistic environment. Comput Ind Eng 126:516–530. https://doi.org/10.1016/j.cie.2018.10.020
Hu MM, Lan JB, Wang ZX (2019) A distance measure, similarity measure and possibility degree for hesitant interval-valued fuzzy sets. Comput Ind Eng 137:106088. https://doi.org/10.1016/j.cie.2019.106088
Hussain Z, Yang MS (2019) Distance and similarity measures of Pythagorean fuzzy sets based on the Hausdorff metric with application to fuzzy TOPSIS. Int J Intell Syst 34(10):2633–2654. https://doi.org/10.1002/int.22169
Hastie R, Kameda T (2005) The robust beauty of majority rules in group decisions. Psychol Rev 112(2):494–508. https://doi.org/10.1037/0033-295X.112.2.494
Ashraf S, Abdullah S (2020) Emergency decision support modeling for COVID-19 based on spherical fuzzy information. Int J Intell Syst 35:1601–1645. https://doi.org/10.1002/int.22262
Mahmood T (2020) A novel approach towards bipolar soft sets and their applications. J Math 2020:4690808. https://doi.org/10.1155/2020/4690808
Riaz M, Garg H, Farid H, Chinram R (2021) Multi-criteria decision making based on bipolar picture fuzzy operators and new distance measures. Comp Model Eng Sci 127(2):771–800. https://doi.org/10.32604/cmes.2021.014174
Chen TY, Li CH (2011) Objective weights with intuitionistic fuzzy entropy measures and computational experiment analysis. Appl Soft Comput 11(8):5411–5423. https://doi.org/10.1016/j.asoc.2011.05.018
Liu S, Chan FTS, Ran W (2013) Multi-attribute group decision-making with multi-granularity linguistic assessment information: an improved approach based on deviation and TOPSIS. Appl Math Model 37(24):10129–10140. https://doi.org/10.1016/j.apm.2013.05.051
Zhang Q, Chen JCH, Chong PP (2004) Decision consolidation: criteria weight determination using multiple preference formats. Decis Support Syst 38(2):247–258. https://doi.org/10.1016/s0167-9236(03)00094-0
Wang YM (2005) On fuzzy multiattribute decision-making models and methods with incomplete preference information. Fuzzy Sets Syst 151(2):285–301. https://doi.org/10.1016/j.fss.2004.08.015
Fu C, Wang Y (2015) An interval difference based evidential reasoning approach with unknown attribute weights and utilities of assessment grades. Comput Ind Eng 81:109–117. https://doi.org/10.1016/j.cie.2014.12.031
Chin KS, Fu C, Wang Y (2015) A method of determining attribute weights in evidential reasoning approach based on incompatibility among attributes. Comput Ind Eng 87:150–162. https://doi.org/10.1016/j.cie.2015.04.016
Nan JX, Wang T, Wang GX, An JJ (2016) The method for solving multi-objective zero-sum and constrained matrix games with heterogeneous values. Fuzzy Syst Math 30(4):121–128
Nan JX, Li DF, Zhang MJ (2008) A lexicographic method for matrix games with payoffs of triangular intuitionistic fuzzy numbers. Int J Comput Intell Syst 3(3):280–289. https://doi.org/10.2991/ijcis.2010.3.3.4
Ali M, Smarandache F, Vladareanu L. (2016) Neutrosophic Sets and Logic. Emerging Research on Applied Fuzzy Sets and Intuitionistic Fuzzy Matrices
Zeng S, Hu Y, Balezentis T, Streimikiene D (2020) A multi-criteria sustainable supplier selection framework based on neutrosophic fuzzy data and entropy weighting. Sustain Dev 28(5):1431–1440. https://doi.org/10.1002/sd.2096
Riaz M, Hashmi MR, Kalsoom H, Pamucar D, Chu Y (2020) Linear Diophantine fuzzy soft rough sets for the selection of sustainable material handling equipment. Symmetry 12(8):1215. https://doi.org/10.3390/sym12081215
Riaz M, Hashmi MR, Pamucar D, Chu Y (2021) Spherical linear diophantine fuzzy sets with modeling uncertainties in MCDM. CMES-Comp Model Eng Sci 126(3):1125–1164. https://doi.org/10.32604/cmes.2021.013699