Year 2023,
Volume: 41 Issue: 1, 178 - 193, 14.03.2023
Serkan Altuntaş
,
Türkay Dereli
,
Zülfiye Erdoğan
References
- REFERENCES
- [1] Groover M. Automation, production systems, and computer-integrated manufacturing. 1st ed. India: Prentice Hall Press; 2007.
- [2] Rasmi SAB, Kazan C, Turkay M. A multi-criteria decision analysis to include environmental, social, and cultural issues in the sustainable aggregate pro-
duction plans. Comput Ind Eng 2019;132:348–360.[CrossRef]
- [3] Su TS, Lin YF. Fuzzy multi-objective procure-ment/production planning decision problems for recoverable manufacturing systems. J Manuf Syst 2015;37:396–
408. [CrossRef]
- [4] Boral S, Howard I, Chaturvedi SK, McKee K, Naikan VNA. A novel hybrid multi-criteria group decision making approach for failure mode and effect analysis: An
essential requirement for sustainable manufac-turing. Sustain Prod Consum 2020;21:14–32. [CrossRef]
- [5] Vazifehdan MN, Darestani SA. Green Logistics out-sourcing employing multi criteria decision making and quality function deployment in the petrochemi-cal
industry. Asian J Shipp Logist 2019;35:243–254.[CrossRef]
- [6] Ervural BC, Ervural B, Kabak O. A group deci-sion making approach for the evaluation of flex-ible manufacturing systems. IFAC-PapersOnLine 2016;49:1329–
1334. [CrossRef]
- [7] Pagone E, Salonitis K, Jolly M. Automatically weighted high-resolution mapping of multi-criteria decision analysis for sustainable manufacturing sys-tems. J
Clean Prod 2020;257:120272. [CrossRef]
- [8] Yu C, Matta A, Semeraro Q. Group decision making in manufacturing systems: An approach using spa-tial preference information and indifference zone. J
Manuf Syst 2020;55:109–125. [CrossRef]
- [9] Ghenai C, Albawab M, Bettayeb M. Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended
SWARA/ARAS hybrid method. Renew Energy 2020;146:580–597. [CrossRef]
- [10] Zhang H. Understanding the linkages: A dynamic sustainability assessment method and decision making in manufacturing systems. Procedia CIRP
2019;80:233–238. [CrossRef]
- [11] Sinha AK, Anand A. Development of sustainable supplier selection index for new product develop-ment using multi criteria decision making. J Clean Prod
2018;197:1587–1596. [CrossRef]
- [12] Stoycheva S, Marchese D, Paul C, Padoan S, Juhmani A, Linkov I. Multi-criteria decision analy-sis framework for sustainable manufacturing in automotive
industry. J Clean Prod 2018;187:257–272. [CrossRef]
- [13] Guo Y, Wang N, Xu ZY, Wu K. The internet of things-based decision support system for informa-tion processing in intelligent manufacturing using data mining technology. Mech Syst Signal Process 2020;142:106630. [CrossRef]
- [14] Kunath M, Winkler H. Integrating the Digital Twin of the manufacturing system into a decision support system for improving the order management pro-cess.
Procedia CIRP 2018;72:225–231. [CrossRef]
- [15] Li X, Fang Z, Yin C. A machine tool matching method in cloud manufacturing using Markov Decision Process and cross-entropy. Robot Comput Integr Manuf
2020;65:101968. [CrossRef]
- [16] Cheng H, Xu W, Ai Q, Liu Q, Zhou Z, Pham DT. Manufacturing capability assessment for human-robot collaborative disassembly based on multi-data fusion.
Procedia Manuf 2017;10:26–36. [CrossRef]
- [17] De Vin LJ, Holm M, Ng AHC. The Information Fusion JDL-U model as a reference model for Virtual Manufacturing. Robot Comput Integr Manuf 2010;26:629–638.
[CrossRef]
- [18] Wu Q, Lin W, Zhou L, Chen Y, Chen H. Enhancing multiple attribute group decision making flex-ibility based on information fusion technique and hesitant
Pythagorean fuzzy sets. Comput Ind Eng 2019;127:954–970. [CrossRef]
- [19] Yin Y, Zhang L, Liao W, Niu H, Chen F. A knowledge resources fusion method based on rough set theory for quality prediction. Comput Ind 2019;108:104–114. [CrossRef]
- [20] Ersoz F, Kabak M. A literature review of multiple criteria decision making methods at defence sector applications. Def Sci J 2010;9:97–125.
- [21] Kahraman C. Fuzzy Multi-Criteria Decision Making: Theory and Applications with Recent Developments. 1 st ed. Berlin: Springer; 2008. [CrossRef]
- [22] Besbes M, Affonso RC, Zolghadri M, Masmoudi F, Haddar M. Multi-criteria decision making for the selection of a performant manual workshop layout: a case
study. IFAC-PapersOnLine 2017;50:12404–12409. [CrossRef]
- [23] Bougrine A, Darmoul S, Hajri-Gabouj S. Topsis based multi-criteria reconfiguration of manufac-turing systems considering operational and ergo-nomic
indicators. In: Amor AB, editor. International Conference on Advanced Systems and Electric Technologies (IC_ASET); 2017 Jan 14-17; New Jersey: IEEE; 2017. pp. 329–334. [CrossRef]
- [24] Kumar S, Kumar S, Barman AG. Supplier selection using fuzzy TOPSIS multi criteria model for a small scale steel manufacturing unit. Procedia Comput Sci
2018;133:905–912. [CrossRef]
- [25] Liao CN, Kao HP. An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management. Expert Syst Appl 2011;38:10803–10811.
[CrossRef]
- [26] Ma J, Harstvedt JD, Jaradat R, Smith B. Sustainability driven multi-criteria project portfolio selection under the uncertain decision-making environment.
Comput Ind Eng 2020;140:106236. [CrossRef]
- [27] Ozcan T, Celebi N, Esnaf S. Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selec-tion
problem. Expert Syst Appl 2011;38:9773–9779.[CrossRef]
- [28] Peng C, Du H, Liao TW. A research on the cutting database system based on machining features and TOPSIS. Robot Comput Integr Manuf 2017;43:96–104. [CrossRef]
- [29] Yuvaraj N, Pradeep Kumar M. Multiresponse optimization of abrasive water jet cutting process parameters using TOPSIS approach. Mater ManufProcess
2015;30:882–889. [CrossRef]
- [30] Geng X, Liu Q. A hybrid service supplier selection approach based on variable precision rough set and VIKOR for developing product service system. Int J
Comput Integr Manuf 2015;28:1063–1076.
- [31] Lin XH, Feng YX, Tan JR, An XH. Product con-cept evaluation based on hybrid model of advanced DEMATEL-VIKOR algorithm. Comput Integr Manuf Syst 2011;17.
- [32] Singh S, Olugu EU, Musa SN, Mahat AB, Wong KY. Strategy selection for sustainable manufactur-ing with integrated AHP-VIKOR method under interval-
valued fuzzy environment. Int J Adv Manuf Technol 2016;84:547–563. [CrossRef]
- [33] Sasananan M, Narkhede BE, Gardas BB, Raut RD. Selection of third party logistics service provider using a multi-criteria decision making approach for
Indian cement manufacturing industries. Sci Technol Asia 2016;21:70–81.
- [34] Yadav V, Sharma MK. Multi-criteria supplier selec-tion model using the analytic hierarchy process approach. J Model Manag 2016;11:326–354. [CrossRef]
- [35] Kluczek A. An overall multi-criteria approach to sustainability assessment of manufacturing pro-cesses. Procedia Manuf 2017;8:136–143. [CrossRef]
- [36] Sevkli M. An application of the fuzzy ELECTRE method for supplier selection. Int J Prod Res 2010;48:3393–3405. [CrossRef]
- [37] Maniya KD, Bhatt MG. A multi-attribute selection of automated guided vehicle using the AHP/M-GRA technique. Int J Prod Res 2011;49:6107–6124.[CrossRef]
- [38] Sandeep M, Kumanan S, Vinodh S. Supplier selec-tion using combined AHP and GRA for a pump manufacturing industry. Int J Logist Syst Manag
2011;10:40–52. [CrossRef]
- [39] Badi IA, Abdulshahed AM, Shetwan AG. A case study of supplier selection for a steelmaking com-pany in Libya by using the Combinative Distance-based
Assessment (CODAS) model. Decis Mak Appl Manag Eng 2018;1:1–12. [CrossRef]
- [40] Mathew M, Sahu S. Comparison of new multi-crite-ria decision making methods for material handling equipment selection. Manag Sci Lett 2018;8:139–150. [CrossRef]
- [41] Triantaphyllou E. Multi-criteria decision making methods. Multi-criteria decision making methods: A comparative study. 1st ed. Boston: Springer; 2000. pp. 5–
21. [CrossRef]
- [42] de Farias Aires RF, Ferreira L. A new approach to avoid rank reversal cases in the TOPSIS method. Comput Ind Eng 2019;132:84–97. [CrossRef]
- [43] Buede DM, Maxwell DT. Rank disagreement: A comparison of multi‐criteria methodologies. J Multi‐Criteria Decis Anal 1995;4:1–21. [CrossRef]
- [44] Zanakis SH, Solomon A, Wishart N, Dublish S. Multi-attribute decision making: A simulation comparison of select methods. Eur J Oper Res 1998;107:507–529. [CrossRef]
- [45] Triantaphyllou E, Shu B. On the maximum num-ber of feasible ranking sequences in multi-cri-teria decision making problems. Eur J Oper Res 2001;130:665–678. [CrossRef]
- [46] Jan KH, Tung CT, Deng P. Rank reversal problem related to wash criterion in analytic hierarchy pro-cess (AHP). Afr J Bus Manag 2011;5:8301–8306.
[CrossRef]
- [47] Lin JSJ, Chou SY, Chouhuang WT, Hsu CP. Note on ‘Wash criterion in analytic hierarchy process. Eur J Oper Res 2008;185:444–447. [CrossRef]
- [48] Yuen KKF. Pairwise opposite matrix and its cog-nitive prioritization operators: comparisons with pairwise reciprocal matrix and analytic prioritiza-tion
operators. J Oper Res Soc 2012;63:322–338. [CrossRef]
- [49] Roszkowska E. Rank ordering criteria weighting methods–a comparative overview. Optimum Econ Stud 2013;5:14–33. [CrossRef]
- [50] Borda JD. Mémoire sur les élections au scru-tin. Histoire de l’Académie Royale des Sciences 1781;102:657–665.
- [51] Argentini A, Blanzieri E. Ranking aggregation based on belief function. In: Greco S, Bouchon-Meunier B, Coletti G, Fedrizzi M, Matarazzo B, Yager RR, editors.
International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems; 2012 Jul 9-13; Berlin: Springer; 2012. pp. 511–
520.
- [52] Kim Y, Chung ES. Assessing climate change vul-nerability with group multi-criteria decision mak-ing approaches. Clim Change 2013;121:301–315.
[CrossRef]
- [53] Condorcet MD. Essai sur l’application de l’analyse à la probabilité des décisions rendues à la pluralité des voix. 1785. [French]
- [54] Bhattacharya S, Raju V. A Condorcet Voting the-ory based AHP approach for MCDM problems,” Indones. J Electr Eng Comput Sci 2017;7:276–286.
[CrossRef]
- [55] Bairagi B, Dey B, Sarkar B, Sanyal SK. A De Novo multi-approaches multi-criteria decision making technique with an application in performance eval-
uation of material handling device. Comput Ind Eng 2015;87:267–282. [CrossRef]
- [56] Saad S, Kunhu N, Mohamed A. A fuzzy-AHP multi-criteria decision making model for procurement pro-cess. Int J Logist Syst Manag 2016;23:1–24.
[CrossRef]
- [57] Memari A, Dargi A, Jokar MRA, Ahmad R, Rahim ARA. Sustainable supplier selection: A multi-cri-teria intuitionistic fuzzy TOPSIS method. J Manuf Syst 2019;50:9–24. [CrossRef]
- [58] Shumaiza, Akram M, Al-Kenani AN, Alcantud JCR. Group decision-making based on the VIKOR method with trapezoidal bipolar fuzzy ınformation.
Symmetry 2019;11:1313. [CrossRef]
- [59] Akram M, Kahraman C, Zahid K. Group decision-making based on complex spherical fuzzy VIKOR approach. Knowl Based Syst 2021;216:106793.[CrossRef]
- [60] Hsu HM, Chen CT. Aggregation of fuzzy opin-ions under group decision making. Fuzzy Sets Syst 1996;79:279–285. [CrossRef]
- [61] Smolíková R, Wachowiak MP. Aggregation operators for selection problems. Fuzzy Sets Syst 2002;131:23–34. [CrossRef]
- [62] Tsiporkova E, Boeva V. Multi-step ranking of alter-natives in a multi-criteria and multi-expert decision making environment. Inf Sci 2006;176:2673–2697.
[CrossRef]
- [63] Huang YS, Li WH. A study on aggregation of TOPSIS ideal solutions for group decision-making. Group Decis Negot 2012;21:461–473. [CrossRef]
- [64] Maleki H, Zadeh AH. Comparison of the REMBRANDT system with the Wang and Elhag approach: A practical example of the rank reversal problem. African J Bus Manag 2012;6:459–473. [CrossRef]
- [65] Dede G, Kamalakis T, Sphicopoulos T. Convergence properties and practical estimation of the prob-ability of rank reversal in pairwise comparisons for
multi-criteria decision making problems. Eur J Oper Res 2015;241:458–468. [CrossRef]
- [66] Al Salem AA, Awasthi A. Investigating rank rever-sal in reciprocal fuzzy preference relation based on additive consistency: Causes and solutions. Comput
Ind Eng 2018;115:573–581. [CrossRef]
- [67] Mousavi-Nasab SH, Sotoudeh-Anvari A. A new multi-criteria decision making approach for sustain-able material selection problem: A critical study on
rank reversal problem. J Clean Prod 2018;182:466–484.[CrossRef]
- [68] Ma LC, Li HL. Using Gower Plots and Decision Balls to rank alternatives involving inconsistent prefer-ences. Decis Support Syst 2011;51:712–719. [CrossRef]
- [69] Abel E, Mikhailov L, Keane J. Inconsistency reduc-tion in decision making via multi-objective optimi-sation. Eur J Oper Res 2018;267:212–226. [CrossRef]
- [70] Dwork C, Kumar R, Naor M, Sivakumar D, Rank aggregation revisited. 2001.
- [71] Dwork C, Kumar R, Naor M, Sivakumar D. Rank aggregation methods for the web. In: Shen VY, Saito N, Lyu MR, editors. Proceedings of the 10th international
conference on World Wide Web; 2001 May 1-5; New York: Association for Computing Machinery; 2001. pp. 613–622. [CrossRef]
- [72] Jahan A, Ismail MY, Shuib S, Norfazidah D, Edwards KL. An aggregation technique for optimal decision-making in materials selection. Mater Des 2011;32:4918–
4924. [CrossRef]
- [73] Ding J, Han D, Dezert J, Yang Y. A new hierarchi-cal ranking aggregation method. Inf Sci 2018;453: 168–185. [CrossRef]
- [74] Telmoudi A, Salem C. Data fusion application from evidential databases as a support for decision mak-ing. Inf Softw Technol 2004;46:547–555.
[CrossRef]
- [75] Waltz EL, Buede DM. Data fusion and decision sup-port for command and control. IEEE Trans Syst Man Cybern 1986;16:865–879. [CrossRef]
- [76] Hall DL. An introduction to multisensor data fusion. Proc IEEE 1997;85:6–23. [CrossRef]
- [77] Zhang Y, Qiang J, Carl GL. Active information fusion for decision making under uncertainty. Proceedings of the Fifth International Conference on Information
Fusion; 2002 Jul 8-11; Annapolis: IEEE; 2002. pp. 643–650.
- [78] Goshtasby AA, Nikolov S. Image fusion: advances in the state of the art. Inf Fusion 2007;2:114–118. [CrossRef]
- [79] Khaleghi B, Khamis A, Karray FO, Razavi SN. Multisensor data fusion: A review of the state-of-the-art. Inf Fusion 2013;14:28–44. [CrossRef]
- [80] Ribeiro RA, Falcão A, Mora A, Fonseca JM. FIF: A fuzzy information fusion algorithm based on multi-criteria decision making. Knowl Based Syst 2014;58:23–
32. [CrossRef]
- [81] Paradis S, Breton R, Roy J. Data fusion in support of dynamic human decision making. Fusion99, 1999.
- [82] Samoylov VV. Data fusion systems: Principles and architecture for data processing in decision making system learning. Inform Autom 2002;1:69–83.
- [83] Nilsson M, Ziemke T. Information fusion: a deci-sion support perspective. 2007 10th International Conference on Information Fusion; 2007 Jul 9-12; Quebec: IEEE; 2007. pp. 1–8. [CrossRef]
- [84] Deng Y, Chan FTS, Wu Y, Wang D. A new linguis-tic MCDM method based on multiple-criterion data fusion. Expert Syst Appl 2011;38:6985–6993.[CrossRef]
- [85] Altuntas S, Dereli T. Facility layout design selec-tion by the technique of precise order preference. 7th International Research Meeting in Business and Management, Jul 11-12, 2016.
- [86] Dorfeshan Y, Mousavi SM, Mohagheghi V, Vahdani B. Selecting project-critical path by a new inter-val type-2 fuzzy decision methodology based on MULTIMOORA, MOOSRA and TPOP methods. Comput Ind Eng 2018;120:160–178. [CrossRef]
- [87] Rao RV. Decision making in the manufacturing environment: using graph theory and fuzzy mul-tiple attribute decision making methods. 2nd ed. London:
Springer Science & Business Media; 2013. [CrossRef]
- [88] Zar JH. Significance testing of the spearman rank correlation coefficient. J Am Stat Assoc 1972;67:578–580. [CrossRef]
- [89] Nie NH, Bent DH, HCH. SPSS: Satatistical pack-age for the social sciences. New York: McGraw-Hill, 1975.
- [90] Khare S, Bhandari A, Saurabh S, Arora A. ECG arrhythmia classification using spearman rank cor-relation and support vector machine. In: Deep K, Nagar A, Pant M, Bansal JC, editors. Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011); 2011 Dec 20- 22; Roorke, India: Springer: 2011. pp. 591–598.
Decision making in the manufacturing environment using the technique of precise order preference
Year 2023,
Volume: 41 Issue: 1, 178 - 193, 14.03.2023
Serkan Altuntaş
,
Türkay Dereli
,
Zülfiye Erdoğan
Abstract
Wrong decisions in manufacturing systems can jeopardize the continuity of production and reduce productivity and efficiency. The refore, it is ess ential to mak e the rig ht dec isions in solving the problems encountered in manufacturing environments. In the literature, there are many methods developed to be used in solving decision-making problems. The results of different methods used in solving the same problem are different from each other. Thus, the rankings obtained by the different methods to solve the same decision-making problem in the manufacturing environment are different. Different rankings obtained for the same problem cause inconsistencies and it is not easy to determine which sort of order is better. In this study, the use of the technique of precise order preference (TPOP) is proposed to solve the decision-making problems in manufacturing systems. Three case studies are presented to illustrate the use of the TPOP method to solve decision-making problems in manufacturing systems. The c ase studies show that the TPOP method can be used easily to solve decision-making problems in manufacturing systems. Furthermore, the consistencies of the multi-criteria decision-making methods used in this study are analyzed using Spearman’s correlation coefficient values. TPOP method has the highest Spearman’s correlation value for three case studies.
References
- REFERENCES
- [1] Groover M. Automation, production systems, and computer-integrated manufacturing. 1st ed. India: Prentice Hall Press; 2007.
- [2] Rasmi SAB, Kazan C, Turkay M. A multi-criteria decision analysis to include environmental, social, and cultural issues in the sustainable aggregate pro-
duction plans. Comput Ind Eng 2019;132:348–360.[CrossRef]
- [3] Su TS, Lin YF. Fuzzy multi-objective procure-ment/production planning decision problems for recoverable manufacturing systems. J Manuf Syst 2015;37:396–
408. [CrossRef]
- [4] Boral S, Howard I, Chaturvedi SK, McKee K, Naikan VNA. A novel hybrid multi-criteria group decision making approach for failure mode and effect analysis: An
essential requirement for sustainable manufac-turing. Sustain Prod Consum 2020;21:14–32. [CrossRef]
- [5] Vazifehdan MN, Darestani SA. Green Logistics out-sourcing employing multi criteria decision making and quality function deployment in the petrochemi-cal
industry. Asian J Shipp Logist 2019;35:243–254.[CrossRef]
- [6] Ervural BC, Ervural B, Kabak O. A group deci-sion making approach for the evaluation of flex-ible manufacturing systems. IFAC-PapersOnLine 2016;49:1329–
1334. [CrossRef]
- [7] Pagone E, Salonitis K, Jolly M. Automatically weighted high-resolution mapping of multi-criteria decision analysis for sustainable manufacturing sys-tems. J
Clean Prod 2020;257:120272. [CrossRef]
- [8] Yu C, Matta A, Semeraro Q. Group decision making in manufacturing systems: An approach using spa-tial preference information and indifference zone. J
Manuf Syst 2020;55:109–125. [CrossRef]
- [9] Ghenai C, Albawab M, Bettayeb M. Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended
SWARA/ARAS hybrid method. Renew Energy 2020;146:580–597. [CrossRef]
- [10] Zhang H. Understanding the linkages: A dynamic sustainability assessment method and decision making in manufacturing systems. Procedia CIRP
2019;80:233–238. [CrossRef]
- [11] Sinha AK, Anand A. Development of sustainable supplier selection index for new product develop-ment using multi criteria decision making. J Clean Prod
2018;197:1587–1596. [CrossRef]
- [12] Stoycheva S, Marchese D, Paul C, Padoan S, Juhmani A, Linkov I. Multi-criteria decision analy-sis framework for sustainable manufacturing in automotive
industry. J Clean Prod 2018;187:257–272. [CrossRef]
- [13] Guo Y, Wang N, Xu ZY, Wu K. The internet of things-based decision support system for informa-tion processing in intelligent manufacturing using data mining technology. Mech Syst Signal Process 2020;142:106630. [CrossRef]
- [14] Kunath M, Winkler H. Integrating the Digital Twin of the manufacturing system into a decision support system for improving the order management pro-cess.
Procedia CIRP 2018;72:225–231. [CrossRef]
- [15] Li X, Fang Z, Yin C. A machine tool matching method in cloud manufacturing using Markov Decision Process and cross-entropy. Robot Comput Integr Manuf
2020;65:101968. [CrossRef]
- [16] Cheng H, Xu W, Ai Q, Liu Q, Zhou Z, Pham DT. Manufacturing capability assessment for human-robot collaborative disassembly based on multi-data fusion.
Procedia Manuf 2017;10:26–36. [CrossRef]
- [17] De Vin LJ, Holm M, Ng AHC. The Information Fusion JDL-U model as a reference model for Virtual Manufacturing. Robot Comput Integr Manuf 2010;26:629–638.
[CrossRef]
- [18] Wu Q, Lin W, Zhou L, Chen Y, Chen H. Enhancing multiple attribute group decision making flex-ibility based on information fusion technique and hesitant
Pythagorean fuzzy sets. Comput Ind Eng 2019;127:954–970. [CrossRef]
- [19] Yin Y, Zhang L, Liao W, Niu H, Chen F. A knowledge resources fusion method based on rough set theory for quality prediction. Comput Ind 2019;108:104–114. [CrossRef]
- [20] Ersoz F, Kabak M. A literature review of multiple criteria decision making methods at defence sector applications. Def Sci J 2010;9:97–125.
- [21] Kahraman C. Fuzzy Multi-Criteria Decision Making: Theory and Applications with Recent Developments. 1 st ed. Berlin: Springer; 2008. [CrossRef]
- [22] Besbes M, Affonso RC, Zolghadri M, Masmoudi F, Haddar M. Multi-criteria decision making for the selection of a performant manual workshop layout: a case
study. IFAC-PapersOnLine 2017;50:12404–12409. [CrossRef]
- [23] Bougrine A, Darmoul S, Hajri-Gabouj S. Topsis based multi-criteria reconfiguration of manufac-turing systems considering operational and ergo-nomic
indicators. In: Amor AB, editor. International Conference on Advanced Systems and Electric Technologies (IC_ASET); 2017 Jan 14-17; New Jersey: IEEE; 2017. pp. 329–334. [CrossRef]
- [24] Kumar S, Kumar S, Barman AG. Supplier selection using fuzzy TOPSIS multi criteria model for a small scale steel manufacturing unit. Procedia Comput Sci
2018;133:905–912. [CrossRef]
- [25] Liao CN, Kao HP. An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management. Expert Syst Appl 2011;38:10803–10811.
[CrossRef]
- [26] Ma J, Harstvedt JD, Jaradat R, Smith B. Sustainability driven multi-criteria project portfolio selection under the uncertain decision-making environment.
Comput Ind Eng 2020;140:106236. [CrossRef]
- [27] Ozcan T, Celebi N, Esnaf S. Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selec-tion
problem. Expert Syst Appl 2011;38:9773–9779.[CrossRef]
- [28] Peng C, Du H, Liao TW. A research on the cutting database system based on machining features and TOPSIS. Robot Comput Integr Manuf 2017;43:96–104. [CrossRef]
- [29] Yuvaraj N, Pradeep Kumar M. Multiresponse optimization of abrasive water jet cutting process parameters using TOPSIS approach. Mater ManufProcess
2015;30:882–889. [CrossRef]
- [30] Geng X, Liu Q. A hybrid service supplier selection approach based on variable precision rough set and VIKOR for developing product service system. Int J
Comput Integr Manuf 2015;28:1063–1076.
- [31] Lin XH, Feng YX, Tan JR, An XH. Product con-cept evaluation based on hybrid model of advanced DEMATEL-VIKOR algorithm. Comput Integr Manuf Syst 2011;17.
- [32] Singh S, Olugu EU, Musa SN, Mahat AB, Wong KY. Strategy selection for sustainable manufactur-ing with integrated AHP-VIKOR method under interval-
valued fuzzy environment. Int J Adv Manuf Technol 2016;84:547–563. [CrossRef]
- [33] Sasananan M, Narkhede BE, Gardas BB, Raut RD. Selection of third party logistics service provider using a multi-criteria decision making approach for
Indian cement manufacturing industries. Sci Technol Asia 2016;21:70–81.
- [34] Yadav V, Sharma MK. Multi-criteria supplier selec-tion model using the analytic hierarchy process approach. J Model Manag 2016;11:326–354. [CrossRef]
- [35] Kluczek A. An overall multi-criteria approach to sustainability assessment of manufacturing pro-cesses. Procedia Manuf 2017;8:136–143. [CrossRef]
- [36] Sevkli M. An application of the fuzzy ELECTRE method for supplier selection. Int J Prod Res 2010;48:3393–3405. [CrossRef]
- [37] Maniya KD, Bhatt MG. A multi-attribute selection of automated guided vehicle using the AHP/M-GRA technique. Int J Prod Res 2011;49:6107–6124.[CrossRef]
- [38] Sandeep M, Kumanan S, Vinodh S. Supplier selec-tion using combined AHP and GRA for a pump manufacturing industry. Int J Logist Syst Manag
2011;10:40–52. [CrossRef]
- [39] Badi IA, Abdulshahed AM, Shetwan AG. A case study of supplier selection for a steelmaking com-pany in Libya by using the Combinative Distance-based
Assessment (CODAS) model. Decis Mak Appl Manag Eng 2018;1:1–12. [CrossRef]
- [40] Mathew M, Sahu S. Comparison of new multi-crite-ria decision making methods for material handling equipment selection. Manag Sci Lett 2018;8:139–150. [CrossRef]
- [41] Triantaphyllou E. Multi-criteria decision making methods. Multi-criteria decision making methods: A comparative study. 1st ed. Boston: Springer; 2000. pp. 5–
21. [CrossRef]
- [42] de Farias Aires RF, Ferreira L. A new approach to avoid rank reversal cases in the TOPSIS method. Comput Ind Eng 2019;132:84–97. [CrossRef]
- [43] Buede DM, Maxwell DT. Rank disagreement: A comparison of multi‐criteria methodologies. J Multi‐Criteria Decis Anal 1995;4:1–21. [CrossRef]
- [44] Zanakis SH, Solomon A, Wishart N, Dublish S. Multi-attribute decision making: A simulation comparison of select methods. Eur J Oper Res 1998;107:507–529. [CrossRef]
- [45] Triantaphyllou E, Shu B. On the maximum num-ber of feasible ranking sequences in multi-cri-teria decision making problems. Eur J Oper Res 2001;130:665–678. [CrossRef]
- [46] Jan KH, Tung CT, Deng P. Rank reversal problem related to wash criterion in analytic hierarchy pro-cess (AHP). Afr J Bus Manag 2011;5:8301–8306.
[CrossRef]
- [47] Lin JSJ, Chou SY, Chouhuang WT, Hsu CP. Note on ‘Wash criterion in analytic hierarchy process. Eur J Oper Res 2008;185:444–447. [CrossRef]
- [48] Yuen KKF. Pairwise opposite matrix and its cog-nitive prioritization operators: comparisons with pairwise reciprocal matrix and analytic prioritiza-tion
operators. J Oper Res Soc 2012;63:322–338. [CrossRef]
- [49] Roszkowska E. Rank ordering criteria weighting methods–a comparative overview. Optimum Econ Stud 2013;5:14–33. [CrossRef]
- [50] Borda JD. Mémoire sur les élections au scru-tin. Histoire de l’Académie Royale des Sciences 1781;102:657–665.
- [51] Argentini A, Blanzieri E. Ranking aggregation based on belief function. In: Greco S, Bouchon-Meunier B, Coletti G, Fedrizzi M, Matarazzo B, Yager RR, editors.
International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems; 2012 Jul 9-13; Berlin: Springer; 2012. pp. 511–
520.
- [52] Kim Y, Chung ES. Assessing climate change vul-nerability with group multi-criteria decision mak-ing approaches. Clim Change 2013;121:301–315.
[CrossRef]
- [53] Condorcet MD. Essai sur l’application de l’analyse à la probabilité des décisions rendues à la pluralité des voix. 1785. [French]
- [54] Bhattacharya S, Raju V. A Condorcet Voting the-ory based AHP approach for MCDM problems,” Indones. J Electr Eng Comput Sci 2017;7:276–286.
[CrossRef]
- [55] Bairagi B, Dey B, Sarkar B, Sanyal SK. A De Novo multi-approaches multi-criteria decision making technique with an application in performance eval-
uation of material handling device. Comput Ind Eng 2015;87:267–282. [CrossRef]
- [56] Saad S, Kunhu N, Mohamed A. A fuzzy-AHP multi-criteria decision making model for procurement pro-cess. Int J Logist Syst Manag 2016;23:1–24.
[CrossRef]
- [57] Memari A, Dargi A, Jokar MRA, Ahmad R, Rahim ARA. Sustainable supplier selection: A multi-cri-teria intuitionistic fuzzy TOPSIS method. J Manuf Syst 2019;50:9–24. [CrossRef]
- [58] Shumaiza, Akram M, Al-Kenani AN, Alcantud JCR. Group decision-making based on the VIKOR method with trapezoidal bipolar fuzzy ınformation.
Symmetry 2019;11:1313. [CrossRef]
- [59] Akram M, Kahraman C, Zahid K. Group decision-making based on complex spherical fuzzy VIKOR approach. Knowl Based Syst 2021;216:106793.[CrossRef]
- [60] Hsu HM, Chen CT. Aggregation of fuzzy opin-ions under group decision making. Fuzzy Sets Syst 1996;79:279–285. [CrossRef]
- [61] Smolíková R, Wachowiak MP. Aggregation operators for selection problems. Fuzzy Sets Syst 2002;131:23–34. [CrossRef]
- [62] Tsiporkova E, Boeva V. Multi-step ranking of alter-natives in a multi-criteria and multi-expert decision making environment. Inf Sci 2006;176:2673–2697.
[CrossRef]
- [63] Huang YS, Li WH. A study on aggregation of TOPSIS ideal solutions for group decision-making. Group Decis Negot 2012;21:461–473. [CrossRef]
- [64] Maleki H, Zadeh AH. Comparison of the REMBRANDT system with the Wang and Elhag approach: A practical example of the rank reversal problem. African J Bus Manag 2012;6:459–473. [CrossRef]
- [65] Dede G, Kamalakis T, Sphicopoulos T. Convergence properties and practical estimation of the prob-ability of rank reversal in pairwise comparisons for
multi-criteria decision making problems. Eur J Oper Res 2015;241:458–468. [CrossRef]
- [66] Al Salem AA, Awasthi A. Investigating rank rever-sal in reciprocal fuzzy preference relation based on additive consistency: Causes and solutions. Comput
Ind Eng 2018;115:573–581. [CrossRef]
- [67] Mousavi-Nasab SH, Sotoudeh-Anvari A. A new multi-criteria decision making approach for sustain-able material selection problem: A critical study on
rank reversal problem. J Clean Prod 2018;182:466–484.[CrossRef]
- [68] Ma LC, Li HL. Using Gower Plots and Decision Balls to rank alternatives involving inconsistent prefer-ences. Decis Support Syst 2011;51:712–719. [CrossRef]
- [69] Abel E, Mikhailov L, Keane J. Inconsistency reduc-tion in decision making via multi-objective optimi-sation. Eur J Oper Res 2018;267:212–226. [CrossRef]
- [70] Dwork C, Kumar R, Naor M, Sivakumar D, Rank aggregation revisited. 2001.
- [71] Dwork C, Kumar R, Naor M, Sivakumar D. Rank aggregation methods for the web. In: Shen VY, Saito N, Lyu MR, editors. Proceedings of the 10th international
conference on World Wide Web; 2001 May 1-5; New York: Association for Computing Machinery; 2001. pp. 613–622. [CrossRef]
- [72] Jahan A, Ismail MY, Shuib S, Norfazidah D, Edwards KL. An aggregation technique for optimal decision-making in materials selection. Mater Des 2011;32:4918–
4924. [CrossRef]
- [73] Ding J, Han D, Dezert J, Yang Y. A new hierarchi-cal ranking aggregation method. Inf Sci 2018;453: 168–185. [CrossRef]
- [74] Telmoudi A, Salem C. Data fusion application from evidential databases as a support for decision mak-ing. Inf Softw Technol 2004;46:547–555.
[CrossRef]
- [75] Waltz EL, Buede DM. Data fusion and decision sup-port for command and control. IEEE Trans Syst Man Cybern 1986;16:865–879. [CrossRef]
- [76] Hall DL. An introduction to multisensor data fusion. Proc IEEE 1997;85:6–23. [CrossRef]
- [77] Zhang Y, Qiang J, Carl GL. Active information fusion for decision making under uncertainty. Proceedings of the Fifth International Conference on Information
Fusion; 2002 Jul 8-11; Annapolis: IEEE; 2002. pp. 643–650.
- [78] Goshtasby AA, Nikolov S. Image fusion: advances in the state of the art. Inf Fusion 2007;2:114–118. [CrossRef]
- [79] Khaleghi B, Khamis A, Karray FO, Razavi SN. Multisensor data fusion: A review of the state-of-the-art. Inf Fusion 2013;14:28–44. [CrossRef]
- [80] Ribeiro RA, Falcão A, Mora A, Fonseca JM. FIF: A fuzzy information fusion algorithm based on multi-criteria decision making. Knowl Based Syst 2014;58:23–
32. [CrossRef]
- [81] Paradis S, Breton R, Roy J. Data fusion in support of dynamic human decision making. Fusion99, 1999.
- [82] Samoylov VV. Data fusion systems: Principles and architecture for data processing in decision making system learning. Inform Autom 2002;1:69–83.
- [83] Nilsson M, Ziemke T. Information fusion: a deci-sion support perspective. 2007 10th International Conference on Information Fusion; 2007 Jul 9-12; Quebec: IEEE; 2007. pp. 1–8. [CrossRef]
- [84] Deng Y, Chan FTS, Wu Y, Wang D. A new linguis-tic MCDM method based on multiple-criterion data fusion. Expert Syst Appl 2011;38:6985–6993.[CrossRef]
- [85] Altuntas S, Dereli T. Facility layout design selec-tion by the technique of precise order preference. 7th International Research Meeting in Business and Management, Jul 11-12, 2016.
- [86] Dorfeshan Y, Mousavi SM, Mohagheghi V, Vahdani B. Selecting project-critical path by a new inter-val type-2 fuzzy decision methodology based on MULTIMOORA, MOOSRA and TPOP methods. Comput Ind Eng 2018;120:160–178. [CrossRef]
- [87] Rao RV. Decision making in the manufacturing environment: using graph theory and fuzzy mul-tiple attribute decision making methods. 2nd ed. London:
Springer Science & Business Media; 2013. [CrossRef]
- [88] Zar JH. Significance testing of the spearman rank correlation coefficient. J Am Stat Assoc 1972;67:578–580. [CrossRef]
- [89] Nie NH, Bent DH, HCH. SPSS: Satatistical pack-age for the social sciences. New York: McGraw-Hill, 1975.
- [90] Khare S, Bhandari A, Saurabh S, Arora A. ECG arrhythmia classification using spearman rank cor-relation and support vector machine. In: Deep K, Nagar A, Pant M, Bansal JC, editors. Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011); 2011 Dec 20- 22; Roorke, India: Springer: 2011. pp. 591–598.