Fuzzy Applications of FUCOM Method in Manufacturing Environment

Conventional manufacturing methods are limited in the machining of newly developed high strength, precision / brittle and complex shaped parts. Non-conventional manufacturing methods are required to machine such parts. Choosing the most suitable manufacturing method for the part is a vital decision-making problem and the solution of this problem is very important for today's manufacturers. In this study, three different Full Consistency Method (FUCOM) methods were combined with fuzzy Technique for Order Preference by Similarity to Ideal Solution method (fuzzy TOPSIS) and fuzzy weighted aggregated sum product assessment (fuzzy WASPAS) techniques. In order to test these developed methods, the selection of non-traditional manufacturing methods from the literature was taken as a case study. It is seen that the model produced successful results.

[1]  MILOŠ MADIĆ,et al.  NON-CONVENTIONAL MACHINING PROCESSES SELECTION USING MULTI-OBJECTIVE OPTIMIZATION ON THE BASIS OF RATIO ANALYSIS METHOD , 2015 .

[2]  Evangelos Triantaphyllou,et al.  Development and evaluation of five fuzzy multiattribute decision-making methods , 1996, Int. J. Approx. Reason..

[3]  Radko Mesiar,et al.  Hesitant L ‐Fuzzy Sets , 2017, Int. J. Intell. Syst..

[4]  Fei Ye,et al.  An extended TOPSIS model based on the Possibility theory under fuzzy environment , 2014, Knowl. Based Syst..

[5]  Inci Batmaz,et al.  A review of data mining applications for quality improvement in manufacturing industry , 2011, Expert Syst. Appl..

[6]  Wade D. Cook,et al.  Distance-based and ad hoc consensus models in ordinal preference ranking , 2006, Eur. J. Oper. Res..

[7]  Dragan Pamucar,et al.  A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM) , 2018, Symmetry.

[8]  P. Goodwin,et al.  Weight approximations in multi-attribute decision models , 2002 .

[9]  S. Boral,et al.  A case-based reasoning approach for non-traditional machining processes selection , 2016 .

[10]  Shankar Chakraborty,et al.  Application of fuzzy axiomatic design principles for selection of non-traditional machining processes , 2016 .

[11]  Charbel José Chiappetta Jabbour,et al.  Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company , 2014, Eur. J. Oper. Res..

[12]  Jin Qi,et al.  An integrated AHP and VIKOR for design concept evaluation based on rough number , 2015, Adv. Eng. Informatics.

[13]  Mustafa Yurdakul,et al.  USAGE OF FUZZY MULTI CRITERIA DECISION MAKING METHODS IN SELECTION OF NONTRADITIONAL MANUFACTURING METHODS , 2014 .

[14]  Marina Bosch,et al.  Fuzzy Multiple Attribute Decision Making Methods And Applications , 2016 .

[15]  R. Yager ON THE THEORY OF BAGS , 1986 .

[16]  Yu-Jie Wang,et al.  The evaluation of financial performance for Taiwan container shipping companies by fuzzy TOPSIS , 2014, Appl. Soft Comput..

[17]  Jie Lu,et al.  An Integrated Group Decision-Making Method Dealing with Fuzzy Preferences for Alternatives and Individual Judgments for Selection Criteria , 2003 .

[18]  J. Dombi,et al.  A method for determining the weights of criteria: the centralized weights , 1986 .

[19]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[20]  Martin Weber,et al.  Behavioral influences on weight judgments in multiattribute decision making , 1993 .

[21]  Ta-Chung Chu,et al.  Facility Location Selection Using Fuzzy TOPSIS Under Group Decisions , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[22]  Christer Carlsson,et al.  Fuzzy multiple criteria decision making: Recent developments , 1996, Fuzzy Sets Syst..

[23]  Zeshui Xu,et al.  Hesitant Fuzzy Sets Theory , 2014, Studies in Fuzziness and Soft Computing.

[24]  Lazim Abdullah,et al.  Fuzzy Multi Criteria Decision Making and its Applications: A Brief Review of Category , 2013 .

[25]  Jurgita Antucheviciene,et al.  A Hybrid Model Based on Fuzzy AHP and Fuzzy WASPAS for Construction Site Selection , 2015, Int. J. Comput. Commun. Control.

[26]  Francisco Herrera,et al.  Hesitant Fuzzy Linguistic Term Sets for Decision Making , 2012, IEEE Transactions on Fuzzy Systems.

[27]  Andrew Kusiak,et al.  Data Mining in Manufacturing: A Review , 2006 .

[28]  Chie-Bein Chen,et al.  An Approach for Solving Fuzzy MADM Problems , 1997, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[29]  Rita Almeida Ribeiro Fuzzy multiple attribute decision making: A review and new preference elicitation techniques , 1996, Fuzzy Sets Syst..

[30]  Ksenija Mandic,et al.  Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods , 2014 .

[31]  Jurgita Antucheviciene,et al.  Hybrid multiple criteria decision-making methods: a review of applications for sustainability issues , 2016 .

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

[33]  Sheng-Hshiung Tsaur,et al.  The evaluation of airline service quality by fuzzy MCDM. , 2002 .

[34]  Chen-Tung Chen,et al.  Extensions of the TOPSIS for group decision-making under fuzzy environment , 2000, Fuzzy Sets Syst..