Selection of robot for automated foundry operations using fuzzy multi-criteria decision making approaches

This paper employs three Fuzzy Multi-Criteria Decision Making (FMCDM) methodologies in the evaluation and selection of robots for automated foundry operations. In the methodologies, a Fuzzy Analytical Hierarchy Process (FAHP) is integrated individually with a Fuzzy Technique for Order Preference by Similarity to the Ideal Solution (FTOPSIS), a Fuzzy VIsekriterijumska optimizacija i KOmpromisno Resenje (FVIKOR) and a Complex PRoportional ASsessment method with the application of Grey systems theory (COPRAS-G). In each case, a FAHP is used to estimate the fuzzy weights of the selection criteria under consideration. FTOPSIS, FVIKOR and COPRAS-G are applied to evaluate as well as to select the robots. A real life problem of robots selection in foundry operation is cited to demonstrate and validate the applicability and potentiality of the employed methodologies. A comparative analysis of the results obtained by the methodologies is carried out. The study finds that the employed methodologies are useful, effective and sound surrogates for selecting the best robot in an FMCDM environment.

[1]  Mitsuo Gen,et al.  Genetic algorithm for robot selection and work station assignment problem , 1996 .

[2]  Bartholomew O. Nnaji.  Evaluation methodology for performance and system economics for robotic devices , 1988 .

[3]  Gwo-Hshiung Tzeng,et al.  Extended VIKOR method in comparison with outranking methods , 2007, Eur. J. Oper. Res..

[4]  Nourredine Boubekri,et al.  Development of an expert system for industrial robot selection , 1991 .

[5]  Caroline M. Eastman,et al.  Response: Introduction to fuzzy arithmetic: Theory and applications : Arnold Kaufmann and Madan M. Gupta, Van Nostrand Reinhold, New York, 1985 , 1987, Int. J. Approx. Reason..

[6]  Marc Roubens,et al.  Multiple criteria decision making , 1994 .

[7]  M. Bohanec,et al.  The Analytic Hierarchy Process , 2004 .

[8]  R. Venkata Rao,et al.  Selection, identification and comparison of industrial robots using digraph and matrix methods , 2006 .

[9]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[10]  Chon-Huat Goh,et al.  Analytic hierarchy process for robot selection , 1997 .

[11]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[12]  Hsuan-Shih Lee,et al.  Generalizing TOPSIS for fuzzy multiple-criteria group decision-making , 2007, Comput. Math. Appl..

[13]  ChenChen-Tung.  Extensions of the TOPSIS for group decision-making under fuzzy environment , 2000 .

[14]  J. Buckley,et al.  Fuzzy hierarchical analysis , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

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

[16]  Y. A. Tung,et al.  A revised weighted sum decision model for robot selection , 1996 .

[17]  Gwo-Hshiung Tzeng,et al.  Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS , 2004, Eur. J. Oper. Res..

[18]  Yi-Hsuan Chen,et al.  A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard , 2009, Expert Syst. Appl..

[19]  Y. Ku,et al.  Introduction to fuzzy arithmetic—theory and applications : Arnold Kaufmann and Madan M. Gupta. 351 pages, diagrams, figures. Van Nostrand Reinhold Company, New York, 1985. , 1986 .

[20]  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.

[21]  Gwo-Hshiung Tzeng,et al.  Multicriteria Planning of Post‐Earthquake Sustainable Reconstruction , 2002 .

[22]  Tien-Chin Wang,et al.  Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment , 2007, Expert Syst. Appl..

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

[24]  E. Ertugrul Karsak,et al.  Robot selection using an integrated approach based on quality function deployment and fuzzy regression , 2008 .

[25]  Cengiz Kahraman,et al.  Fuzzy multi-criteria evaluation of industrial robotic systems , 2007, Comput. Ind. Eng..

[26]  Mohammad Izadikhah,et al.  Extension of the TOPSIS method for decision-making problems with fuzzy data , 2006, Appl. Math. Comput..

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

[28]  Gwo-Hshiung Tzeng,et al.  Defuzzification within a Multicriteria Decision Model , 2003, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[29]  Gwo-Hshiung Tzeng,et al.  Fuzzy MCDM approach for selecting the best environment-watershed plan , 2011, Appl. Soft Comput..

[30]  Celik Parkan,et al.  Decision-making and performance measurement models with applications to robot selection , 1999 .

[31]  Shan-Huo Chen.  Ranking fuzzy numbers with maximizing set and minimizing set , 1985 .

[32]  Hsu-Shih Shih,et al.  Incremental analysis for MCDM with an application to group TOPSIS , 2008, Eur. J. Oper. Res..

[33]  Subir Kumar Saha,et al.  Attribute based specification, comparison and selection of a robot , 2004 .

[34]  Prasenjit Chatterjee,et al.  Selection of industrial robots using compromise ranking and outranking methods , 2010 .

[35]  Ram Rachamadugu,et al.  A closer look at the use of data envelopment analysis for technology selection , 1997 .

[36]  I-Shuo Chen,et al.  A hybrid MCDM model encompassing AHP and COPRAS-G methods for selecting company supplier in Iran , 2012 .

[37]  Moutaz Khouja,et al.  The use of data envelopment analysis for technology selection , 1995 .

[38]  T. Chu,et al.  A Fuzzy TOPSIS Method for Robot Selection , 2003 .

[39]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[40]  David E. Booth,et al.  Fuzzy clustering procedure for evaluation and selection of industrial robots , 1995 .

[41]  Cheng-Wei Lin,et al.  Multi-criteria analysis of alternative-fuel buses for public transportation , 2005 .

[42]  Ram Narasimhan,et al.  An Experimental Evaluation of Articulation of Preferences in Multiple Criterion Decision‐Making (MCDM) Methods , 1988 .

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

[44]  V. P. Agrawal,et al.  Computer aided robot selection: the ‘multiple attribute decision making’ approach , 1991 .

[45]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .