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APPLYING EDAS AS AN APPLICABLE MCDM METHOD FOR INDUSTRIAL ROBOT SELECTION

Yıl 2019, Cilt: 37 Sayı: 3, 779 - 796, 01.09.2020

Öz

In order to stay an actual competitor in today’s environment, it is essential for manufacturing organizations to make decisions promptly and correctly. In the real-time manufacturing decision making problems, some alternatives are more likely to be evaluated with respect to multiple conflicting criteria. Several multi-criteria decision-making (MCDM) methods have been available to help decision makers in choosing the best decisive course of actions. The aim of the study is to apply an efficient and relatively new method called Evaluation based on Distance from Average Solution (EDAS) as an applicable and useful MCDM method for robot selection problem (RSP). In order to examine the feasibility and effectiveness of the presented method, several numerical examples from the literature are considered. Comparing with other methods especially MCDM methods given in the literature for the industrial RSPs, the Spearman’s rank correlations analysis indicates that this method is capable of accurately ranking selected robots.

Kaynakça

  • [1] Rao R.V., (2013) Decision Making in Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods, Springer Series in Advanced Manufacturing Volume 2, London: Springer-Verlag.
  • [2] Chatterjee P., Athawale V.M., Chakraborty S., (2010) Selection of industrial robot using compromise ranking and outranking methods, Robotics and Computer Integrating Manufacturing 26(5), 483-489.
  • [3] Rao R.V., (2007) Decision Making in the Manufacturing Environment: Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods. London: Springer.
  • [4] Keshavarz Ghorabaee M., Zavadskas E.K., Olfat L., Turskis Z., (2015) Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS), Informatica 26(3), 435-451.
  • [5] Stević Ž., Vasiljević M., Vesković S., (2016) Evaluation in logistics using combined AHP and EDAS method. XLIII International Symposium on Operational Research, Serbia.
  • [6] Turskis Z., Juodagalvienė B., (2016) A novel hybrid multicriteria decision-making model to assess a stairs shape for dwelling houses, Journal of Civil Engineering and Management 22(8), 1078-1087.
  • [7] Keshavarz Ghorabaee M., Zavadskas E.K., Amiri M., Turskis Z., (2016) Extended EDAS method for fuzzy multi-criteria decision-making: An application to supplier selection, International Journal of Computers Communications & Control 11(3), 358-371.
  • [8] Kahraman C., Keshavarz Ghorabaee M., Zavadskas E.K., Cevik Onar S., Yazdani M., Oztaysi B., (2017) Intuitionistic fuzzy EDAS method: An application to solid waste disposal site selection, Journal of Environmental Engineering and Landscape Management 25(1), 1-12.
  • [9] Keshavarz Ghorabaee M., Amiri M., Zavadskas E.K., Turskis Z., (2017a) Multi-criteria group decision-making using an extended EDAS method with interval type-2 fuzzy sets. E&M Ekonomie a Management 20(1), 48–68.
  • [10] Keshavarz Ghorabaee M., Amiri M., Zavadskas E.K., Turskis Z., Antucheviciene J., (2017b) Stochastic EDAS method for multi-criteria decision-making with normally distributed data, Journal of Intelligent & Fuzzy Systems 33(3), 1627-1638.
  • [11] Peng X., Liu C., (2017) Algorithms for neutrosophic soft decision making based on EDAS, new similarity measure and level soft set, Journal of Intelligent & Fuzzy Systems 32(1), 955-968.
  • [12] Stanujkic D., Zavadskas E.K., Keshavarz Ghorabaee M., Turskis Z., (2017) An extension of the EDAS method based on the use of interval grey numbers, Studies in Informatics and Control 26(1), 5-12.
  • [13] Seidmann A., Arbel A., Shapira R., (1984) A two-phase analytic approach to robotic system design, Robotics and Computer-Integrated Manufacturing 1(2), 181-190.
  • [14] Jones M.S., Malmborg C.J., Agee M.H., (1985) Decision support system used for robot selection, Industrial Engineering 17, 66-73.
  • [15] Nnaji B.O., (1988) Evaluation methodology for performance and system economics for robotic devices, Computers and Industrial Engineering 14, 27-39.
  • [16] Nnaji B.O., Yannacopoulou M., (1989) A utility theory based robot selection and evaluation for electronics assembly, Computers & Industrial Engineering 14(4), 477-493.
  • [17] Agrawal V.P., Kohli V., Gupta S., (1991) Computer aided robot selection: the multiple attribute decision making approach, International Journal of Production Research 29(8), 1629-1644.
  • [18] Boubekri N., Sahoui M., Lakrib C., (1991) Development of an expert system for industrial robot selection, Computers & Industrial Engineering 20, 119-127.
  • [19] Khouja M., Offodile O.F., (1994) The industrial robots selection problem: literature review and directions for future research, IIE Transactions 26(4), 50-61.
  • [20] Khouja M., (1995). The use of data envelopment analysis for technology selection, Computers & Industrial Engineering 28(1), 123-132.
  • [21] Baker R.C., Talluri S., (1996) A closer look at the use of data envelopment analysis for technology selection, Computers & Industrial Engineering 32(1), 101-108.
  • [22] Goh C.-H., Tung Y.C.A., Cheng C.H., (1996) A revised weighted sum decision model for robot selection, Computers & Industrial Engineering 30, 193-199.
  • [23] Goh C.H., (1997) Analytic Hierarchy Process for robot selection, Journal of Manufacturing Systems 16(5), 381-386.
  • [24] Karsak E.E., (1998) A two-phase robot selection procedure, Production Planning & Control 9(7), 675-684.
  • [25] Parkan C., Wu M. L., (1999) Decision making and performance measurement models with application to robot selection, Computers & Industrial Engineering 36, 503-523.
  • [26] Braglia M., Petroni A., (1999) Evaluating and selecting investments in industrial robot, International Journal of Production Research 37(18), 4157-4178.
  • [27] Talluri S., Yoon K.P., (2000) A cone-ratio DEA approach for AMT justification, International Journal of Production Economics 66(2), 119-129.
  • [28] Ghrayeb O., Phojanamongkolkij N., Marcellus R., Zhao W., (2004) A practical framework to evaluate and select robots for assembly operations, Journal of Advanced Manufacturing Systems 3(2), 151-167.
  • [29] Bhangale P.P., Agrawal V.P., Saha S.K., (2004) Attribute based specification, comparison and selection of a robot, Mechanism and Machine Theory 39, 1345-1366.
  • [30] Karsak E.E., Ahiska S.S., (2005) Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection, International Journal of Production Research 43(8), 1537-1554.
  • [31] Bhattacharya A., Sarkar B., Mukherjee S.K., (2005) Integrating AHP with QFD for robot selection under requirement perspective, International Journal of Production Research 43(17), 3671-3685.
  • [32] Rao R.V., Padmanabhan K.K., (2006) Selection, identification and comparison of industrial robots using digraph and matrix methods, Robotics and Computer-Integrated Manufacturing 22, 373-383.
  • [33] Shih H.-S., (2008) Incremental analysis for MCDM with an application to group TOPSIS, European Journal of Operational Research 186(2), 720-734.
  • [34] Kumar R., Garg R.K., (2010) Optimal selection of robots by using distance based approach method, Robotics and Computer-Integrated Manufacturing 26(5), 500-506.
  • [35] Chakraborty S. (2011) Application of the MOORA method for decision making in manufacturing environment, International Journal of Advanced Manufacturing Technology 54(9/12), 1155-1166.
  • [36] Kentli A., Kar A.K., (2011) A satisfaction function and distance measure based multi-criteria robot selection procedure, International Journal of Production Research 49, 5821–5832.
  • [37] Rao R.V., Patel B.K., Parnichkun M., (2011) Industrial robot selection using a novel decision making method considering objective and subjective preferences, Robotics and Autonomous Systems 59(6), 367-375.
  • [38] Alinezhad A., Makui A., Mavi R.K., Zohrehbandian M., (2011) An MCDM-DEA approach for technology selection, Journal of Industrial Engineering International 7(12), 32-38.
  • [39] Athawale V.M., Chakraborty S., (2011) A comparative study on the ranking performance of some multi-criteria decision-making methods for industrial robot selection, International Journal of Industrial Engineering Computations 2(4), 831-850.
  • [40] Koulouriotis D.E., Ketipi M. K., (2011) A fuzzy digraph method for robot evaluation and selection, Expert Systems with Applications 38(9), 11901-11910.
  • [41] Bairagi B., Balaram D., Sarkar B., Sanyal S., (2012) A Novel Mutiplicative Model of Multi Criteria Analysis for Robot Selection, International Journal on Soft Computing, Artificial Intelligence and Applications 1(3), 1-9.
  • [42] Mondal S., Chakraborty S., (2013) A solution to robot selection problems using data envelopment analysis, International Journal of Industrial Engineering Computation 4(3), 355-372.
  • [43] Chakraborty S., Zavadskas E.K., (2014) Applications of WASPAS Method in Manufacturing Decision Making, Informatica 25(1), 1-20.
  • [44] Chakraborty S., Zavadskas E.K., Antucheviciene J., (2015) Applications of WASPAS Method as A Multi-Criteria Decision-Making Tool, Economic Computation and Economic Cybernetics Studies and Research 49(1), 5-22.
  • [45] Koulouriotis D.E., Ketipi M.K., (2014) Robot evaluation and selection Part A: an integrated review and annotated taxonomy, The International Journal of Advanced Manufacturing Technology 71, 1371-1394.
  • [46] Şenyiğit E., Demirel B., (2018) The selection of material in dental implant with entropy based simple additive weighting and analytic hierarchy process methods, Sigma Journal of Engineering and Natural Sciences 36(3), 2018, 731-740.
  • [47] Sen D.K., Datta S., Patel S.K., Mahapatra S.S., (2015) Multi-criteria decision making towards selection of industrial robot: Exploration of PROMETHEE II method, Benchmarking: An International Journal 22(3), 465-487.
  • [48] Imany M.M., Shlesinger R. J., (1989) Decision models for robot selection: a comparison of ordinary least squares and linear goal programming methods, Decision Sciences 20(1), 40-53.
Yıl 2019, Cilt: 37 Sayı: 3, 779 - 796, 01.09.2020

Öz

Kaynakça

  • [1] Rao R.V., (2013) Decision Making in Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods, Springer Series in Advanced Manufacturing Volume 2, London: Springer-Verlag.
  • [2] Chatterjee P., Athawale V.M., Chakraborty S., (2010) Selection of industrial robot using compromise ranking and outranking methods, Robotics and Computer Integrating Manufacturing 26(5), 483-489.
  • [3] Rao R.V., (2007) Decision Making in the Manufacturing Environment: Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods. London: Springer.
  • [4] Keshavarz Ghorabaee M., Zavadskas E.K., Olfat L., Turskis Z., (2015) Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS), Informatica 26(3), 435-451.
  • [5] Stević Ž., Vasiljević M., Vesković S., (2016) Evaluation in logistics using combined AHP and EDAS method. XLIII International Symposium on Operational Research, Serbia.
  • [6] Turskis Z., Juodagalvienė B., (2016) A novel hybrid multicriteria decision-making model to assess a stairs shape for dwelling houses, Journal of Civil Engineering and Management 22(8), 1078-1087.
  • [7] Keshavarz Ghorabaee M., Zavadskas E.K., Amiri M., Turskis Z., (2016) Extended EDAS method for fuzzy multi-criteria decision-making: An application to supplier selection, International Journal of Computers Communications & Control 11(3), 358-371.
  • [8] Kahraman C., Keshavarz Ghorabaee M., Zavadskas E.K., Cevik Onar S., Yazdani M., Oztaysi B., (2017) Intuitionistic fuzzy EDAS method: An application to solid waste disposal site selection, Journal of Environmental Engineering and Landscape Management 25(1), 1-12.
  • [9] Keshavarz Ghorabaee M., Amiri M., Zavadskas E.K., Turskis Z., (2017a) Multi-criteria group decision-making using an extended EDAS method with interval type-2 fuzzy sets. E&M Ekonomie a Management 20(1), 48–68.
  • [10] Keshavarz Ghorabaee M., Amiri M., Zavadskas E.K., Turskis Z., Antucheviciene J., (2017b) Stochastic EDAS method for multi-criteria decision-making with normally distributed data, Journal of Intelligent & Fuzzy Systems 33(3), 1627-1638.
  • [11] Peng X., Liu C., (2017) Algorithms for neutrosophic soft decision making based on EDAS, new similarity measure and level soft set, Journal of Intelligent & Fuzzy Systems 32(1), 955-968.
  • [12] Stanujkic D., Zavadskas E.K., Keshavarz Ghorabaee M., Turskis Z., (2017) An extension of the EDAS method based on the use of interval grey numbers, Studies in Informatics and Control 26(1), 5-12.
  • [13] Seidmann A., Arbel A., Shapira R., (1984) A two-phase analytic approach to robotic system design, Robotics and Computer-Integrated Manufacturing 1(2), 181-190.
  • [14] Jones M.S., Malmborg C.J., Agee M.H., (1985) Decision support system used for robot selection, Industrial Engineering 17, 66-73.
  • [15] Nnaji B.O., (1988) Evaluation methodology for performance and system economics for robotic devices, Computers and Industrial Engineering 14, 27-39.
  • [16] Nnaji B.O., Yannacopoulou M., (1989) A utility theory based robot selection and evaluation for electronics assembly, Computers & Industrial Engineering 14(4), 477-493.
  • [17] Agrawal V.P., Kohli V., Gupta S., (1991) Computer aided robot selection: the multiple attribute decision making approach, International Journal of Production Research 29(8), 1629-1644.
  • [18] Boubekri N., Sahoui M., Lakrib C., (1991) Development of an expert system for industrial robot selection, Computers & Industrial Engineering 20, 119-127.
  • [19] Khouja M., Offodile O.F., (1994) The industrial robots selection problem: literature review and directions for future research, IIE Transactions 26(4), 50-61.
  • [20] Khouja M., (1995). The use of data envelopment analysis for technology selection, Computers & Industrial Engineering 28(1), 123-132.
  • [21] Baker R.C., Talluri S., (1996) A closer look at the use of data envelopment analysis for technology selection, Computers & Industrial Engineering 32(1), 101-108.
  • [22] Goh C.-H., Tung Y.C.A., Cheng C.H., (1996) A revised weighted sum decision model for robot selection, Computers & Industrial Engineering 30, 193-199.
  • [23] Goh C.H., (1997) Analytic Hierarchy Process for robot selection, Journal of Manufacturing Systems 16(5), 381-386.
  • [24] Karsak E.E., (1998) A two-phase robot selection procedure, Production Planning & Control 9(7), 675-684.
  • [25] Parkan C., Wu M. L., (1999) Decision making and performance measurement models with application to robot selection, Computers & Industrial Engineering 36, 503-523.
  • [26] Braglia M., Petroni A., (1999) Evaluating and selecting investments in industrial robot, International Journal of Production Research 37(18), 4157-4178.
  • [27] Talluri S., Yoon K.P., (2000) A cone-ratio DEA approach for AMT justification, International Journal of Production Economics 66(2), 119-129.
  • [28] Ghrayeb O., Phojanamongkolkij N., Marcellus R., Zhao W., (2004) A practical framework to evaluate and select robots for assembly operations, Journal of Advanced Manufacturing Systems 3(2), 151-167.
  • [29] Bhangale P.P., Agrawal V.P., Saha S.K., (2004) Attribute based specification, comparison and selection of a robot, Mechanism and Machine Theory 39, 1345-1366.
  • [30] Karsak E.E., Ahiska S.S., (2005) Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection, International Journal of Production Research 43(8), 1537-1554.
  • [31] Bhattacharya A., Sarkar B., Mukherjee S.K., (2005) Integrating AHP with QFD for robot selection under requirement perspective, International Journal of Production Research 43(17), 3671-3685.
  • [32] Rao R.V., Padmanabhan K.K., (2006) Selection, identification and comparison of industrial robots using digraph and matrix methods, Robotics and Computer-Integrated Manufacturing 22, 373-383.
  • [33] Shih H.-S., (2008) Incremental analysis for MCDM with an application to group TOPSIS, European Journal of Operational Research 186(2), 720-734.
  • [34] Kumar R., Garg R.K., (2010) Optimal selection of robots by using distance based approach method, Robotics and Computer-Integrated Manufacturing 26(5), 500-506.
  • [35] Chakraborty S. (2011) Application of the MOORA method for decision making in manufacturing environment, International Journal of Advanced Manufacturing Technology 54(9/12), 1155-1166.
  • [36] Kentli A., Kar A.K., (2011) A satisfaction function and distance measure based multi-criteria robot selection procedure, International Journal of Production Research 49, 5821–5832.
  • [37] Rao R.V., Patel B.K., Parnichkun M., (2011) Industrial robot selection using a novel decision making method considering objective and subjective preferences, Robotics and Autonomous Systems 59(6), 367-375.
  • [38] Alinezhad A., Makui A., Mavi R.K., Zohrehbandian M., (2011) An MCDM-DEA approach for technology selection, Journal of Industrial Engineering International 7(12), 32-38.
  • [39] Athawale V.M., Chakraborty S., (2011) A comparative study on the ranking performance of some multi-criteria decision-making methods for industrial robot selection, International Journal of Industrial Engineering Computations 2(4), 831-850.
  • [40] Koulouriotis D.E., Ketipi M. K., (2011) A fuzzy digraph method for robot evaluation and selection, Expert Systems with Applications 38(9), 11901-11910.
  • [41] Bairagi B., Balaram D., Sarkar B., Sanyal S., (2012) A Novel Mutiplicative Model of Multi Criteria Analysis for Robot Selection, International Journal on Soft Computing, Artificial Intelligence and Applications 1(3), 1-9.
  • [42] Mondal S., Chakraborty S., (2013) A solution to robot selection problems using data envelopment analysis, International Journal of Industrial Engineering Computation 4(3), 355-372.
  • [43] Chakraborty S., Zavadskas E.K., (2014) Applications of WASPAS Method in Manufacturing Decision Making, Informatica 25(1), 1-20.
  • [44] Chakraborty S., Zavadskas E.K., Antucheviciene J., (2015) Applications of WASPAS Method as A Multi-Criteria Decision-Making Tool, Economic Computation and Economic Cybernetics Studies and Research 49(1), 5-22.
  • [45] Koulouriotis D.E., Ketipi M.K., (2014) Robot evaluation and selection Part A: an integrated review and annotated taxonomy, The International Journal of Advanced Manufacturing Technology 71, 1371-1394.
  • [46] Şenyiğit E., Demirel B., (2018) The selection of material in dental implant with entropy based simple additive weighting and analytic hierarchy process methods, Sigma Journal of Engineering and Natural Sciences 36(3), 2018, 731-740.
  • [47] Sen D.K., Datta S., Patel S.K., Mahapatra S.S., (2015) Multi-criteria decision making towards selection of industrial robot: Exploration of PROMETHEE II method, Benchmarking: An International Journal 22(3), 465-487.
  • [48] Imany M.M., Shlesinger R. J., (1989) Decision models for robot selection: a comparison of ordinary least squares and linear goal programming methods, Decision Sciences 20(1), 40-53.
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Articles
Yazarlar

Neşe Yalçın Bu kişi benim 0000-0002-9489-5401

Nuşin Uncu Bu kişi benim 0000-0003-3030-3363

Yayımlanma Tarihi 1 Eylül 2020
Gönderilme Tarihi 16 Eylül 2018
Yayımlandığı Sayı Yıl 2019 Cilt: 37 Sayı: 3

Kaynak Göster

Vancouver Yalçın N, Uncu N. APPLYING EDAS AS AN APPLICABLE MCDM METHOD FOR INDUSTRIAL ROBOT SELECTION. SIGMA. 2020;37(3):779-96.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/