A SWARA-CoCoSo-Based Approach for Spray Painting Robot Selection

Kumar, Vidyapati; Kalita, Kanak; Chatterjee, Prasenjit; Zavadskas, Edmundas Kazimieras; Chakraborty, Shankar

doi:10.15388/21-INFOR466

Informatica

A SWARA-CoCoSo-Based Approach for Spray Painting Robot Selection

Volume 33, Issue 1 (2022), pp. 35–54

Vidyapati Kumar Kanak Kalita Prasenjit Chatterjee Edmundas Kazimieras Zavadskas Shankar Chakraborty

https://doi.org/10.15388/21-INFOR466

Pub. online: 3 December 2021 Type: Research Article

Open Access

Received
1 September 2021

Accepted
1 November 2021

Published
3 December 2021

Abstract

In order to avoid working in a constrained hazardous environment, manual spray painting operation is gradually being replaced by automated robotic systems in many manufacturing industries. Application of spray painting robots ensures defect-free painting of dissimilar components with higher repeatability, flexibility, productivity, reduced cycle time and minimum wastage of paint. Due to availability of a large number of viable options in the market, selection of a spray painting robot suitable for a given application poses a great problem. Thus, this paper proposes the integrated application of step-wise weight assessment ratio analysis (SWARA) and combined compromise solution (CoCoSo) methods to identify the most apposite spray painting robot for an automobile industry based on seven evaluation criteria (payload, mass, speed, repeatability, reach, cost and power consumption). The SWARA method identifies cost as the most significant criterion based on a set preference order, whereas, Fanuc P-350iA/45 is selected as the best spray painting robot by CoCoSo method. The derived ranking results are also contrasted with other popular multi-criteria decision making (MCDM) techniques (TOPSIS, VIKOR, COPRAS, PROMETHEE and MOORA) and subjective criteria weighting methods (AHP, PIPRECIA, BWM and FUCOM). High degrees of similarity in the ranking patterns between the adopted approach and other MCDM techniques prove its effectiveness in solving complex industrial robot selection problems. This integrated approach is proved to be quite robust being almost unaffected by the changing values of the corresponding tuning parameter in low-dimensional MCDM problems.

References

Agarwal, S., Chakraborty, S., Prasad, K., Chakraborty, S. (2021). A rough multi-attributive border approximation area comparison approach for arc welding robot selection. Jordan Journal of Mechanical & Industrial Engineering, 15(2), 169–180.

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.

Athawale, V.M., Chatterjee, P., Chakraborty, S. (2012). Selection of industrial robots using compromise ranking method. International Journal of Industrial and Systems Engineering, 11(1–2), 3–15.

Azimi, M., Taghizadeh, H., Farahmand, N., Pourmahmoud, J. (2014). Selection of industrial robots using the Polygons area method. International Journal of Industrial Engineering Computations, 5(4), 631–646.

Bader, F., Rahimifard, S. (2020). A methodology for the selection of industrial robots in food handling. Innovative Food Science & Emerging Technologies, 64, 102379.

Behzadian, M., Otaghsara, S.K., Yazdani, M., Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051–13069.

Brans, J.-P., Vincke, P. (1985). A preference ranking organisation method: (the PROMETHEE method for multiple criteria decision-making). Management Science, 31(6), 647–656.

Brauers, W.K.M., Zavadskas, E.K., Peldschus, F., Turskis, Z. (2008). Multi-objective decision-making for road design. Transport, 23(3), 183–193.

Breaz, R.E., Bologa, O., Racz, S.G. (2017). Selecting industrial robots for milling applications using AHP. Procedia Computer Science, 122, 346–353.

Chatterjee, P., Athawale, V.M., Chakraborty, S. (2010). Selection of industrial robots using compromise ranking and outranking methods. Robotics and Computer-Integrated Manufacturing, 26(5), 483–489.

Chodha, V., Dubey, R., Kumar, R., Singh, S., Kaur, S. (2021). Selection of industrial arc welding robot with TOPSIS and Entropy MCDM techniques. Materials Today: Proceedings.

Cui, Y., Liu, W., Rani, P., Alrasheedi, M. (2021). Internet of Things (IoT) adoption barriers for the circular economy using Pythagorean fuzzy SWARA-CoCoSo decision-making approach in the manufacturing sector. Technological Forecasting and Social Change, 171, 120951.

Ghoushchi, S. Jafarzadeh, Ab Rahman, M.N., Raeisi, D., Osgooei, E., Jafarzadeh Ghoushji, M. (2020). Integrated decision-making approach based on SWARA and GRA methods for the prioritization of failures in solar panel systems under Z-information. Symmetry, 12(2), 310.

Goswami, S.S.S., Behera, D.K.K., Afzal, A., Razak Kaladgi, A., Khan, S.A.A., Rajendran, P., Subbiah, R., Asif, M. (2021). Analysis of a robot selection problem using two newly developed hybrid MCDM models of TOPSIS-ARAS and COPRAS-ARAS. Symmetry, 13(8), 1331.

Gujela, O.P., Gujela, V., Gujela, D.P. (2015). Six DOF spray painting robot analysis. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 4, 7721–7731.

Haugan, K. (1974). Spray painting robots: advanced paint shop automation. Industrial Robot: An International Journal. 1(6), 270–272.

Karabašević, D., Stanujkić, D., Urošević, S. (2015). The MCDM model for personnel selection based on SWARA and ARAS methods. Management Journal of Sustainable Business and Management Solutions in Emerging Economies, 20(77), 43–52.

Karande, P., Zavadskas, E., Chakraborty, S. (2016). A study on the ranking performance of some MCDM methods for industrial robot selection problems. International Journal of Industrial Engineering Computations, 7(3), 399–422.

Keršuliene, V., Zavadskas, E.K., Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243–258.

Khouja, M., Offodile, F. (1994). The industrial robots selection problem: literature review and directions for future research. IIE Transactions, 26(4), 50–61.

Kumar, J., Verma, R.K. (2021). A novel methodology of Combined Compromise Solution and Principal Component Analysis (CoCoSo-PCA) for machinability investigation of graphene nanocomposites. CIRP Journal of Manufacturing Science and Technology, 33, 143–157.

Lai, H., Liao, H., Wen, Z., Zavadskas, E.K., Al-Barakati, A. (2020). An improved CoCoSo method with a maximum variance optimization model for cloud service provider selection. Inzinerine ekonomika–Engineering Economics, 31(4), 411–424.

Li, X., Wang, K., Liu, L., Xin, J., Yang, H., Gao, C. (2011). Application of the entropy weight and TOPSIS method in safety evaluation of coal mines. Procedia Engineering, 26, 2085–2091.

Majeed, R.A., Breesam, H.K. (2021). Application of SWARA technique to find criteria weights for selecting landfill site in Baghdad governorate. In: IOP Conference Series: Materials Science and Engineering, Vol. 1090, p. 012045. IOP Publishing.

Mathew, M., Sahu, S., Upadhyay, A.K. (2017). Effect of normalization techniques in robot selection using weighted aggregated sum product assessment. International Journal of Innovative Research and Advanced Studies, 4(2), 59–63.

Mishra, A.R., Rani, P. (2021). Assessment of sustainable third party reverse logistic provider using the single-valued neutrosophic combined compromise solution framework. Cleaner and Responsible Consumption, 2, 100011.

Mondal, S., Kuila, S., Singh, A.K., Chatterjee, P. (2017). A complex proportional assessment method-based framework for industrial robot selection problem. International Journal of Engineering Science, 3(2), 368–378.

Muzan, I.W., Faisal, T., Al-Assadi, H., Iwan, M. (2012). Implementation of industrial robot for painting applications. Procedia Engineering, 41, 1329–1335.

Omoniwa, B. (2014). A solution to multi criteria robot selection problems using grey relational analysis. International Journal of Computer and Information Technology, 3(2), 328–332.

Opricovic, S., Tzeng, G.-H. (2004). Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455.

Pamučar, D., Stević, Ž., Sremac, S. (2018). A new model for determining weight coefficients of criteria in mcdm models: full consistency method (fucom). Symmetry, 10(9), 393.

Rani, P., Ali, J., Krishankumar, R., Mishra, A.R., Cavallaro, F., Ravichandran, K.S. (2021). An integrated single-valued neutrosophic combined compromise solution methodology for renewable energy resource selection problem. Energies, 14(15), 4594.

Rashid, T., Ali, A., Chu, Y.-M. (2021). Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection. Plos One, 16(2), 0246738.

Rehman, U. Ateekh, Ahamri, A. Al (2013a). Evaluation of alternative industrial robots using AHP and ELECTRE: a comparative study. International Journal of Industrial and Systems Engineering, 14(1), 58–73.

Rehman, U. Ateekh, Ahamri, A. Al (2013b). Assessment of alternative industrial robots using AHP and TOPSIS. International Journal of Industrial and Systems Engineering, 15(4), 475–489.

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.

Saaty, T.L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281.

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, 465–487.

Sen, D.K., Datta, S., Mahapatra, S. (2016). Application of TODIM (Tomada de Decisión Inerativa Multicritero) for industrial robot selection. Benchmarking: An International Journal, 23, 1818–1833.

Simion, M., Socaciu, L., Giurgiu, O., PETRIŞOR, S.M. (2018). The selection of industrial robots for military industry using AHP method: a case study. Acta Technica Napocensis-Series: Applied Mathematics, Mechanics, and Engineering, 61(2).

Stanujkic, D., Karabasevic, D., Zavadskas, E.K. (2015). A framework for the selection of a packaging design based on the SWARA method. Inzinerine ekonomika–Engineering Economics, 26(2), 181–187.

Suszyński, M., Rogalewicz, M. (2020). Selection of an industrial robot for assembly jobs using multi-criteria decision making methods. Management and Production Engineering Review, 11, 62–72.

Tashtoush, T., Otero, E., Velasquez, A., Rodroguez, P., Hernandez, R. (2015). Selection process of industrial robots. In: World Congress on Industrial Automation, San Francisco, USA, pp. 272–278.

Ulutaş, A., Karakuş, C.B., Topal, A. (2020). Location selection for logistics center with fuzzy SWARA and CoCoSo methods. Journal of Intelligent & Fuzzy Systems, 38(4), 4693–4709.

Yalçin, N., Uncu, N. (2019). Applying EDAS as an applicable MCDM method for industrial robot selection. Sigma Journal of Engineering and Natural Sciences, 37(3), 779–796.

Yazdani, M., Wen, Z., Liao, H., Banaitis, A., Turskis, Z. (2019a). A grey combined compromise solution (CoCoSo-G) method for supplier selection in construction management. Journal of Civil Engineering and Management, 25(8), 858–874.

Yazdani, M., Zarate, P., Zavadskas, E.K., Turskis, Z. (2019b). A Combined Compromise Solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57, 2501–2519.

Zavadskas, E.K., Kaklauskas, A., Turskis, Z., Tamošaitiene, J. (2008). Selection of the effective dwelling house walls by applying attributes values determined at intervals. Journal of Civil Engineering and Management, 14(2), 85–93.

Zolfani, S.H., Saparauskas, J. (2013). New application of SWARA method in prioritizing sustainability assessment indicators of energy system. Inzinerine ekonomika–Engineering Economics, 24(5), 408–414.

Biographies

Kumar Vidyapati

vidyapatikumar.me@kgpian.iitkgp.ac.in

V.Kumar is pursuing his PhD in the Department of Mechanical Engineering of Indian Institute of Technology Kharagpur, India. The research area of V. Kumar includes application of different soft computing tools in solving complex optimization and decision making problems.

Kalita Kanak

drkanakkalita@veltech.edu.in

K. Kalita is currently associated with Vel Tech University, India as Assistant Professor in the Mechanical Engineering department. The research area of K. Kalita includes optimization of composite laminated structures, computational mechanics and soft computing techniques.

Chatterjee Prasenjit

p.chatterjee@mckvie.edu.in

P. Chatterjee is currently the Dean (Research and Consultancy) at MCKV Institute of Engineering, India (NAAC Accredited “A” Grade Autonomous Institute under UGC Act, 1956). P. Chatterjee is mainly interested in development of different MCDM tools and their real-time applications, operations management and quantitative techniques.

Zavadskas Edmundas Kazimieras

edmundas.zavadskas@vgtu.lt

E.K. Zavadskas, prof. chief researcher of Institute of Sustainable Construction, Faculty of Civil Engineering, VilniusTech, Lithuania. A member of the Lithuanian Academie of Sciences; Honorary doctor from Poznan, Saint-Petersburg, and Kyiv universities. Chairman of EURO Working Group ORSDCE. Associate, guest editor or board member for 40 international journals. Founding editor of journals TEDE, JCEM, IJSPM. He is a highly cited researcher in 2014, 2018–2021. Research interests: multi-criteria decision making, civil engineering, sustainable development, fuzzy systems.

Chakraborty Shankar

s_chakraborty00@yahoo.co.in

S. Chakraborty is a professor at the Department of Production Engineering of Jadavpur University, India, and a regular reviewer of several journals of international repute. The research area of S. Chakraborty includes operations research, multi-criteria decision making, statistical quality control and soft computing.

Full article Related articles Cited by

Open access article under the CC BY license.

Keywords

robot spray painting MCDM SWARA CoCoSo

Metrics

since January 2020

2164 Article info views	806 Full article views
970 PDF downloads	186 XML downloads

RSS

Authors

Abstract

References

Biographies

Export citation

Copy and paste formatted citation

Download citation in file