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How to cite this paper
APA: Bairagi, B. (2022). Technique of Accurate Ranking Order (TARO): A novel multi criteria analysis approach in performance evaluation of industrial robots for material handling. Decision Science Letters, 11(4), 563-589.
Chicago/Turabian: Bairagi, B. 2022. "Technique of Accurate Ranking Order (TARO): A novel multi criteria analysis approach in performance evaluation of industrial robots for material handling." Decision Science Letters 11, no. 4 (2022): 563-589.
AMA: Bairagi, B. Technique of Accurate Ranking Order (TARO): A novel multi criteria analysis approach in performance evaluation of industrial robots for material handling. Decision Science Letters. 2022;11(4):563-589.
References
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Ali, A., Rashid, T. (2020). Best–worst method for robot selection. Soft Computing, https://doi.org/10.1007/s00500-020-05169-z
Bairagi, B., Dey, B., Sarkar, B., & Sanyal, S. (2012). A Novel Multiplicative Model of Multi Criteria Analysis for Robot Selection. International Journal on Soft Computing, Artificial Intelligence and Applications, 1(3), 1-9.
Bairagi, B., Dey, B., Sarkar, B., & Sanyal, S. (2014). Selection of robot for automated foundry operations using fuzzy multi-criteria decision making approaches. International Journal of Management Science and Engineering Management, 9 (3), 221-232.
Bairagi, B., Dey, B., Sarkar, B., & Sanyal, S. K. (2015). A De Novo multi-approach multi-criteria decision making technique with an application in performance evaluation of material handling device. Computers & Industrial Engineering, 87, 267–282.
Bairagi, B., Dey, B., Sarkar, B. & Sanyal, S. (2015). Selection of robotic systems in fuzzy multi criteria decision-making environment. International Journal of Computational Systems Engineering, 2 (1), 32-42.
Bhangale, P. P., Agrawal, V. P., & Saha, S. K. (2004). Attribute based specification, comparison and selection of a robot. Mechanism and Machine Theory, 39, 1345–66.
Boubekri, N., Sahoui, M., & Lakrib, C. (1991). Development of an expert system for industrial robot selection. Computers and Industrial Engineering, 20, 119–127.
Chakraborty, S. (2010). Applications of the MOORA method for decision making in manufacturing environment. International Journal of Manufacturing Environment, DOI 1 0.1007/s00170-010-2972-0
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, https://doi.org/10.1016/j.matpr.2021.04.487
Fu, Y., Li, M., & Luo, Hao. (2019). George Q. Huang Industrial robot selection using stochastic multicriteria acceptability analysis for group decision making. Robotics and Autonomous Systems, 122, 103304.
Galetto, M., Franceschini, F, Domenico, A., Maisano, D. A., & Mastrogiacomo, L. (2018). Engineering characteristics prioritization in QFD using ordinal scales: a robustness analysis. European Journal of Industrial Engineering, 12 (2), 151 – 174.
Goh C-H. (1997). Analytic hierarchy process for robot selection. Journal of Manufacturing Systems, 16 (5), 381–386.
Goswami, S.S., & Beher, D.K. (2021). Solving Material Handling Equipment Selection Problems in an Industry with the Help of Entropy Integrated COPRAS and ARAS MCDM techniques. Process Integration and Optimization for Sustainability https://doi.org/10.1007/s41660-021-00192-5
Kahraman, C., Cevik, S., Ates, N. Y., & Gulbay, M. (2007). Fuzzy Multi-criteria evaluation of industrial robotic systems. Computers and Industrial Engineering, 52, 414-433.
Karsak, E. E. (2008). Robot selection using an integrated approach based on quality function deployment and fuzzy regression. International Journal of Production Research, 46(3), 723–738.
Karsak, E.E., Sener, Z., & Dursun, M. (2012). Robot selection using a fuzzy regression-based decision-making approach. International Journal of Production Research, 50(23), 6826–34.
Khouja, M., Booth, D.E., Suh, M., & Mahaney, Jr. J. K. (2000). Statistical procedures for task assignment and robot selection in assembly cells. International Journal of Computer Integrated Manufacturing, 13(2), 95–106.
Kır, S., & Yazgan, H. R., (2019). A novel hierarchical approach for a heterogeneous 3D pallet loading problem subject to factual loading and delivery constraints. European Journal of Industrial Engineering, 13(5), 627 – 650.
Layek, A. M., & Lars, J. R. (2000). Algorithm based decision support system for the concerted selection of equipment in machining/assembly cells. International Journal of Production Research, 38(2), 323–339.
Li, J., Barwood, M., & Rahimifard, S. 2019. A multi-criteria assessment of robotic disassembly to support recycling and recovery. Resources, Conservation and Recycling, 140, 158-165.
Liu, H-C., M Quan, M-Y., Hua Shi. H., & Guo, C. (2018). An integrated MCDM method for robot selection under interval‐valued Pythagorean uncertain linguistic environment. International Journal of Intellectual Systems, 1-27. DOI: 10.1002/int.22047
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 (IJIRAS), 4 (2), 59-63.
Narayanamoorthy , S., Geetha, S., Rakkiyappan, R., & Joo , Y. H. (2019). Interval-valued intuitionistic hesitant fuzzy entropy based VIKOR method for industrial robots selection. Expert Systems with Applications, 121(1), 28-37.
Nasrollahi, M., Ramezani, J. & Sadraei, Mahmoud, S. (2020). A FBWM-PROMETHEE approach for industrial robot selection, Helion 6, e03859.
Pamuc, D., & Cirovic, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42, 3016–3028.
Parkan, C., &Wu, M. L. (1999). Decision-making and performance measurement models with application to robot selection. Computers & Industrial Engineering. 36, 503-523.
Rao, R. & Padmanabhan, K. K. (2006). Selection, identification and comparison of industrial robots using digraph and matrix methods. Robotics and Computer Integrated Manufacturing, 22, 373–383.
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, 367–375.
Rashid, T., Ali, A., & Chu Y-M. (2021). Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection. PLoS ONE ,16(2),e0246738. https://doi.org/10.1371/journal. pone.024673
Rashid, T., Beg, I., & Husnine, S. M. (2014). Robot selection by using generalized interval-valued fuzzy numbers with TOPSIS. Applied Soft Computing, 21, 462–468.
Parameshwaran, R., Kumar, S. P. & Saravanakumar. K. (2015). An integrated fuzzy MCDM based approach for robot selection considering objective and subjective criteria. Applied Soft Computing, 26, 31–41.
Samani M.R.G., Hosseini-Motlagh S-M; Sheshkol M.I., & Shetab-Boushehri S-N. (2019). A bi-objective integrated model for the uncertain blood network design with raising products quality. European Journal of Industrial Engineering, 13 (5), 553 – 588.
Shih, H.S. (2008). Incremental analysis for MCDM with an application to group TOPSIS. European Journal of Operational Research, 186, 720–734.
Tansel, İ, Y. Mustafa, Y. & Dengiz, B. (2013). Development of a decision support system for robot selection. Robotics and Computer-Integrated Manufacturing, 29 (4), 142-155.
Yalcin, N., & Nusin Uncu, N. (2019). Applying EDAS as an applicable MCDM method for industrial robot selection. Sigma Journal of Engineering & Natural Science, 37 (3), 779-796.
Ali, A., Rashid, T. (2020). Best–worst method for robot selection. Soft Computing, https://doi.org/10.1007/s00500-020-05169-z
Bairagi, B., Dey, B., Sarkar, B., & Sanyal, S. (2012). A Novel Multiplicative Model of Multi Criteria Analysis for Robot Selection. International Journal on Soft Computing, Artificial Intelligence and Applications, 1(3), 1-9.
Bairagi, B., Dey, B., Sarkar, B., & Sanyal, S. (2014). Selection of robot for automated foundry operations using fuzzy multi-criteria decision making approaches. International Journal of Management Science and Engineering Management, 9 (3), 221-232.
Bairagi, B., Dey, B., Sarkar, B., & Sanyal, S. K. (2015). A De Novo multi-approach multi-criteria decision making technique with an application in performance evaluation of material handling device. Computers & Industrial Engineering, 87, 267–282.
Bairagi, B., Dey, B., Sarkar, B. & Sanyal, S. (2015). Selection of robotic systems in fuzzy multi criteria decision-making environment. International Journal of Computational Systems Engineering, 2 (1), 32-42.
Bhangale, P. P., Agrawal, V. P., & Saha, S. K. (2004). Attribute based specification, comparison and selection of a robot. Mechanism and Machine Theory, 39, 1345–66.
Boubekri, N., Sahoui, M., & Lakrib, C. (1991). Development of an expert system for industrial robot selection. Computers and Industrial Engineering, 20, 119–127.
Chakraborty, S. (2010). Applications of the MOORA method for decision making in manufacturing environment. International Journal of Manufacturing Environment, DOI 1 0.1007/s00170-010-2972-0
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, https://doi.org/10.1016/j.matpr.2021.04.487
Fu, Y., Li, M., & Luo, Hao. (2019). George Q. Huang Industrial robot selection using stochastic multicriteria acceptability analysis for group decision making. Robotics and Autonomous Systems, 122, 103304.
Galetto, M., Franceschini, F, Domenico, A., Maisano, D. A., & Mastrogiacomo, L. (2018). Engineering characteristics prioritization in QFD using ordinal scales: a robustness analysis. European Journal of Industrial Engineering, 12 (2), 151 – 174.
Goh C-H. (1997). Analytic hierarchy process for robot selection. Journal of Manufacturing Systems, 16 (5), 381–386.
Goswami, S.S., & Beher, D.K. (2021). Solving Material Handling Equipment Selection Problems in an Industry with the Help of Entropy Integrated COPRAS and ARAS MCDM techniques. Process Integration and Optimization for Sustainability https://doi.org/10.1007/s41660-021-00192-5
Kahraman, C., Cevik, S., Ates, N. Y., & Gulbay, M. (2007). Fuzzy Multi-criteria evaluation of industrial robotic systems. Computers and Industrial Engineering, 52, 414-433.
Karsak, E. E. (2008). Robot selection using an integrated approach based on quality function deployment and fuzzy regression. International Journal of Production Research, 46(3), 723–738.
Karsak, E.E., Sener, Z., & Dursun, M. (2012). Robot selection using a fuzzy regression-based decision-making approach. International Journal of Production Research, 50(23), 6826–34.
Khouja, M., Booth, D.E., Suh, M., & Mahaney, Jr. J. K. (2000). Statistical procedures for task assignment and robot selection in assembly cells. International Journal of Computer Integrated Manufacturing, 13(2), 95–106.
Kır, S., & Yazgan, H. R., (2019). A novel hierarchical approach for a heterogeneous 3D pallet loading problem subject to factual loading and delivery constraints. European Journal of Industrial Engineering, 13(5), 627 – 650.
Layek, A. M., & Lars, J. R. (2000). Algorithm based decision support system for the concerted selection of equipment in machining/assembly cells. International Journal of Production Research, 38(2), 323–339.
Li, J., Barwood, M., & Rahimifard, S. 2019. A multi-criteria assessment of robotic disassembly to support recycling and recovery. Resources, Conservation and Recycling, 140, 158-165.
Liu, H-C., M Quan, M-Y., Hua Shi. H., & Guo, C. (2018). An integrated MCDM method for robot selection under interval‐valued Pythagorean uncertain linguistic environment. International Journal of Intellectual Systems, 1-27. DOI: 10.1002/int.22047
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 (IJIRAS), 4 (2), 59-63.
Narayanamoorthy , S., Geetha, S., Rakkiyappan, R., & Joo , Y. H. (2019). Interval-valued intuitionistic hesitant fuzzy entropy based VIKOR method for industrial robots selection. Expert Systems with Applications, 121(1), 28-37.
Nasrollahi, M., Ramezani, J. & Sadraei, Mahmoud, S. (2020). A FBWM-PROMETHEE approach for industrial robot selection, Helion 6, e03859.
Pamuc, D., & Cirovic, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42, 3016–3028.
Parkan, C., &Wu, M. L. (1999). Decision-making and performance measurement models with application to robot selection. Computers & Industrial Engineering. 36, 503-523.
Rao, R. & Padmanabhan, K. K. (2006). Selection, identification and comparison of industrial robots using digraph and matrix methods. Robotics and Computer Integrated Manufacturing, 22, 373–383.
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, 367–375.
Rashid, T., Ali, A., & Chu Y-M. (2021). Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection. PLoS ONE ,16(2),e0246738. https://doi.org/10.1371/journal. pone.024673
Rashid, T., Beg, I., & Husnine, S. M. (2014). Robot selection by using generalized interval-valued fuzzy numbers with TOPSIS. Applied Soft Computing, 21, 462–468.
Parameshwaran, R., Kumar, S. P. & Saravanakumar. K. (2015). An integrated fuzzy MCDM based approach for robot selection considering objective and subjective criteria. Applied Soft Computing, 26, 31–41.
Samani M.R.G., Hosseini-Motlagh S-M; Sheshkol M.I., & Shetab-Boushehri S-N. (2019). A bi-objective integrated model for the uncertain blood network design with raising products quality. European Journal of Industrial Engineering, 13 (5), 553 – 588.
Shih, H.S. (2008). Incremental analysis for MCDM with an application to group TOPSIS. European Journal of Operational Research, 186, 720–734.
Tansel, İ, Y. Mustafa, Y. & Dengiz, B. (2013). Development of a decision support system for robot selection. Robotics and Computer-Integrated Manufacturing, 29 (4), 142-155.
Yalcin, N., & Nusin Uncu, N. (2019). Applying EDAS as an applicable MCDM method for industrial robot selection. Sigma Journal of Engineering & Natural Science, 37 (3), 779-796.