Optimization of physical instruments' characteristics with TOPSIS

Received: June 15, 2022
Revised: July 28, 2022
Accepted: August 30, 2022
Department of Chemistry and Biochemistry, Department of Physics, Concordia University

The present study focuses on the characteristics optimization of  the  physical instruments with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The hypothesis in this research work was that the characteristics of spectrometers and rheometers could affect their rankings, which in turn could be influenced by the underestimation of their cost criterion. In this paper, the characteristics optimization of the FTIR spectrometers and rheometers was carried out with TOPSIS. Moreover, its modified algorithm was also used in order to analyze the inappropriate consideration of these instruments due to category confusion. The modification of TOPSIS helped obtain an automated decision-making method for the treatment of data. The results showed  that  the  rankings of the FTIR spectrometers and rheometers were different as expected. Moreover,  the  rankings  of  the  FTIR  spectrometers  were  different  with  using  the  unmodified  and modified TOPSIS; however, that of the rheometers did not change. The change in the ranking of the FTIR spectrometers was due to the application of the fuzzy disjunction in the TOPSIS code. In this case, the first and second candidates were placed in the first and second positions, respectively, whereas the second candidate had a better rank than the first one in the analysis with the unmodified TOPSIS code. The rank improvement of the first candidate in the category of FTIR spectrometers after the modification of the TOPSIS code was also observed. The results of this work can be used in mechanical engineering and materials science as the appropriate use of instruments in these fields depends on the consideration of their characteristics for which their optimization in comparison with those of other instruments could provide interesting results. Such investigations would provide complementary data for the experimental approaches in further applications. 

[1] Guo X. et al., “Qualitatively and quantitatively characterizing water adsorption of a cellulose nanofiber film using micro-FTIR spectroscopy”, RSC Adv., vol. 8, pp. 4214–4220, 2018, https://doi.org/10.1039/C7RA09894D 
[2] Hernández-Rangel F. J. et al., “Continuous improvement process in the development of alow-cost rotational rheometer”, Processes, vol. 8, 935, 2020, https://doi.org/10.3390/pr8080935 
[3] Feng T. et al., “Reduction-responsive carbon dots for real-time ratiometric monitoring of anticancer prodrug activation in living cells”, ACS Biomat. Sci. Eng., vol. 3, pp. 1535–1541, 2017, https://doi.org/10.1021/acsbiomaterials.7b00264 
[4] Ghanbari A. et al., “Experimental methods in chemical engineering: Rheometry”, The Canadian Journal of Chemical Engineering, vol. 98, 2020, https://doi.org/10.1002/cjce.23749 
[5] Kim Y. et al., “Investigation of rheological properties of blended cement pastes using rotational viscometer and  dynamic  shear  rheometer”,  Advances  in  Materials  Science  and  Engineering,  vol.  2018,  pp.  1–6,  2018, https://doi.org/10.1155/2018/6303681 
[6] Kamnev A. A. et al., “Fourier transform infrared (FTIR) spectroscopic analyses of microbiological samples and biogenic selenium nanoparticles of microbial origin: Sample peparation effects”, Molecules, vol. 26, 1146, 2021, https://doi.org/10.3390/molecules26041146 
[7] Javanbakht T. et al. “Correlation between physicochemical properties of superparamagnetic iron oxide nanoparticles and their reactivity with hydrogen peroxide”, Canadian Journal of Chemistry, vol. 98, pp. 601–608, 2020, https://doi.org/10.1139/cjc-2020-0087 
[8] Javanbakht T. et al., “Comparative study of antibiofilm activity and physicochemical properties of microelectrode arrays”, Microelectronic Engineering, vol. 229, 111305, 2020,  https://doi.org/10.1016/j.mee.2020.111305 Taraneh Javanbakht 
[9] Chilufya L., “Hydrothermal synthesis and characterization of tungsten oxide containing organic-inorganic hybrid material”, Thesis, Izmir Institute of Technology, 2019. 
[10] Mudunkotuwa I. A. et al., “ATR-FTIR spectroscopy as a tool to probe surface adsorption on nanoparticles at the liquid–solid interface in environmentally and biologically relevant media”, Analyst, vol. 139, pp. 870–881, 2014, https://doi.org/10.1039/C3AN01684F 
[11] Keša P. et al., “Photoacoustic properties of polypyrrole nanoparticles”, Nanomaterials, vol. 11, 9, 2457, 2021, https://doi.org/10.3390/nano11092457 
[12] Guo Y. et al., “FTIR microspectroscopy of particular liptinite- (lopinite-) rich, Late Permian coals from Southern China”, International Journal of Coal Geology, vol. 29, pp. 187–197, 1996, https://doi.org/10.1016/0166-5162(95)00024-0 
[13] Javanbakht T. and David E., “Rheological and physical properties of a nanocomposite of graphene oxide nanoribbons  with polyvinyl alcohol”, Journal of Thermoplastic Composite  Materials, vol. 35, pp. 651–664, 2020, https://doi.org/10.1177/0892705720912767 
[14] McKenna  G.  B.,  “Deformation  and  flow  of  matter :  Interrogating  the  physics  of  materials  using rheological methods”, J. Rheol., vol. 56, pp. 113–158, 2012, https://doi.org/10.1122/1.3671401  
[15] Bae J.-E. et al., “Comparison  of  stress-controlled and strain-controlled rheometers for large amplitude oscillatory shear”, Rheologica Acta, vol. 52, pp. 841–857, 2013, https://doi.org/10.1007/s00397-013-0720-8 
[16] Stickel J. J. et al., “Connecting large amplitude oscillatory shear rheology to steady simple shear rheology and application to biomass slurries”, Applied Rheology, vol. 24, pp. 1–10, 2014. 
[17] Tewari  P. C. et  al., “Ranking  of sintered material for high loaded automobile application by applying  entropy-TOPSIS  method”, Materials Today: Proceedings, vol. 2, pp. 2375–2370,  2015, 
[18] Bakhoum E. S. et al., “A hybrid approach using AHP–TOPSIS–entropy methods for sustainable ranking of structural materials”, International Journal of Sustainable Engineering, vol. 6, pp. 212–224, 2013, https://doi.org/10.1080/19397038.2012.719553 
[19] Jahan A. et al., “A target-based normalization technique for materials selection”, Materials and Design, vol. 35, pp. 647–654, 2012, https://doi.org/10.1016/j.matdes.2011.09.005 
[20] Mathiyazhagan K., Gnanavelbabu A., and Prabhuraj B. L., “A sustainable assessment model for material selection in construction industries perspective using hybrid MCDM approaches”, Journal of Advances in Management Research, Emerald Group Publishing, vol. 16, pp. 234–259, 2019, https://doi.org/10.1108/JAMR-09-2018-0085 
[21] Swapna D., Rao C. S., Kumar S., and Radhika S., “AHP and TOPSIS based selection of aluminium alloy for automobile  panels”, Journal of Mechanical and Energy Engineering, vol. 3, pp. 43–50, 2019, https://doi.org/10.30464/jmee.2019.3.1.43 
[22] Calizaya A., et al., “Multi-criteria decision analysis (MCDA) for integrated water resources management (IWRM) in the lake Poopo basin, Bolivia”, Water Resour Manage, vol. 24, pp. 2267–2289,  2010, https://doi.org/10.1007/s11269-009-9551-x 
[23] Ahmad M., et al., “Cyber security quantification of healthcare medical devices through soft computing technique”, International Journal of Advanced Technology in Engineering and Science, vol. 9, pp. 21–27, 2021. 
[24] Sabu M., et al., “Factors influencing the adoption of ICT tools in Kerala marine fisheries sector: an analytic hierarchy process approach”, Technology Analysis and Strategic Management, vol. 30, pp. 1–15, 2017, https://doi.org/10.1080/09537325.2017.1388363 
[25] Javanbakht  T.,  and  Chakravorty  S.,  “Prediction  of  human  behavior  with  TOPSIS”,  Journal  of  Fuzzy Extension and Applications, vol. 3, pp. 109–125, 2022. 
[26] Javanbakht T., “Être et Pensée”,. Beaudin J. P & Robert S. (Eds.), BouquinBec, Montreal, 2020. 
[27] Javanbakht  T.,  “Analysis  of  nanoparticles  characteristics  with  TOPSIS  for  their  manufacture optimization”, J. Eng. Sci., vol. 9, pp. C1–C8, 2022. 
[28] Shukla  A., et al.,  “Applications of TOPSIS algorithm on various  manufacturing processes:  A review”, Materials Today: Proceedings, vol. 4, pp. 5320–5329, 2017, https://doi.org/10.1016/j.matpr.2017.05.042 
[29] Raja S., and Rajan A. J., “A decision-making model for selection of the suitable FDM machine using fuzzy TOPSIS”, Mathematical Problems in Engineering, 7653292, 2022, https://doi.org/10.1155/2022/7653292 
[30] Samala T., et al., “A systematic simulation-based multi-criteria decision-making approach for the evaluation  of  semi–fully  flexible  machine  system  process  parameters”,  Electronics,  vol.  11,  233,  2022, 
[31] Mabkhot M. M., et al., “An ontology-based  multi-criteria decision support  system to reconfigure manufacturing systems”, IISE Transactions, vol. 52, 2020, https://doi.org/10.1080/24725854.2019.1597317 
[32] Alkhawlani M. M., et al., “Multi-criteria vertical handover by TOPSIS and fuzzy logic”, International Conference on Communications and Information Technology, pp. 96–102, 2011,  https://doi.org/10.1109/ICCITECHNOL.2011.5762703 
[33] Biderci H., and Canbaz B., “Ergonomic room selection with intuitive fuzzy TOPSIS method”, Procedia Computer Science, vol. 158, pp. 58–67, 2019, https://doi.org/10.1016/j.procs.2019.09.153 
[34] Haddad A. N., et al., “Application of fuzzy TOPSIS method in supporting supplier selection with focus  on  HSE  criteria:  A  case  study  in  the  oil  and  gas  industry”,  Infrastructures,  vol.  6,  105,  2021, 
[35] Jumarni R. F., and Zamri N., “An integration of fuzzy TOPSIS and fuzzy logic for multi-criteria decision making problems”, International Journal of Engineering and Technology, vol. 7, pp. 102–106, 2018, 
[36] Yousif M. K., and Shaout M., “Fuzzy logic computational model for performance evaluation of Sudanese universities and academic staff”,  Journal of King Saud University –  Computer and Information Sciences, vol. 30,  pp. 80–119, 2018, https://doi.org/10.1016/j.jksuci.2016.08.002  
[37] Oh  K.  W.,  and  Bandler  W.,  “Properties  of  fuzzy  implication  operators”,  International  Journal  of Approximate Reasoning, 1:273-28, 1987, https://doi.org/10.1016/S0888-613X(87)80002-6 
[38] Javanbakht T., and Sokolowski W., “Thiol-ene/acrylate systems for biomedical shape-memory polymers”, Shape Memory Polymers for Biomedical Applications, pp. 157–166, 2015, https://doi.org/10.1016/B978-0-85709-698-2.00008-8 
[39] Javanbakht  T.  “Investigation  of  rheological  properties  of  graphene  oxide  and  its  nanocomposite  with polyvinyl alcohol”, Ukrainian Journal of Mechanical Engineering and Materials Science, vol. 7, pp. 23–32, 2021, https://doi.org/10.23939/ujmems2021.01-02.023 
[40] Emami M. R., “Systematic methodology of fuzzy-logic modeling and control and application to robotics”, Thesis, University of Toronto, 1997.  
[41] Hu X., Chen Z., and Sun Y., “Fuzzy logic based logical query answering on knowledge graphs”, AAAI Technical Track on Data Mining and Knowledge Management, vol. 36,  2022, https://doi.org/10.1609/aaai.v36i4.20310 
[42] Smets P., and Magre P., “Implication in fuzzy logic”, International Journal of Approximate Reasoning, vol. 1, pp. 327–347, 1987, https://doi.org/10.1016/0888-613X(87)90023-5 
[43] Ying M., “Implication operators in fuzzy logic”, IEEE Transactions on Fuzzy Systems, vol. 10, pp. 88–91, 2002, https://doi.org/10.1109/91.983282