International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Narathip Pawaree 1, Chawisorn Phukapak 2*, Sorawin Phukapak 3*, Wasana Phuangpornpitak 4, Narong Wichapa 5

1 Department of Industrial Management, Faculty of Technology, Udon Thani Rajabhat University, Udon Thani 41000, Thailand

2 Department of Energy and Environment Engineering, Faculty of Engineering, Rajabhat Maha Sarakham University, Mahasarakham 40000, Thailand

3 Department of Energy Engineering, Faculty of Technology, Udon Thani Rajabhat University, Udon Thani 41000, Thailand

4 Department of Logistics Manangement, Faculty of Business Administration and Information Technology, Rajamangala University of Technology Isan Khonkaen Campus, Khon Kaen 40002, Thailand

5 Department of Industrial Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Kalasin, 46000 Thailand

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ABSTRACT

Productivity improvement is a more complicated and challenging issue to resolve. There is a solution to the multi-response optimization problem. This study proposes a novel approach to optimizing parameters in the cotton swab process using a hybrid Multiple criteria decision-making (MCDM) method based on Response surface methodology (RSM). Simultaneously enhance decision-making efficiency by integrating Technique for order preference by similarity to ideal solution (TOPSIS) and Weighted aggregated sum product assessment (WASPAS) methodology. The optimal conditions were a speed rate of 1300 rpm, a thickness of 1.5 g/m, and a slidver gap of 20 cm, while the defect and downtime were 2.54 kg and 360.67 mins, respectively. The confirmation demonstrates that the actual practical and predicted results were similar. The proposed method's total cost improves from condition A to condition B by 33.86% and 2.45%, respectively. Furthermore, the energy consumption of cotton was found to be 6,208.08 MJ. The total energy consumption may be divided into three main categories: electric energy, thermal energy, and manual energy, which account for 43.12%, 55.73%, and 1.15%, respectively. The entropy reaches its maximum value in the drying and packaging units, which have inefficiencies of 91.24% and 4.35%, respectively, while the combined inefficiencies in the other five units are only 4.41%. This study contributes to advancing decision-making processes and offers insights for enhancing operational efficiency in the pharmaceutical or other manufacturer sector.


Keywords: Energy consumption, Multiple criteria decision-making, Response surface methodology, Weighted aggregated sum product assessment


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REFERENCES

  1. Abdullah, A., Saraswat, S., Talib, F., 2023. Barriers and strategies for sustainable manufacturing implementation in SMEs: A hybrid fuzzy AHP-TOPSIS framework. Sustainable Manufacturing and Service Economics, 2, 100012.

  2. Ali, J., Bashir, Z., Rashid, T., 2021. WASPAS-based decision-making methodology with unknown weight information under uncertain evaluations. Expert Systems with Applications, 168, 114143.

  3. Alıemeke, B.N.G., Oladeinde, M., 2020. Box-Behnken design optimization of sand-casting process parameters. International Journal of Engineering Technologies IJET, 6(2), 25–36.

  4. Alizadeh, R., Soltanisehat, L., Lund, P.D., Zamanisabzi, H., 2020. Improving renewable energy policy planning and decision-making through a hybrid MCDM method. Energy Policy, 137, 111174.

  5. Altın, H., 2020. A comparison of the city life quality index for European cities using the Waspas and Vikor methods. Journal of Business Economics and Finance, 9(2), 97–117.

  6. Arshad, A., Ali, H.M., Habib, A., Bashir, M.A., Jabbal, M. and Yan, Y., 2019. Energy and exergy analysis of fuel cells: A review. Thermal Science and Engineering Progress, 9, 308–321.

  7. Bagal, D.K., Giri, A., Pattanaik, A.K., Jeet, S., Barua, A., Panda, S.N., 2021. MCDM optimization of characteristics in resistance spot welding for dissimilar materials utilizing advanced hybrid Taguchi method-coupled CoCoSo, EDAS and WASPAS method. In Next Generation Materials and Processing Technologies: Select Proceedings of RDMPMC 2020, 475–490.

  8. Bagal, D.K., Mahapatra, S.K., Barua, A., Jeet, S., Pattanaik, A.K., Patnaik, D., 2021. Multi-parametric optimization of wire-EDM of Inconel 718 super alloy using Taguchi-coupled WASPAS method. In Advances in Mechanical Processing and Design: Select Proceedings of ICAMPD 2019, 459–467.

  9. Behera, N.C., Jeet, S., Nayak, C.K., Bagal, D.K., Panda, S.N., Barua, A., 2022. Parametric appraisal of strength and hardness of resin compacted sand castings using hybrid Taguchi-WASPAS-Material Generation Algorithm. Materials Today: Proceedings, 50, 1226–1233.

  10. Çelikbilek, Y., Tüysüz, F., 2020. An in-depth review of theory of the TOPSIS method: An experimental analysis. Journal of Management Analytics, 7(2), 281–300.

  11. Chadaga, K., Prabhu, S., Vivekananda, B.K., Niranjana, S., Umakanth, S., 2021. Battling COVID-19 using machine learning: A review. Cogent Engineering, 8(1), 1958666.

  12. Chicco, D., Warrens, M.J., Jurman, G., 2021. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj computer science, 7, e623.

  13. Chodha, V., Dubey, R., Kumar, R., Singh, S., Kaur, S., 2022. Selection of industrial arc welding robot with TOPSIS and Entropy MCDM techniques. Materials Today: Proceedings, 50, 709–715.

  14. Chowdhury, P., Paul, S.K., 2020. Applications of MCDM methods in research on corporate sustainability: A systematic literature review. Management of Environmental Quality: An International Journal, 31(2), 385–405.

  15. Deng, W., Shang, S., Cai, X., Zhao, H., Song, Y., Xu, J., 2021. An improved differential evolution algorithm and its application in optimization problem. Soft Computing, 25, 5277–5298.

  16. Dietmair, A., Verl, A., 2009. A generic energy consumption model for decision making and energy efficiency optimization in manufacturing. International Journal of Sustainable Engineering, 2(2), 123–133.

  17. Dincera, I., Hussain, M.M. and Al-Zaharnah, I., 2004. Energy and exergy use in the utility sector of Saudi Arabia. Desalination, 169(3), 245–255.

  18. Dutta, S., Sarma, D.K., 2024. Multi-response optimization of machining parameters to minimize the overcut and circularity error during micro-EDM of nickel-titanium shape memory alloy. Advances in Materials and Processing Technologies, 10(1), 1–21.

  19. Emovon, I., Oghenenyerovwho, O.S., 2020. Application of MCDM method in material selection for optimal design: A review. Results in Materials, 7, 100115.

  20. Ghaleb, A.M., Kaid, H., Alsamhan, A., Mian, S.H., Hidri, L., 2020. Assessment and comparison of various MCDM approaches in the selection of manufacturing process. Advances in Materials Science and Engineering, 2020(1), 4039253.

  21. Goswami, S.S., Behera, D.K., Afzal, A., Razak Kaladgi, A., Khan, S.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.

  22. Hajduk, S., 2021. Multi-criteria analysis in the decision-making approach for the linear ordering of urban transport based on TOPSIS technique. Energies, 15(1), 274.

  23. Hocine, A., Zhuang, Z.Y., Kouaissah, N., Li, D.C., 2020. Weighted-additive fuzzy multi-choice goal programming (WA-FMCGP) for supporting renewable energy site selection decisions. European Journal of Operational Research, 285(2), 642–654.

  24. Hua, Y., Liu, Q., Hao, K., Jin, Y., 2021. A survey of evolutionary algorithms for multi-objective optimization problems with irregular Pareto fronts. IEEE/CAA Journal of Automatica Sinica, 8(2), 303–318.

  25. Hutagalung, J., Boy, A.F., Yahdie, M.A., 2022. Implementasi Metode Weighted Aggregated Sum Product Assesment (WASPAS) dalam Pemilihan Oli Mesin Sepeda Motor 150 CC. Bulletin of Informatics and Data Science, 1(2), 55–63.

  26. Jauhari, W.A., Mayangsari, S., Kurdhi, N.A., Wong, K.Y., 2017. A fuzzy periodic review integrated inventory model involving stochastic demand, imperfect production process and inspection errors. Cogent Engineering, 4(1), 1308653.

  27. Jeong, D., 2022. A study on evaluation of online trading system in MRO supply business. The Journal of Economics, Marketing and Management, 10(2), 1–13.

  28. Jeong, I.J., Lee, D.H., Son, Y.J., 2024. A process capability function approach to multiple response surface optimization based on a posterior procedure. Quality Engineering, 1–14.

  29. Koohathongsumrit, N., Chankham, W., 2022. A hybrid approach of fuzzy risk assessment-based incenter of centroid and MCDM methods for multimodal transportation route selection. Cogent Engineering, 9(1), 2091672.

  30. Kouaissah, N., Hocine, A., 2020. Optimizing sustainable and renewable energy portfolios using a fuzzy interval goal programming approach. Computers and Industrial Engineering, 144, 106448.

  31. Kundu, J., Singh, H., 2016. Friction stirs welding: multi-response optimization using Taguchi-based GRA. Production and Manufacturing Research, 4(1), 228–241.

  32. Kunovjanek, M., Wankmüller, C., 2021. An analysis of the global additive manufacturing response to the COVID-19 pandemic. Journal of Manufacturing Technology Management, 32(9), 75–100.

  33. Li, Z., Lu, D., Gao, X., 2021. Optimization of mixture proportions by statistical experimental design using response surface method-A review. Journal of Building Engineering, 36, 102101.

  34. Liaw, C.F., Hsu, W.C.J., Lo, H.W., 2020. A hybrid MCDM model to evaluate and classify outsourcing providers in manufacturing. Symmetry, 12(12), 1962.

  35. Majumder, A., Das, P.K., Majumder, A., Debnath, M., 2014. An approach to optimize the EDM process parameters using desirability-based multi-objective PSO. Production and Manufacturing Research, 2(1), 228–240.

  36. Miç, P., Antmen, Z.F., 2021. A decision-making model based on TOPSIS, WASPAS, and MULTIMOORA methods for university location selection problem. Sage Open, 11(3), 21582440211040115.

  37. Mousavi, S., Thiede, S., Li, W., Kara, S., Herrmann, C., 2016. An integrated approach for improving energy efficiency of manufacturing process chains. International Journal of Sustainable Engineering, 9(1), 11–24.

  38. O’Mahony, D., McDermott, O., Lynch, A., Cormican, K., 2023. The impact of serialisation on operational efficiency and productivity in pharmaceutical sites: A literature review. Cogent Engineering, 10(1), 2231729.

  39. Odunfa, M., Joel, O.L., 2022. Energy, exergy and cost analysis of flour production from wheat in Nigeria: Case study of Nigeria Eagle Flour mills, Ibadan. Global Journal of Engineering and Technology Advances, 13(01), 067–097.

  40. Panda, S.N., Pattanaik, A.K., Patel, A.K., Nayak, S., Patra, P., Bagal, D.K., 2023. Process parameter optimization for machining of Ti-6Al-4V using WASPAS and multi-objective genetic algorithm along with exponential trend line analysis. Materials Today: Proceedings.

  41. Pawaree, N., Phokha, S., Phukapak, C., 2024. Multi-Response Optimization of charcoal briquettes process for green economy using a novel TOPSIS linear programming and Genetic Algorithms based on Response Surface Methodology. Results in Engineering, 102226.

  42. Rajesh, R., 2021. Optimal trade-offs in decision-making for sustainability and resilience in manufacturing supply chains. Journal of Cleaner Production, 313, 127596.

  43. Rokooei, S., Alvanchi, A., Rahimi, M., 2022. Perception of COVID-19 impacts on the construction industry over time. Cogent Engineering, 9(1), 2044575.

  44. Saeed, M.A., Junejo, F., Amin, I., 2024. Optimizing sustainable machining for magnesium alloys: A comparative study of GRA and TOPSIS. Cogent Engineering, 11(1), 2308986.

  45. Sahoo, S.K., Goswami, S.S., 2023. A comprehensive review of multiple criteria decision-making (MCDM) methods: advancements, applications, and future directions. Decision Making Advances, 1(1), 25–48.

  46. Sakdiyah, S.H., Eltivia, N., Afandi, A., 2022. Root cause analysis using fishbone diagram: company management decision making. Journal of Applied Business, Taxation and Economics Research, 1(6), 566–576.

  47. Sałabun, W., Wątróbski, J., Shekhovtsov, A., 2020. Are mcda methods benchmarkable? A comparative study of topsis, vikor, copras, and promethee ii methods. Symmetry, 12(9), 1549.

  48. Sapkota, G., Das, S., Sharma, A., Ghadai, R. K. 2023. Comparative study of WASPAS, EDAS and MOORA MCDM techniques in selection of coconut shell ash reinforced aluminum metal matrix composite. In AIP Conference Proceedings, 2786(1).

  49. Shabbir, M.S., Wisdom, O., 2020. The relationship between corporate social responsibility, environmental investments and financial performance: evidence from manufacturing companies. Environmental Science and Pollution Research, 27(32), 39946–39957.

  50. Sharma, A.K., Mukherjee, I., 2020. Enhanced multi-objective solution approach for multiple quality characteristics optimization problems considering predictive uncertainties. International Journal of Quality and Reliability Management, 37(1), 112–144.

  51. Singh, M., Singh, K. and Sethi, A.S., 2020. Analytical hierarchy process and TOPSIS for selecting best parameters of green manufacturing. Measuring Business Excellence, 24(3), 345–365.

  52. Singh, S., Agrawal, V., SAXENA, K. K., Mohammed, K. A. 2023. Optimization on manufacturing processes at Indian industries using TOPSIS. Indian Journal of Engineering and Materials Sciences (IJEMS), 30(1), 32–44.

  53. Sotoudeh-Anvari, A., 2022. The applications of MCDM methods in COVID-19 pandemic: A state of the art review. Applied Soft Computing, 126, 109238.

  54. Sreeraj, P., Kumaran, S.T., Kumar, S.S., Uthayakumar, M., Pethuraj, M., 2022. Application of MCDM based hybrid optimization of WEDM process parameters. Materials Today: Proceedings, 50, 1186–1192.

  55. Sriburum, A., Wichapa, N., Khanthirat, W., 2023. A novel TOPSIS linear programming model based on the Taguchi Method for solving the Multi-Response Optimization Problems: A case study of a fish scale scraping machine. Engineered Science, 23, 882.

  56. Stefanini, R., Barbara, B., Vignali, G., 2022. Manufacturing, use phase or final disposal: where to focus the efforts to reduce the environmental impact of a food machine? Production and Manufacturing Research, 10(1), 624–640.

  57. Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P., 2020. Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to Compromise solution (MARCOS). Computers and industrial engineering, 140, 106231.

  58. Sweeting, W.J., Hutchinson, A.R., Savage, S.D., 2011. Factors affecting electric vehicle energy consumption. International Journal of Sustainable Engineering, 4(3), 192–201.

  59. To-On, P., Wichapa, N., Khanthirat, W., 2023. A novel TOPSIS linear programming model based on response surface methodology for determining optimal mixture proportions of lightweight concrete blocks containing sugarcane bagasse ash. Heliyon, 9(7).

  60. Vazquez Hernandez, J., Elizondo Rojas, M.D., 2023. Improving spare parts (MRO) inventory management policies after COVID-19 pandemic: A Lean Six Sigma 4.0 project. The TQM Journal, 36(6), 1627–1650.

  61. Waheed, M.A., Jekayinfa, S.O., Ojediran, J.O., Imeokparia, O.E., 2008. Energetic analysis of fruit juice processing operations in Nigeria. Energy, 33(1), 35–45.

  62. Wicaksono, F.D., Ciptomulyono, U., Artana, K.B., Irawan, M.I., 2022. Evaluation of the fatal accident factors in the petroleum industry using hybrid Gaussian multi-criteria decision-making (MCDM) approach. Cogent Engineering, 9(1), 2154003.

  63. Yu, Z., Guindani, M., Grieco, S.F., Chen, L., Holmes, T.C., Xu, X., 2022. Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research. Neuron, 110(1), 21–35.

  64. Yusuf, L.A., Popoola, K., Musa, H., 2021. A review of energy consumption and minimization strategies of machine tools in manufacturing process. International Journal of Sustainable Engineering, 14(6), 1826–1842.

  65. Zhang, Q., Yi, H., Yu, Z., Gao, J., Wang, X., Lin, H. and Shen, B., 2018. Energy-exergy analysis and energy efficiency improvement of coal-fired industrial boilers based on thermal test data. Applied Thermal Engineering, 144, 614–627.

  66. Zhao, X., Deng, Q., Liu, X., Zhang, L., Wu, S., Jiang, C., 2022. Integrated scheduling of distributed service resources for complex equipment considering multiple on-site MRO tasks. International Journal of Production Research, 60(10), 3219–3236.

  67. Zhou, C., Qi, L., Liu, X., Bo, H., 2022. MRO configuration management for complex products. Journal of Systems Science and Systems Engineering, 31(3), 359–380.


ARTICLE INFORMATION

Received: 2024-06-19
Revised: 2024-09-08
Accepted: 2024-10-07
Available Online: 2024-12-30


Cite this article:

Pawaree, N., Phukapak, C., Phukapak, S., Phuangpornpitak, W., Wichapa, N. 2024. Optimization parameters for the cotton swab process using hybrid MCDM methods based on response surface methodology. International Journal of Applied Science and Engineering, 21, 2024216. https://doi.org/10.6703/IJASE.202412_21(5).005

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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