Optimization of Production Operator Performance Assessment with Grey Geometric Mean Weighting and Combinative Distance-based Assessment
DOI:
https://doi.org/10.34010/komputika.v14i2.15977Abstract
The performance of production operators plays a crucial role in determining the level of efficiency and effectiveness of the manufacturing process in a company that has a long-term impact on the company's competitiveness. Production operator performance appraisals often face a number of problems that can reduce the accuracy and fairness of evaluations. One of the main problems is the subjectivity of assessment, where evaluation is based more on the personal perception of the supervisor or assessor without a consistently measurable standard. The purpose of this study is to apply a more objective, structured, and accurate production operator performance evaluation model by integrating the grey geometric mean weighting (G2M Weighting) method as an uncertainty-based criterion weighting approach and combinative distance-based assessment (CODAS) as an alternative ranking method. The results of the production operator's performance ranking are that CR Operator ranks first with the highest performance score of 0.7737, GM Operator is ranked second with a score of 0.6187, followed by AN Operator in third place with a score of 0.5895. This research makes a significant contribution to the development of a performance evaluation system in the manufacturing industry environment by integrating the G2M Weighting and CODAS methods as an objective and systematic approach.
References
[1] A. E. Torkayesh, M. Deveci, S. Karagoz, and J. Antucheviciene, “A state-of-the-art survey of evaluation based on distance from average solution (EDAS): Developments and applications,” Expert Syst. Appl., vol. 221, p. 119724, 2023, doi: https://doi.org/10.1016/j.eswa.2023.119724.
[2] C. Sun, S. Li, and Y. Deng, “Determining Weights in Multi-Criteria Decision Making Based on Negation of Probability Distribution under Uncertain Environment,” Mathematics, vol. 8, no. 2. 2020. doi: 10.3390/math8020191.
[3] Z. Çolak, “A hybrid MCDM method for enhancing site selection for wind power plants in Turkey,” Energy Sustain. Dev., vol. 82, p. 101536, 2024, doi: https://doi.org/10.1016/j.esd.2024.101536.
[4] A. Tomar, R. R. Kumar, and I. Gupta, “Decision making for cloud service selection: a novel and hybrid MCDM approach,” Cluster Comput., vol. 26, no. 6, pp. 3869–3887, Dec. 2023, doi: 10.1007/s10586-022-03793-y.
[5] M. O. Esangbedo, J. Xue, S. Bai, and C. O. Esangbedo, “Relaxed Rank Order Centroid Weighting MCDM Method With Improved Grey Relational Analysis for Subcontractor Selection: Photothermal Power Station Construction,” IEEE Trans. Eng. Manag., 2022, doi: 10.1109/TEM.2022.3204629.
[6] V. Rajput, R. Soni, A. Jha, and A. Agrawal, “Ranking of epoxy/Kota stone dust composite by MCDM approach using hybrid AHP-MOORA methods,” in MATEC Web of Conferences, 2024, vol. 393, p. 1006.
[7] I. Z. Mukhametzyanov, “Elimination of the Domains’ Displacement of the Normalized Values in MCDM Tasks: The IZ-Method,” Int. J. Inf. Technol. Decis. Mak., vol. 23, no. 01, pp. 289–326, Jan. 2024, doi: 10.1142/S0219622023500037.
[8] N. Hendrastuty, S. Setiawansyah, M. G. An’ars, F. A. Rahmadianti, V. H. Saputra, and M. Rahman, “G2M weighting: a new approach based on multi-objective assessment data (case study of MOORA method in determining supplier performance evaluation),” Indones. J. Electr. Eng. Comput. Sci., vol. 38, no. 1, pp. 403–416, 2025, doi: 10.11591/ijeecs.v38.i1.pp403-416.
[9] M. A. Alsalem et al., “Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach,” Expert Syst. Appl., vol. 246, p. 123066, 2024, doi: https://doi.org/10.1016/j.eswa.2023.123066.
[10] A. Alamoodi et al., “Evaluating agriculture 4.0 decision support systems based on hyperbolic fuzzy-weighted zero-inconsistency combined with combinative distance-based assessment,” Comput. Electron. Agric., vol. 227, p. 109618, 2024, doi: https://doi.org/10.1016/j.compag.2024.109618.
[11] H. Wang, L. Feng, M. Deveci, K. Ullah, and H. Garg, “A novel CODAS approach based on Heronian Minkowski distance operator for T-spherical fuzzy multiple attribute group decision-making,” Expert Syst. Appl., vol. 244, p. 122928, 2024, doi: https://doi.org/10.1016/j.eswa.2023.122928.
[12] M. Ramesh, D. J. D. James, S. Mohan, and K. Panneerselvam, “Evaluation and study of PBI reinforced with HDPE on abrasive wear using ANOVA and CODAS approach for protective shell applications,” J. Pipeline Sci. Eng., p. 100279, 2025, doi: https://doi.org/10.1016/j.jpse.2025.100279.
[13] S. Zeng and C. Yang, “Risk evaluation of livestream e-commerce platforms based on expert trust networks and CODAS,” Expert Syst. Appl., vol. 260, p. 125408, 2025, doi: https://doi.org/10.1016/j.eswa.2024.125408.
[14] S. H. Gurmani, S. Zhang, F. A. Awwad, and E. A. A. Ismail, “Combinative distance-based assessment method using linguistic T-spherical fuzzy aggregation operators and its application to multi-attribute group decision-making,” Eng. Appl. Artif. Intell., vol. 133, p. 108165, 2024, doi: https://doi.org/10.1016/j.engappai.2024.108165.
[15] O. T. Amusan, N. I. Nwulu, and S. L. Gbadamosi, “Multi-criteria decision-based hybrid energy selection system using CRITIC weighted CODAS approach,” Sci. African, vol. 26, p. e02372, 2024, doi: https://doi.org/10.1016/j.sciaf.2024.e02372.
[16] Z. Li, Y. Wang, J. Xie, Y. Cheng, and L. Shi, “Hybrid multi-criteria decision-making evaluation of multiple renewable energy systems considering the hysteresis band principle,” Int. J. Hydrogen Energy, vol. 49, pp. 450–462, 2024, doi: https://doi.org/10.1016/j.ijhydene.2023.09.059.
[17] D. Pamucar and S. Biswas, “A Novel Hybrid Decision Making Framework for Comparing Market Performance of Metaverse Crypto Assets,” Decis. Mak. Adv., vol. 1, no. 1, pp. 49–62, Dec. 2023, doi: 10.31181/dma1120238.
[18] Y. Rahmanto, J. Wang, S. Setiawansyah, A. Yudhistira, D. Darwis, and R. R. Suryono, “Optimizing Employee Admission Selection Using G2M Weighting and MOORA Method,” Paradig. - J. Komput. dan Inform., vol. 27, no. 1 SE-, pp. 1–10, Mar. 2025, doi: 10.31294/p.v27i1.8224.
