Evaluating the Performance of Production Lines Using Expanded Data Envelopment Analysis, Analytic Hierarchy Process, and Entropy in a Grey Environment (Case Study: Kaleh Company)

Authors

  • Saeede Maleki * Faculty of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
  • Seyed Esmaeil Najafi 1Faculty of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
  • Zohreh Moghaddas Faculty of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

https://doi.org/10.22105/raise.v1i1.40

Abstract

In today's competitive world, production at any cost is no longer on the agenda of organizations. In this context, the efficiency of production units in converting inputs to outputs is crucial, as inefficiency in a production unit can lead to wasting resources and inputs. For this reason, the Data Envelopment Analysis (DEA) method has attracted the attention of many researchers worldwide in recent years, and various applications of this method are observed for evaluating the performance of different institutions and activities. In this research, DEA is used to evaluate the performance of Kalleh Company's production line. After determining the efficient units, a combined method of Analytic Hierarchy Process (AHP) and Entropy is used to rank the efficient units. In other words, the Entropy method is used to weight the influential criteria, and the AHP is used to rank the efficient units. In other words, this research uses a combination of DEA, Entropy, and AHP methods to evaluate the performance of Kalleh Company's production line. Additionally, to consider real-world uncertainties and decision-making, the grey theory is used. Grey Theory uses interval numbers and creates more degrees of freedom to consider uncertainty. For this purpose, the combined method presented in this research is developed in a grey environment to deal with uncertainty. Finally, the proposed method is applied to Kalleh Company to evaluate performance. The results of the proposed method showed that production lines 1, 4, 8, and 14 were efficient. These lines were then re-evaluated using the combined Grey AHP and Entropy method, and line 4 was selected as the best production line.

Keywords:

Production line performance evaluation, Data envelopment analysis method, Entropy method, Analytic hierarchy process method, Grey theory

References

  1. [1] Adiyeloja, I., Kehinde, O., Babaremu, K., Jen, T.-C., & Okokpujie, I. (2023). Performance evaluation of production lines in a manufacturing company using data envelopment analysis (DEA). Mathematical modelling of engineering problems, 10(1), 39-47. http://dx.doi.org/10.18280/mmep.100105

  2. [2] Habibifar, N., Hamid, M., Bastan, M., & Azar, A. T. (2019). Performance optimisation of a pharmaceutical production line by integrated simulation and data envelopment analysis. International journal of simulation and process modelling, 14(4), 360–376. https://doi.org/10.1504/IJSPM.2019.103587

  3. [3] Al-Refaie, A., Abbasi, G., & Al-Hawadi, A. (2023). DEA efficiency assessment of packaging lines in a pharmaceutical industry. Engineering letters, 31(3), 1–9. https://www.engineeringletters.com/issues_v31/issue_3/EL_31_3_38.pdf

  4. [4] Zhou, P., Ang, B. W., & Poh, K. L. (2008). Measuring environmental performance under different environmental DEA technologies. Energy economics, 30(1), 1–14. https://doi.org/10.1016/j.eneco.2006.05.001

  5. [5] Huang, M., Xia, T., Chen, Z., Pan, E., & Xi, L. (2021). A DEA integrated grey factor analysis approach for efficiency evaluation and ranking in uncertain systems. Computers & industrial engineering, 162, 107681. https://doi.org/10.1016/j.cie.2021.107681

  6. [6] Wang, C. N., Dang, T. T., Nguyen, N. A. T., & Wang, J. W. (2022). A combined data envelopment analysis (DEA) and grey based multiple criteria decision making (G-MCDM) for solar PV power plants site selection: A case study in Vietnam. Energy reports, 8, 1124–1142. https://doi.org/10.1016/j.egyr.2021.12.045

  7. [7] Asnaashari, H., Sheikh Aboumasoudi, A., Mozaffari, M. R., & Feylizadeh, M. R. (2023). Applying claim reduction criteria in selecting efficient contractors with the two-step grey data envelopment analysis approach. Grey systems: theory and application, 13(4), 785–807. https://doi.org/10.1108/GS-03-2023-0027

  8. [8] Wang, C. N., Yang, F. C., Vo, N. T. M., Duong, C. T., & Nguyen, V. T. T. (2024). Enhancing Operational efficiency in industrial systems: A DEA-grey integration. IEEE access, 12, 58532–58550. https://doi.org/10.1109/ACCESS.2024.3374335

  9. [9] Pourjavad, E., & Shirouyehzad, H. (2014). A data envelopment analysis approach for measuring the efficiency in continuous manufacturing lines: a case study. International journal of services and operations management, 18(2), 142–158. https://doi.org/10.1504/IJSOM.2014.061998

  10. [10] Zhang, M., Cui, W. K., Zhang, Y. J., & Xu, Y. H. (2021). Research on World Food Production Efficiency and Environmental Sustainability Based on Entropy‐DEA Model. Complexity, 2021(1), 8730264. https://doi.org/10.1155/2021/8730264

  11. [11] Ammirato, S., Fattoruso, G., & Violi, A. (2022). Parsimonious AHP-DEA integrated approach for efficiency evaluation of production processes. Journal of risk and financial management, 15(7), 1–15. https://doi.org/10.3390/jrfm15070293

  12. [12] Wang, D., Wei, F., & Yang, F. (2023). Efficiency evaluation of a two-stage production process with feedback: An improved DEA model. INFOR: information systems and operational research, 61(1), 67–85. https://doi.org/10.1080/03155986.2022.2104573

  13. [13] He, J., Lau, W. T., & Liu, Y. (2024). Innovative production efficiency in Chinese high-tech industries during the 13th five-year plan considering environmental factors: Evidence from a three-stage DEA model. Green and low-carbon economy, 2(1), 37–48. http://ojs.bonviewpress.com/index.php/GLCE/article/view/910

  14. [14] Thakkar, J. J. (2021). Application of integrated approach of grey AHP and Grey TOPSIS. In multi-criteria decision making (pp. 325–338). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-33-4745-8_19

  15. [15] Ortega, J., Moslem, S., Tóth, J., & Ortega, M. (2023). A two-phase decision making based on the grey analytic hierarchy process for evaluating the issue of park-and-ride facility location. Journal of urban mobility, 3, 100050. https://doi.org/10.1016/j.urbmob.2023.100050

  16. [16] Ni, X., Li, J., Xu, J., Shen, Y., & Liu, X. (2024). Grey relation analysis and multiple criteria decision analysis method model for suitability evaluation of underground space development. Engineering geology, 338, 107608. https://doi.org/10.1016/j.enggeo.2024.107608

  17. [17] Esangbedo, M. O., Bai, S., Mirjalili, S., & Wang, Z. (2021). Evaluation of human resource information systems using grey ordinal pairwise comparison MCDM methods. Expert systems with applications, 182, 115151. https://doi.org/10.1016/j.eswa.2021.115151

  18. [18] Zhang, S., Liu, S., Fang, Z., Zhang, Q., & Zhang, J. (2023). Generalized grey information entropy weight TOPSIS model for financial performance evaluation considering differentiation. Kybernetes, 52(11), 5412–5426. https://doi.org/10.1108/K-03-2022-0418

  19. [19] Çirkin, E., Özdağoğlu, A., Patel, I., & Janardhanan, M. N. (2023). Integrated grey entropy and COPRAS methods for machine selection decision problem. International journal of services and operations management, 44(4), 571–586. https://doi.org/10.1504/IJSOM.2023.130177

  20. [20] Han, Z. Q., Xu, Z. Q., & Yang, W. E. (2024). Optimal site selection of electrochemical energy storage station based on a novel grey multi-criteria decision-making framework. Sustainable energy technologies and assessments, 67, 103844. https://doi.org/10.1108/K-03-2022-0418

  21. [21] Lin, Y. H., Lee, P. C., Chang, T. P., & Ting, H. I. (2008). Multi-attribute group decision making model under the condition of uncertain information. Automation in construction, 17(6), 792–797. https://doi.org/10.1108/K-03-2022-0418

  22. [22] Ju-Long, D. (1982). Control problems of grey systems. Systems & control letters, 1(5), 288–294. https://doi.org/10.1016/S0167-6911(82)80025-X

  23. [23] Xia, J. (2000). Grey system theory to hydrology. Huazhong University of Science and Technology Press, Wuhan.

  24. [24] Oztaysi, B. (2014). A decision model for information technology selection using AHP integrated TOPSIS-Grey: The case of content management systems. Knowledge-based systems, 70, 44–54. https://doi.org/10.1016/j.knosys.2014.02.010

  25. [25] Banker, R. D., Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Returns to scale in DEA. In handbook on data envelopment analysis (pp. 41–70). Springer. https://doi.org/10.1007/978-1-4419-6151-8_2

  26. [26] Friedman, L., & Sinuany-Stern, Z. (1998). Combining ranking scales and selecting variables in the DEA context: The case of industrial branches. Computers & operations research, 25(9), 781–791. https://doi.org/10.1016/S0305-0548(97)00102-0

  27. [27] Kao, C., & Liu, S.-T. (2000). Fuzzy efficiency measures in data envelopment analysis. Fuzzy sets and systems, 113(3), 427–437. https://doi.org/10.1016/S0165-0114(98)00137-7

  28. [28] Jafari, M. (2020). System identification of a soil tunnel based on a hybrid artificial neural network–numerical model approach. Iranian journal of science and technology, transactions of civil engineering, 44(3), 889–899. https://doi.org/10.1007/s40996-020-00405-w

Published

2024-08-10

How to Cite

Evaluating the Performance of Production Lines Using Expanded Data Envelopment Analysis, Analytic Hierarchy Process, and Entropy in a Grey Environment (Case Study: Kaleh Company). (2024). Research Annals of Industrial and Systems Engineering, 1(1), 42-61. https://doi.org/10.22105/raise.v1i1.40

Similar Articles

1-10 of 13

You may also start an advanced similarity search for this article.