Dual DEA Model for Resilience and Sustainability Assessment in Maintenance Systems: A Multi-Objective Genetic Approach with Dairy Plant Case Study
Abstract
In this study, a novel model based on dual Data Envelopment Analysis (DEA) and a Multi‑Objective Genetic Algorithm (MOGA) was developed to simultaneously evaluate and optimize resilience and sustainability in industrial maintenance and repair systems. The proposed model introduces new composite indices—the Composite Resilience Index (CRI) and the Composite Sustainability Index (CSI)—and employs dynamic weighting through Shannon entropy, enabling a concurrent analysis of technical efficiency, responsiveness to operational disruptions, and environmental adaptability. By incorporating the balancing function |CRI − CSI|, the model identifies an equilibrium between conflicting resilience and sustainability objectives. Implemented within the hybrid MOGA–DEA–Composite Resilience Frontier (CRF) framework, the approach was examined in a dairy factory consisting of three production lines. The results revealed that the monthly Preventive Maintenance (PM) policy (S₁) achieved a DEA efficiency score of 0.92, with CRI = 0.83, CSI = 0.86, and a 15% cost reduction, thus representing the optimal trade‑off among multiple objectives. Sensitivity analysis confirmed the model’s stability against changes in objective weights and its strong generalizability. This research ultimately provides an intelligent decision‑support framework for industrial managers in designing resilient and sustainable maintenance policies, particularly for Industry 5.0 manufacturing environments.
Keywords:
Dual data envelopment analysis, Multi objective genetic algorithm, Resilience, Sustainability, Composite resilience frontier, Intelligent maintenanceReferences
- [1] Venkataraman, S., & Salas, P. (2007). Optimization of composite laminates for robust and predictable progressive failure response. AIAA journal, 45(5), 1113–1125. https://doi.org/10.2514/1.22077
- [2] Mo, H., Sansavini, G., & Xie, M. (2018). Performance-based maintenance of gas turbines for reliable control of degraded power systems. Mechanical systems and signal processing, 103, 398–412. https://doi.org/10.1016/j.ymssp.2017.10.021
- [3] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
- [4] Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38(3–4), 145–156. https://doi.org/10.1016/j.omega.2009.07.003
- [5] Deb, K., Sindhya, K., & Hakanen, J. (2016). Multi-objective optimization. In Decision sciences (pp. 161–200). CRC Press. https://dl.acm.org/doi/abs/10.5555/559152
- [6] Barlow, R., & Hunter, L. (1960). Optimum preventive maintenance policies. Operations research, 8(1), 90–100. https://doi.org/10.1287/opre.8.1.90
- [7] Christer, A. H. (2002). A review of delay time analysis for modelling plant maintenance. In Osaki, S. (Ed.), Stochastic models in reliability and maintenance (pp. 89–123). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-24808-8_4
- [8] Rosenblatt, M. J., & Lee, H. L. (1986). Economic production cycles with imperfect production processes. IIE transactions, 18(1), 48–55. https://doi.org/10.1080/07408178608975329
- [9] Nakagawa, T. (2002). Sequential imperfect preventive maintenance policies. IEEE transactions on reliability, 37(3), 295–298. https://doi.org/10.1109/24.3758
- [10] Goyal, S. K., & Barmes, R. (2005). Economic manufacturing quantity model for an imperfect process. International journal of production economics, 92(2), 191–202.
- [11] Mungani, D. S., & Visser, T. (2013). Maintenance approaches for different production methods. South african journal of industrial engineering, 24(3), 1–14. https://doi.org/10.7166/24-3-700
- [12] Bahria, N., Chelbi, A., Dridi, I. H., & Bouchriha, H. (2018). Maintenance and quality control integrated strategy for manufacturing systems. European journal of industrial engineering, 12(3), 307–331. https://doi.org/10.1504/EJIE.2018.092006
- [13] Iravani, S. M. R., & Duenyas, I. (2002). Integrated maintenance and production control of a deteriorating production system. IIE transactions, 34(5), 423–435. https://doi.org/10.1023/A:1013596731865
- [14] Renna, P. (2019). Adaptive policy of buffer allocation and preventive maintenance actions in unreliable production lines. Journal of industrial engineering international, 15(3), 411–421. https://doi.org/10.1007/s40092-018-0301-7
- [15] Rasay, H., Fallahnezhad, M. S., & Zaremehrjerdi, Y. (2019). An integrated model of statistical process control and maintenance planning for a two-stage dependent process under general deterioration. European journal of industrial engineering, 13(2), 149–177. https://doi.org/10.1504/EJIE.2019.098508
- [16] Nardo, M. Di, Madonna, M., Addonizio, P., & Gallab, M. (2021). A mapping analysis of maintenance in Industry 4.0. Journal of applied research and technology, 19(6), 653–675. https://doi.org/10.22201/icat.24486736e.2021.19.6.1460
- [17] Moghaddam, R., Heydari, J., & Sadeghi Niaraki, A. (2023). AI driven predictive maintenance in Industry 4.0: A multi objective approach. International journal of production economics, 264, 108061.
- [18] Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19). https://doi.org/10.3390/su12198211
- [19] Jawahir, I. S. (2008). Beyond the 3r’s: 6r concepts for next generation manufacturing: Recent trends and case studies. Symposium on sustainability and product development, IIT, Chicago. Research Institute for Sustainability Engineering College of Engineering Lexington, KY 40506-01. https://people.utm.my/zulk/wp-content/blogs.dir/916/files/2017/09/Beyond-the-3R-to-6R-concept-Jawahir.pdf
- [20] Polese, F., Gallucci, C., Carrubbo, L., & Santulli, R. (2021). Predictive maintenance as a driver for corporate sustainability: Evidence from a public-private co-financed R&D project. Sustainability, 13(11), 5884. https://doi.org/10.3390/su13115884
- [21] Yan, T., Lei, Y., Wang, B., Han, T., Si, X., & Li, N. (2020). Joint maintenance and spare parts inventory optimization for multi-unit systems considering imperfect maintenance actions. Reliability engineering & system safety, 202, 106994. https://doi.org/10.1016/j.ress.2020.106994
- [22] Hollnagel, E., Woods, D. D., & Leveson, N. (2017). Resilience engineering: Concepts and precepts. Crc Press. https://books.google.com/books?hl=en&lr=lang_en&id=rygf6axAH7UC&oi=fnd&pg=PP1&dq=Resilience+engineering:+concepts+and+precepts.&ots=ir8BQQ6Zea&sig=yqLHmoAPlbSOFIcpDccL0JeFd0Y
- [23] Bhamra, R., Dani, S., & Burnard, K. (2011). Resilience: The concept, a literature review and future directions. International journal of production research, 49, 5375–5393. https://doi.org/10.1080/00207543.2011.563826
- [24] Pumpuni-Lenss, G., Blackburn, T., & Garstenauer, A. (2017). Resilience in complex systems: An agent-based approach. Systems engineering, 20(2), 158–172. https://doi.org/10.1002/sys.21387
- [25] Ghaljahi, M., Omidi, L., & Karimi, A. (2025). Resilience assessment in process industries: A review of literature. Heliyon, 11(4), e42498. https://doi.org/10.1016/j.heliyon.2025.e42498
- [26] Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
- [27] Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)-thirty years on. European journal of operational research, 192(1), 1–17. https://doi.org/10.1016/j.ejor.2008.01.032
- [28] Assaf, S. A., Hadidi, L. A., Hassanain, M. A., & Rezq, M. F. (2015). Performance evaluation and benchmarking for maintenance decision making units at petrochemical corporation using a DEA model. The international journal of advanced manufacturing technology, 76(9), 1957–1967. https://doi.org/10.1007/s00170-014-6422-2
- [29] Vörösmarty, G., & Dobos, I. (2020). A literature review of sustainable supplier evaluation with data envelopment analysis. Journal of cleaner production, 264, 121672. https://doi.org/10.1016/j.jclepro.2020.121672
- [30] Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42(1), 124–131. https://doi.org/10.1016/j.omega.2013.04.002
- [31] Goldberg, D. E. (1994). Genetic and evolutionary algorithms come of age. Communications of the acm, 37(3), 113–120. https://go.gale.com/ps/i.do?id=GALE%7CA15061357&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=00010782&p=AONE&sw=w
- [32] Karimi, A., Mohajerani, M., Alinasab, N., & Akhlaghinezhad, F. (2024). Integrating machine learning and genetic algorithms to optimize building energy and thermal efficiency under historical and future climate scenarios. Sustainability, 16(21). https://doi.org/10.3390/su16219324
- [33] Chen, X. D., Zhan, J. P., Wu, Q. H., & Guo, C. X. (2014). Multi-objective optimization of generation maintenance scheduling. 2014 IEEE pes general meeting| conference & exposition (pp. 1–5). IEEE. https://doi.org/10.1109/PESGM.2014.6939295
- [34] Pourhejazy, P., Kwon, O. K., Chang, Y. T., & Park, H. (Kevin). (2017). Evaluating resiliency of supply chain network: A data envelopment analysis approach. Sustainability, 9(2). https://doi.org/10.3390/su9020255
- [35] Mohtasim, M. S., Das, B. K., Paul, U. K., Kibria, M. G., & Hossain, M. S. (2025). Hybrid renewable multi-generation system optimization: Attaining sustainable development goals. Renewable and sustainable energy reviews, 212, 115415. https://doi.org/10.1016/j.rser.2025.115415