Development of a Model to Evaluate the Efficiency of Maintenance Units for Automated Guided Vehicles in Industry 4.0Using an Integrated Approach Based on Data Envelopment Analysis and Game Theory

Massahi, Maziyar; Tohidi, Hamid; Golrizgashti, Seyedehfatemeh

doi:10.22034/IJRRS.2026.9.1.6

Development of a Model to Evaluate the Efficiency of Maintenance Units for Automated Guided Vehicles in Industry 4.0Using an Integrated Approach Based on Data Envelopment Analysis and Game Theory

Document Type : Original Research Article

Authors

Maziyar Massahi

Hamid Tohidi

Seyedehfatemeh Golrizgashti

Department of Industrial Engineering, ST.C., Islamic Azad University, Tehran, Iran

10.22034/IJRRS.2026.9.1.6

Abstract

With the advent of the Fourth Industrial Revolution, the use of specialized tools and technologies in industrial systems has significantly expanded. Automated Guided Vehicles (AGVs) have emerged as a vital component in factories aligned with Industry 4.0 standards. However, like any other equipment, AGVs require systematic and ongoing maintenance to ensure optimal performance.

This study aims to evaluate the efficiency of AGV maintenance units through an integrated model that combines Data Envelopment Analysis (DEA) and Game Theory. The model is applied under conditions that assess the impact of various maintenance strategies—corrective, preventive, and predictive—as well as the spatial configuration of maintenance and depot sites. Game Theory is incorporated to account for cooperative effects among decision-making units (DMUs), enhancing the realism and analytical depth of the evaluation.
The results of the integrated DEA–Game Theory analysis indicate that incorporating the proposed network structure provides valuable insights into the sources of inefficiency across DMUs. This enables the identification of optimal maintenance strategies and the most effective spatial arrangements for maintenance and depot facilities. A comparative analysis with the classical DEA model reveals that, out of three efficient DMUs in the traditional framework, only one remains efficient under the integrated model, while the other two exhibit inefficiencies across at least three sub-networks.
Ultimately, the findings suggest that adopting a predictive maintenance approach—alongside corrective and preventive strategies—yields superior performance. Moreover, the co-location of AGV maintenance and depot facilities with existing production machinery maintenance sites is identified as the most efficient configuration.

Keywords

Maintenance Efficiency Evaluation

Data Envelopment Analysis

Automated guided vehicles

Industry 4.0

Game Theory

Network DEA Model

Hybrid Decision-Making Approach

Subjects

Maintenance engineering

[1] R. Yan, S. J. Dunnett, and L. M. Jackson, "Maintenance modelling of complex automated guided vehicle systems," Annual Reliability and Maintainability Symposium (RAMS), Orlando, USA, 2019, https://doi.org/10.1109/RAMS.2019.8769020.

[2] N. R. Mohamad, M. H. F. Fazli Md Fauadi, S. F. Zainudin, A. Z. M. Noor, F. A. Jafar, and M. M. Ali, "Optimization of material transportation assignment for automated guided vehicle (AGV) system," International Journal of Engineering and Technology, vol. 7, no. 3.20, pp. 334–338, 2018, https://doi.org/10.14419/ijet.v7i3.20.19269.

[3] R. Yan, L. M. Jackson, and S. J. Dunnett, "Automated guided vehicle mission reliability modeling using a combined fault tree and Petri net approach," International Journal of Advanced Manufacturing Technology, vol. 92, pp. 1825–1837, 2017, https://doi.org/10.1007/s00170-017-0175-7.

[4] R. Yan, S. J. Dunnett, and L. M. Jackson, "Optimising the maintenance strategy for a multi-AGV system using genetic algorithms," in Safety and Reliability – Safe Societies in a Changing World, J. Haugen et al., Eds., pp. 1523–1530, Taylor & Francis Group, 2018.

[5] H. Gao, H. Yu, G. Xie, H. Ma, Y. Xu, and D. Li, "Hardware and software architecture of intelligent vehicles and road verification in typical traffic scenarios," IET Intelligent Transport Systems, vol. 13, no. 6, pp. 960–966, 2019, https://doi.org/10.1049/iet-its.2018.5351.

[6] A. Gola and G. Kłosowski, "Application of fuzzy logic and genetic algorithms in automated works transport organization," in Distributed Computing and Artificial Intelligence, 14th International Conference, S. Omatu et al., Eds., pp. 29-36, Springer, 2018, https://doi.org/10.1007/978-3-319-62410-5_4.

[7] Y. Khosiawan, A. Khalfay, and I. Nielsen, "Scheduling unmanned vehicle and automated guided vehicle operations in an indoor manufacturing environment using differential evolution-fused particle swarm optimization," International Journal of Advanced Robotic Systems, vol. 15, no. 1, pp. 1–15, 2018, https://doi.org/10.1177/1729881417754145.

[8] I. Shafieenejad, M. Nourianpour, M. Banitalebi Dehkordi, and K. Ansari, "A comprehensive review of safety and risk management strategies in aerospace operations for human casualty mitigation," International Journal of Reliability, Risk and Safety: Theory and Application, vol. 6, no. 1, pp. 111-130, 2023, https://doi.org/10.22034/IJRRS.2023.6.1.12.

[9] Nemeth, J. Püspöki, A.B. Viharos, L. Zsóka, and B. Pirka, "Layout configuration, maintenance planning and simulation of AGV-based robotic assembly systems," IFAC-PapersOnLine, vol. 52, no. 13, pp. 1626–1631, 2019, https://doi.org/10.1016/j.ifacol.2019.11.433.

[10] J. Kosiek, A. Kaizer, A. Salomon, and A. Sacharko, "Analysis of modern port technologies based on literature review," TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, vol. 15, no. 3, pp. 667-674, 2021, http://dx.doi.org/10.12716/1001.15.03.22.

[11] T. Yan, "Positioning of logistics and warehousing automated guided vehicle based on improved LSTM network," International Journal of System Assurance Engineering and Management, vol. 14, no. 2, pp. 509-518, 2023, https://doi.org/10.1007/s13198-021-01243-3.

[12] C. Chen, Z. H. Hu, and L. Wang, "Scheduling of AGVs in automated container terminal based on the deep deterministic policy gradient (DDPG) using the convolutional neural network (CNN)," Journal of Marine Science and Engineering, vol. 9, no. 12, 2021, Art. no. 1439, https://doi.org/10.3390/jmse9121439.

[13] N. Ghasemi, E. Najafi, F. H. Lotfi, and F. Movahedi Sobhani, "Measuring the efficiency of organizations with hierarchical structures using DEA and game theory," Journal of Industrial Engineering and Management Studies, vol. 8, no. 2, pp. 1–25, 2022, https://doi.org/10.22116/jiems.2022.138122.

[14] Ž. Stević, S. Miškić, D. Vojinović, E. Huskanović, M. Stanković, and D. Pamučar, "Development of a model for evaluating the efficiency of transport companies: PCA–DEA–MCDM model," Axioms, vol. 11, no. 3, 2022, Art. no. 140, https://doi.org/10.3390/axioms11030140.

[15] N. Singh, Q. V. Dang, A. Akcay, I. Adan, and T. Martagan, "A matheuristic for AGV scheduling with battery constraints," European Journal of Operational Research, vol. 298, no. 3, pp. 855–873, 2022, https://doi.org/10.1016/j.ejor.2021.08.008.

[16] M. Khoveyni and R. Eslami, "Two-stage network DEA with shared resources: Illustrating the drawbacks and measuring the overall efficiency," Knowledge-Based Systems, vol. 251, 2022, Art. no. 108725, https://doi.org/10.1016/j.knosys.2022.108725.

[17] H. Sarfaraz, A. K. Yazdi, T. Hanne, P. F. Wanke, and R. S. Hosseini, "Assessing repair and maintenance efficiency for water suppliers: A novel hybrid USBM-FIS framework," Operations Management Research, vol. 16, no. 3, pp. 1321–1342, 2023, https://doi.org/10.1007/s12063-023-00347-2.

[18] E. Turanoglu Bekar, "Efficiency measurement based on novel performance measures in total productive maintenance (TPM) using a fuzzy integrated COPRAS and DEA method," Frontiers in Manufacturing Technology, vol. 3, 2023. Art. no. 1072777, https://doi.org/10.3389/fmtec.2023.1072777

[19] N. R. Mohamad, M. H. F. Md Fauadi, S. F. Zainudin, A. Z. M. Noor, F. A. Jafar, and M. M. Ali, "Optimization of material transportation assignment for automated guided vehicle (AGV) system," International Journal of Engineering and Technology, vol. 7, no. 3.20, pp. 334–338, 2018, https://doi.org/10.14419/ijet.v7i3.20.19269.

[20] R. Mahmoudi, A. Emrouznejad, and M. Rasti-Barzoki, "A bargaining game model for performance assessment in network DEA considering sub-networks: A real case study in banking," Neural Computing and Applications, vol. 31, pp. 6429–6447, 2019, https://doi.org/10.1007/s00521-018-3428-y.

[21] A. Homayoni, R. Fallahnejad, and F. H. Lotfi, "Cross Malmquist Productivity Index in Data Envelopment Analysis," 4OR – A Quarterly Journal of Operations Research, vol. 20, pp. 567–602, 2022, https://doi.org/10.1007/s10288-021-00489-8.

[22] S. Mohsenirad and K. Triantis, "Testing for heterogeneity in data envelopment analysis," Annals of Operations Research, 2025. https://doi.org/10.1007/s10479-024-06460-0.

[23] D. A. Mahmoodirad, D. Pamucar, S. Niroomand, and V. Simic, "Data envelopment analysis based performance evaluation of hospitals – Implementation of novel picture fuzzy BCC model," Expert Systems with Applications, vol. 263, 2025, Art. no. 125775, https://doi.org/10.1016/j.eswa.2024.125775.

[24] M. Jahangoshai Rezaee, A. Moini, and F. Haji-Ali Asgari, "Unified performance evaluation of health centers with integrated model of data envelopment analysis and bargaining game," Journal of Medical Systems, vol. 36, pp. 3805–3815, 2012, https://doi.org/10.1007/s10916-012-9853-z.

[25] J. Harsanyi, "A bargaining model for the cooperative n-person game," in Contributions to the Theory of Games, A. W. Tucker and R. D. Luce, Eds., vol. IV, pp. 325–356, Princeton University Press, 1959, https://doi.org/10.1515/9781400882168-019.