1- Tang, X., 2020. Research on smart logistics model based on internet of things technology.
IEEE Access, 8, 151150-151159. DOI:
https://doi.org/10.1109/ACCESS.2020.3016330.
2- Song, Y., Yu, F. R., Zhou, L., Yang, X., and He, Z., 2020. Applications of the internet of things (IoT) in smart logistics: a comprehensive survey.
IEEE Internet of Things Journal, 8(6), 4250-4274. DOI:
https://doi.org/10.1109/JIOT.2020.3034385.
3- Lee, S., Kang, Y., and Prabhu, V. V., 2016. Smart logistics: distributed control of green crowdsourced parcel services.
International Journal of Production Research, 54(23), 6956-6968. DOI:
https://doi.org/10.1080/00207543.2015.1132856.
4- Jafarzadeh Ghoushchi, S., Memarpour Ghiaci, A., Rahnamay Bonab, S., and Ranjbarzadeh, R., 2022. Barriers to circular economy implementation in designing of sustainable medical waste management systems using a new extended decision-making and FMEA models.
Environmental Science and Pollution Research, 1-19. DOI:
https://doi.org/10.1007/s11356-022-19018-z.
5- Ghoushchi, S. J., Yousefi, S., and Khazaeili, M., 2019. An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures.
Applied Soft Computing, 81, 105505. DOI:
https://doi.org/10.1016/j.asoc.2019.105505.
6- Jafarzadeh Ghoushchi, S., Shaffiee Haghshenas, S., Memarpour Ghiaci, A., Guido, G., and Vitale, A., 2022. Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment.
Neural Computing and Applications, 1-19. DOI:
https://doi.org/10.1007/s00521-022-07929-4.
7- Fleisch, E., Christ, O., and Dierkes, M., 2005. Die betriebswirtschaftliche vision des internets der dinge.
In Das Internet der Dinge (pp. 3-37): Springer. DOI:
https://doi.org/10.1007/3-540-28299-8_1.
8- Uckelmann, D., 2008. A definition approach to smart logistics.
Paper presented at the International Conference on Next Generation Wired/Wireless Networking. DOI:
https://doi.org/10.1007/978-3-540-85500-2_28.
9- Barreto, L., Amaral, A., and Pereira, T., 2017. Industry 4.0 implications in logistics: an overview.
Procedia Manufacturing, 13, 1245-1252. DOI:
https://doi.org/10.1016/j.promfg.2017.09.045.
10- Cullity, B., 1972. Introduction to magnetic materials (MA, USA. In: Addison-Wesley, Reading.
11- Lin, C.-C., and Yang, J.-W., 2018. Cost-efficient deployment of fog computing systems at logistics centers in industry 4.0.
IEEE Transactions on Iindustrial Informatics, 14(10), 4603-4611. DOI:
https://doi.org/10.1109/TII.2018.2827920.
12- Zhang, C., and Peng, D., 2013. Countermeasures on how to develop China’s smart logistics. China Business and Market, 10(1), 35-39.
13- Zhang, Y., Liu, S., Liu, Y., Yang, H., Li, M., Huisingh, D., and Wang, L., 2018. The ‘internet of things’ enabled real-time scheduling for remanufacturing of automobile engines.
Journal of Cleaner Production, 185, 562-575. DOI:
https://doi.org/10.1016/j.jclepro.2018.02.061.
14- Zhao, L., 2005. Intelligent logistics and its supporting technology. Intell, 24(12), 49-50.
15- McFarlane, D., Giannikas, V., and Lu, W., 2016. Intelligent logistics: Involving the customer.
Computers in Industry, 81, 105-115. DOI:
https://doi.org/10.1016/j.compind.2015.10.002.
16- Ghoushchi, S. J., Jalalat, S. M., Bonab, S. R., Ghiaci, A. M., Haseli, G., and Tomaskova, H., 2022. Evaluation of wind turbine failure modes using the developed SWARA-CoCoSo methods based on the spherical fuzzy environment.
IEEE Access, 10, 86750-86764. DOI:
https://doi.org/10.1109/ACCESS.2022.3199359.
17- Sumrit, D., and Keeratibhubordee, J., 2025. Risk assessment framework for reverse logistics in waste plastic recycle industry: A hybrid approach incorporating FMEA decision model with AHP-LOPCOW-ARAS under trapezoidal fuzzy set.
Decision Making: Applications in Management and Engineering, 42-81. DOI:
https://doi.org/10.31181/dmame812025984.
18- Phokee, W., Chaiklieng, S., Boriwan, P., Phoka, T., Vanoirbeek, J. and Chatpun, S., 2025. A Cascading Delphi Method-Based FMEA Risk Assessment Framework for Surgical Instrument Design: A Case Study of a Fetoscope.
Applied Sciences, 15(11), p.6203. DOI:
https://doi.org/10.3390/app15116203.
19- Kumari, S., Ahmad, K., Khan, Z.A. and Ahmad, S., 2025. Analysing the failure modes of water treatment plant using FMEA based on fuzzy AHP and fuzzy VIKOR methods.
Arabian Journal for Science and Engineering, pp.1-16. DOI:
https://doi.org/10.1007/s13369-025-10000-8.
20- Deepak, D., Brar, G.S., Dwivedi, S., Farwaha, H.S., Ranjan, N., Alkhaleel, B.A. and Pandey, S., 2025. Optimizing concrete pump maintenance in the construction sector using enhanced MCDM techniques.
Scientific Reports, 15(1), p.13816. DOI:
https://doi.org/10.1038/s41598-025-98481-3.
21- Rashidian, F., Eydi, A., and Roghanian, E., 2024. Reliable and green road-rail routing using a hybrid procedure of DANP, COCOSO, and FMEA criticality methods: A case study of cement transportation network in Iran.
Journal of Cleaner Production, 447, 141250. DOI:
https://doi.org/10.1016/j.jclepro.2024.141250.
22- Altubaishe, B., and Desai, S., 2023. Multicriteria decision making in supply chain management using FMEA and hybrid AHP-PROMETHEE algorithms.
Sensors, 23(8), DOI:
https://doi.org/10.3390/s23084041.
23- Jin, G., Meng, Q., and Feng, W., 2022. Optimization of Logistics System with Fuzzy FMEA-AHP Methodology.
Processes, 10(10), 1973. DOI:
https://doi.org/10.3390/pr10101973.
24- Oturakçı, M., and Konyalıoğlu, A. K., 2022. Action selection based on fuzzy AHP-based TOPSIS method in fuzzy FMEA-based risk assessment: A case study.
Paper presented at the International Conference on Intelligent and Fuzzy Systems. DOI:
https://doi.org/10.1007/978-3-031-09173-5_45.
25- Bathrinath, S., Mohan, S., Koppiahraj, K., Bhalaji, R., and Santhi, B., 2022. Analysis of factors affecting sustainable performance in construction sites using fuzzy AHP-WASPAS methods.
Materials Today: Proceedings. DOI:
https://doi.org/10.1016/j.matpr.2022.03.393.
26- Pourmehdi, M., Paydar, M. M., and Asadi-Gangraj, E., 2021. Reaching sustainability through collection center selection considering risk: using the integration of Fuzzy ANP-TOPSIS and FMEA.
Soft Computing, 25(16), 10885-10899. DOI:
https://doi.org/10.1007/s00500-021-05786-2.
27- Alvand, A., Mirhosseini, S. M., Ehsanifar, M., Zeighami, E., and Mohammadi, A., 2021. Identification and assessment of risk in construction projects using the integrated FMEA-SWARA-WASPAS model under fuzzy environment: a case study of a construction project in Iran.
International Journal of Construction Management, 1-23. DOI:
https://doi.org/10.1080/15623599.2021.1877875.
28- Stanujkic, D., Zavadskas, E. K., Karabasevic, D., Smarandache, F., and Turskis, Z., 2017. The use of the pivot pairwise relative criteria importance assessment method for determining the weights of criteria. Infinite Study.
29- Keršuliene, V., Zavadskas, E.K. and Turskis, Z., 2010. Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA).
Journal of business economics and management, 11(2), pp.243-258. DOI:
https://doi.org/10.3846 /jbem. 2010.12.
30- Popovic, G., Stanujkic, D., Mihic, M., Smarandache, F., Karabasevic, D. and Mircetic, V., 2025. The single-valued Neutrosophic extension of the PIPRECIA method.
Knowledge-Based Systems, 315, p.113271. DOI:
https://doi.org/10.1016/j.knosys.2025.113271.
31- Ghiaci, A.M. and Gheidar-Kheljani, J., 2024. A hybrid model of extended FMEA model based on F-PIPRECIA and Z-EDAS methods with Bow Tie to evaluate cybersecurity risks in Industry 4.0.
Engineering Management and Soft, Computing, 9(2), pp.149-176. DOI:
https://doi.org/10.22091/jemsc.2024.9642.1175.
32- Ghoushchi, S.J., Bonab, S.R., Ghiaci, A.M., Haseli, G., Tomaskova, H. and Hajiaghaei-Keshteli, M., 2021. Landfill site selection for medical waste using an integrated SWARA-WASPAS framework based on spherical fuzzy set.
Sustainability, 13(24), p.13950. DOI:
https://doi.org/10.3390/su132413950. [in Persian]
33- Yücenur, G.N. and Maden, A., 2025. Location selection for a photovoltaic agricultural with f-PIPRECIA and WASPAS methods: A case study.
Energy, 314, p.134179. DOI:
https://doi.org/10.1016/j.energy.2024.134179.
34- Lotfi, Z., 1965. Fuzzy sets. Information and control, 8(3), 338-353.
35- Wang, Y.J., 2015. Ranking triangle and trapezoidal fuzzy numbers based on the relative preference relation.
Applied mathematical modelling, 39(2), pp.586-599. DOI:
https://doi.org/10.1016/j.apm.2014.06.011.
36- Zadeh, L. A., 2011. A note on Z-numbers.
Information Sciences, 181(14), 2923-2932. DOI:
https://doi.org/10.1016/j.ins.2011.02.022.
37- Zavadskas, E. K., Turskis, Z., Antucheviciene, J., and Zakarevicius, A., 2012. Optimization of weighted aggregated sum product assessment.
Elektronika ir elektrotechnika, 122(6), 3-6. DOI:
https://doi.org/10.5755/j01.eee.122.6.1810.
38- Chakraborty, S., and Saha, A. K., 2022. A framework of LR fuzzy AHP and fuzzy WASPAS for health care waste recycling technology.
Applied Soft Computing, 127, 109388. DOI:
https://doi.org/10.1016/j.asoc.2022.109388.
