Increasing the speed of processing supply chain decisions in uncertain conditions using the Internet of Things | ||
| Journal of Engineering Management and Soft Computing | ||
| مقاله 8، دوره 12، شماره 1 - شماره پیاپی 22، فروردین 2026، صفحه 102-130 اصل مقاله (717.25 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22091/jemsc.2026.13590.1293 | ||
| نویسندگان | ||
| Hassan penam1؛ Mahdi Rezaei* 2 | ||
| 1Master of Industrial Engineering, University of Qom, Qom, Iran, Email: hassan.penam1999@gmail.com | ||
| 2Assistant Professor, Department of Industrial Engineering, Faculty of Engineering and Technology, University of Qom, Qom, Iran, Email: mahdirezaei@qom.ac.ir | ||
| چکیده | ||
| New technologies have enabled companies to respond to market needs quickly and efficiently. In this context, the new concept of virtual network chain was formed. This research has studied a multi-period and multi-product network to the Internet of Things using virtual empowerment with the aim of maximizing profit and minimizing the processing speed of uncertain data. In addition to the usual cost, the virtualization cost is considered in the objective. To achieve on-line decision-making design in the network, an acceptable value for the transfer time is set. And to be close to reality, the product return rate and uncertain transportation costs are considered. The virtual closed-loop network (VCLSC) is modeled with the probability of no interruption of intervals. The set has the loops of supplier, manufacturer, distributor, customer, recycling center and organ center, and the returned products are recovered or destroyed based on the quality. The model is solved robustly and its behavior is examined by analyzing the input programs. The results show that the processing is highly sensitive to the change of options. | ||
| کلیدواژهها | ||
| Decision acceleration؛ Internet of Things؛ Uncertainty؛ Robust optimization؛ Closed-loop supply chain | ||
| مراجع | ||
|
Abdel-Basset, M., Manogaran, G., & Mohamed, M. (2018). Internet of Things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems. Future Generation Computer Systems, 86, 614-628. https://doi.org/10.1016/j.future.2018.04.051
Abdoli, A., & Mokhtari, H. (2024). Development of a mathematical programming model to redesign the supply chain network with the possibility of changing the usage of facilities. Ind. Manag. Persp., 14(3), 79-116 (In Persian). https://doi.org/10.48308/JIMP.14.3.79
Amin, S. H., & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165-4176. https://doi.org/10.1016/j.apm.2012.09.039
Ben-Tal, A., El Ghaoui, L. & Nemirovski, A. (2009). Robust optimization. Princeton University Press.
Ben-Tal, A., & Nemirovski, A. (1998). Robust convex optimization. Mathematics of Operations Research, 23, 769-805. https://doi.org/10.1287/moor.23.4.769
Ben-Tal, A. & Nemirovski, A. (2000) Robust solutions of linear programming problems contaminated with uncertain data. Math. Programming, 88, 411-421. https://doi.org/10.1007/PL00011380
Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35-53.
Chung, J. S. (2022, February 10). Effects of government subsidy programs on job creation for sustainable supply chain management. Socio-Economic planning sciences, corrected proofArticle 101261. https://doi.org/10.1016/j.seps.2022.101261
Da Cruz Caria, P., Hermans, M., Degen, D., Janssen, S., & Sweerts, L. (2016). Virtual supply chain management at Cisco: Advantages and future developments.
Dalal, S., Lilhore, U. K., Simaiya, S., Radulescu, M., & Belascu, L. (2024). Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM. Technological Forecasting and Social Change, 209, 123841.
Du, F., & Evans, G. W. (2008). A bi-objective reverse logistics network analysis for post-sale service. Computers & Operations Research, 35(8), 2617-2634. https://doi.org/10.1016/j.cor.2006.12.020
Dutta, P., Choi, T.-M., Somani, S., & Butala, R. (2020). Blockchain technology in supply chain operations: Applications, challenges and research opportunities. Transportation Research Part E: Logistics and Transportation Review, 142, 1-33. https://doi.org/10.1016/j.tre.2020.102067
Dweekat, A. J., & Park, J. (2016). Internet of Things-enabled supply chain performance measurement model. In The 2016 International Conference on Industrial Engineering, Management Science and Application (ICIMSA). https://doi.org/10.1109/ICIMSA.2016.7504014
Fang, C., Liu, X., Pardalos, P. M., & Pei, J. (2015). Optimization for a three-stage production system in the internet of things: Procurement, production and product recovery, and acquisition. The International Journal of Advanced Manufacturing Technology, 83(5-8), 689-710. https://doi.org/10.1007/s00170-015-7593-1
Geyer, R., & Blass, V. D. (2010). The economics of cell phone reuse and recycling. The International Journal of Advanced Manufacturing Technology, 47(5-8), 515-525. https://doi.org/10.1007/s00170-009-2228-z
Gitinavard, H., Akbarpour Shirazi, M., & Fazel Zarandi, M. H. (2024). A possibilistic programming approach for biomass supply chain network design under hesitant fuzzy membership function estimation. Scientia Iranica, 31(18), 1606-1624. https://doi.org/10.24200/sci.2021.55021.4035
Gunasekaran, A., & Ngai, E. W. T. (2007). Virtual supply-chain management. Production Planning and Control, 15(6), 584-595. https://doi.org/10.1080/09537280412331283955
Hashemi, S., & sotoudeh, s. (2020). Provide a framework for enhancing the security of IoT-based smart homes using the IoT-A reference architecture. Journal of Information and Communication Technology, 35(10), 27-42. (in Persian) https://dorl.net/dor/20.1001.1.27170411.1397.10.35.4.0
He, L., Xue, M., & Gu, B. (2020). Internet-of-things enabled supply chain planning and coordination with big data services: Certain theoretic implications. Journal of Management Science and Engineering, 5(1), 1-22.
Helo, P., Shamsuzzoha, A., & Sandhu, M. (2016). Cloud-based virtual supply Chain. In The International Conference on Industrial Engineering and Operations Management. Detroit, USA.
Huang, X. (2020). Quality of service optimization in wireless transmission of industrial Internet of Things for intelligent manufacturing. The International Journal of Advanced Manufacturing Technology, 107(3), 1007-1016. https://doi.org/10.1007/s00170-019-04288-8
Jennifer, S. R. (2020). A novel information processing in IoT based real time health care monitoring system. Journal of Electronics and Informatics, 2(3), 188-196. https://doi.org/10.36548/jei.2020.3.006
Kuli_nska, E., & Kuli_nska, K. (2019), General data protection regulation and virtual supply chains. Studia Ekonomiczne, 383, 31-40.
Lee, D. H., & Dong, M. (2009). Dynamic network design for reverse logistics operations under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 45(1), 61-71.https://doi.org/10.1016/j.tre.2008.08.002
Lee, D. H., & Dong, M. (2008). A heuristic approach to logistics network design for end-of-lease computer products recovery. Transportation Research Part E: Logistics and Transportation Review, 44(3), 455-474 .https://doi.org/10.1016/j.tre.2006.11.003
Li, Q., Dou, R., Chen, F., & Nan, G. (2014). QoS-oriented Web service composition approach based on multi-population genetic algorithm for Internet of things. International Journal of Computational Intelligence Systems, 7(sup2), 26-34. https://doi.org/10.1080/18756891.2014.947090
Listeş, O., & Dekker, R. (2005). A stochastic approach to a case study for product recovery network design. European Journal of Operational Research, 160(1), 268-287. https://doi.org/10.1016/j.ejor.2001.12.001
Liu, X., & Sun, Y. (2011). Information flow management of vendor-managed inventory system in automobile parts inbound logistics based on Internet of Things. JSW, 6(7), 1374-1380.
Long, Q. (2014). An agent-based distributed computational experiment framework for virtual supply chain network development. Expert Systems with Applications, 41, 4094-4112. https://doi.org/10.1016/j.eswa.2014.01.001
Lotfi, Z., Mukhtar, M., Sahran, S., & Taei Zadeh, A. (2013). Information sharing in supply chain management. In The 4th International Conference on Electrical Engineering and Informatics. https://doi.org/10.1016/j.protcy.2013.12.194
Matsuda, M., Nishi, T., Hasegawa, M., & Terunuma, T. (2020). Construction of a virtual supply chain using enterprise e-catalogues. Procedia, 93, 688-693. https://doi.org/10.1016/j.procir.2020.04.093
Min, H., & Ko, H. J. (2008). The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers. International Journal of Production Economics, 113(1), 176-192. https://doi.org/10.1016/j.ijpe.2007.01.017
Mizuno, S. (2022, March 2). Dynamic pricing and inventory management of a dualchannel supply chain under different power structures. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2022.02.049
Mohammadi, T., Sajadi, M., Najafi, E., & Taghizadeh Yazdi, M. (2022). Optimizing smart supply chain with vendor managed inventory through the Internet of Things. Industrial Management Journal, 14(3), 458-483. (in Persian). https://doi.org/10.22059/IMJ.2022.343552.1007948
Mula, J., Peidro, D., Díaz-Madroñero, M., & Vicens, E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204(3), 377-390. https://doi.org/10.1016/j.ejor.2009.09.008
Nemati, Y., Madhoushi, M., & Safaei Ghadikolaei, A. (2017). Towards supply chain planning integration: Uncertainty analysis using fuzzy mathematical programming approach in a plastic forming company. Interdisciplinary Journal of Management Studies, 10(2), 335-364. https://doi.org/10.22059/ijms.2017.218842.672334
Nishi, T., Matsuda, M., Hasegawa, M., Alizadeh, R., Liu, Z., & Terunuma, T. (2020). Automatic construction of virtual supply chain as multi-agent system using enterprise e-catalogues. International Journal of Automation Technology, 14, 713-722. https://doi.org/10.20965/ijat.2020.p0713
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closedloop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637-649. https://doi.org/10.1016/j.apm.2010.07.013
Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets and Systems, 161(20), 2668-2683. https://doi.org/10.1016/j.fss.2010.04.010
Qiang, Q., Ke, K., Anderson, T., & Dong, J. (2013). The closed-loop supply chain network with competition, distribution channel investment, and uncertainties. Omega, 41(2), 186-194. https://doi.org/10.1016/j.omega.2011.08.011
Ramezani, M., Kimiagari, A. M., Karimi, B., & Hejazi, T. H. (2014). Closed-loop supply chain network design under a fuzzy environment. Knowledge-Based Systems, 59, 108-120. https://doi.org/10.1016/j.knosys.2014.01.016
Ren, Q. Y. (2019). Research on financial risk management model of internet supply chain based on data science. Cognitive Systems Research, 56, 50-55. https://doi.org/10.1016/j.cogsys.2019.02.001
Rezaei, A. R., & Liu, Q. (2024). A multi objective optimization framework for robust and resilient supply chain network design using NSGAII and MOPSO algorithms. International Journal of Industrial Engineering Computations, 15(2), 773-790. https://doi.org/10.5267/j.ijiec.2024.3.003
Rezaei, M., & Faghihi‑Nezhad, M.‑T. (2022). Real-time supply chain performance management: An IoT-based framework for continuous improvement. In Proceedings of the Second International Conference on Distributed Computing and High Performance Computing (DCHPC 2022) (pp. 8–14). Qom, Iran. https://doi.org/10.1109/DCHPC55044.2022.9732086
Rezaei, M., Salmasnia, A., & Maleki, M. R. (2023). An integrated model for continuous and simultaneous performance improvement: A SCOR-Based supply chain decision alignment. IJIEPR 2023, 34(3), 1-18 URL: http://ijiepr.iust.ac.ir/article-1-1770-en.html
Saffar, M. H. S. G., & Razmi, J. (2015). A new multi objective optimization model for designing a green supply chain network under uncertainty. International Journal of Industrial Engineering Computations, 6(1), 15-32. https://doi.org/10.5267/j.ijiec.2014.10.001
Sallam, K., Mohamed, M., & Mohamed, A. W. (2023). Internet of Things (IoT) in supply chain management: Challenges, opportunities, and best practices. SMIJ, 2(2), 32. https://doi.org/10.61185/SMIJ.2023.22103
Scott, A. H. S., & Mula, J. M. (2009). Contextual factors associated with information systems in a virtual supply Chain. In The 2009 Fourth International Conference on Cooperation and Promotion of Information Resources in Science and Technology. https://doi.org/10.1109/COINFO.2009.76
Shaharudin, M. R., Govindan, K., Zailani, S., & Tan, K. C. (2015). Managing product returns to achieve supply chain sustainability: An exploratory study and research propositions. Journal of Cleaner Production, 101, 1-15. https://doi.org/10.1016/j.jclepro.2015.03.074
Shamsuzzoha, A., & Helo, P. (2017). Virtual supply chain for networked business: Perspective of collaborative bill-of-materials, scheduling and process monitoring for developing innovative product. International Journal of Computer Integrated Manufacturing, 31(6), 595-610.
Shaw, D. B. (2021). Logistics and supply chain management performance measures. International Encyclopedia of Transportation, 16-23. https://doi.org/10.1016/B978-0-08-102671-7.10210-6
Smith, K. J., & Dhillon, G. (2019). Supply chain virtualization: Facilitating agent trust utilizing blockchain technology. In Revisiting Supply Chain Risk (pp. 299-311). https://doi.org/10.1007/978-3-030-03813-7_18
Soyster, A. (1973). Convex programming with setinclusive constraints and applications to inexact linear programming. Operations Research, 21, 1154–7. https://doi.org/10.1287/opre.21.5.1154
Suryawanshi, P., & Dutta, P. (2022). Optimization models for supply chains under risk, uncertainty, and resilience: A state-of-the-art review and future research directions. Transportation Research Part e: Logistics and Transportation Review, 157, 102553. https://doi.org/10.1016/j.tre.2021.102553
Venckauskas, A., Stuikys, V., Damasevicius, R., & Jusas, N. (2016). Modelling of Internet of Things units for estimating security-energy-performance relationships for quality of service and environment awareness. Security and Communication Networks, 9(16), 3324-3339. https://doi.org/10.1002/sec.1537
Verdouw, C. N., Beulens, A. J. M., & van der Vorst, J. G. A. J. (2013). Virtualisation of floricultural supply chains: A review from an internet of things perspective. Computers and Electronics in Agriculture, 99, 160-175. https://doi.org/10.1016/j.compag.2013.09.006
Verdouw, C. N., Wolfert, J., Beulens, A. J. M., & Rialland, A. (2016). Virtualization of food supply chains with the internet of things. Journal of Food Engineering, 176, 128-136.
Vlachos, I., & Graham, G. (2025). The internet of things in supply chain management: Past, present, and future – A systematic literature review and implementation framework. International Journal of Production Research, 1–31. https://doi.org/10.1080/00207543.2025.2553824
Yadav, J., & Misra, M. (2019). An overview of food supply chain virtualization and granular traceability using blockchain technology. International Journal of Blockchain and Cryptocurrencies, 1, 154-178. https://doi.org/10.1504/IJBC.2020.108997
Yan, R. (2017). Optimization approach for increasing revenue of perishable product supply chain with the internet of things. Industrial Management and Data Systems, 117(4), 729-741.
Zeballos, L. J., Méndez, C. A., Barbosa-Povoa, A. P., & Novais, A. Q. (2014). Multi-period design and planning of closed-loop supply chains with uncertain supply and demand. Computers & Chemical Engineering, 66, 151-164. https://doi.org/10.1016/j.compchemeng.2014.02.027
Zhang, L. L. (2021). The impact of supply chain competition on the introduction of clean development mechanisms. Transportation Research Part E: Logistics and Transportation Review, 155, 102506. https://doi.org/10.1016/j.tre.2021.102506
Tavakkoli-Moghaddam, R., Akbari, A. H., Tanhaeean, M., Moghdani, R., Gholian-Jouybari, F., & Hajiaghaei-Keshteli, M. (2024). Multi-objective boxing match algorithm for multi-objective optimization problems. Expert Systems with Applications, 239, 122394. https://doi.org/10.1016/j.eswa.2023.122394
| ||
|
آمار تعداد مشاهده مقاله: 67 تعداد دریافت فایل اصل مقاله: 73 |
||