Mathematical modeling for selecting resilience scenarios for the sesame industry supply chain in Yazd province with climate change and market fluctuations | ||
| Journal of Engineering Management and Soft Computing | ||
| مقاله 9، دوره 11، شماره 2 - شماره پیاپی 21، اسفند 2025، صفحه 194-220 اصل مقاله (1.07 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22091/jemsc.2025.12751.1270 | ||
| نویسندگان | ||
| Ahmad ali Vakili Ahmadabadi؛ Abolfazl Sadeghian* ؛ Mohammad Taghi Honari؛ Mojdeh Rabbani | ||
| Department of Industrial Management, Ya. c., Islamic Azad University, Yazd, Iran | ||
| چکیده | ||
| In this paper a mixed integer nonlinear programming (MINLP) model, which is nonlinear due to the adoption of a comprehensive objective function, has been proposed. The results of this study show that using mathematical models, various scenarios can be designed to assess the resilience of the supply chain in various dimensions such as technical; cost; market; and organizational. These scenarios allow managers and decision makers to identify the strengths and weaknesses of the supply chain by simulating different conditions and adopt effective strategies to improve resilience. According to the results obtained, the total utility generated by assigning scenarios to each risk is calculated to be 1442 and the total cost of this allocation is 41301 monetary units. This research shows that designing supply chain resilience scenarios for sesame industries not only helps to improve the efficiency and sustainability of these industries, but also brings them closer to achieving the goals of sustainable development and conservation of natural resources. | ||
| کلیدواژهها | ||
| Resilience؛ Supply Chain؛ Sesame Industries | ||
| مراجع | ||
|
Abolghasemian, M., Bigdeli, H., & Shamami, N. (2024). Locating Routing Problem (LRP) of distribution of priority support items to ground forces in war conditions. Engineering Management and Soft Computing, 10(1), 262-292. https://doi.org/10.22091/jemsc.2024.11320.1206 Akbari, A. H., & Jafari, M. (2025). Development of a Deep Reinforcement Learning Algorithm in a Dynamic Cellular Manufacturing System Considering Order Rejection, Case Study: Stone Paper Factory. Engineering Management and Soft Computing, 10(2), 204-222. https://doi.org/10.22091/jemsc.2025.11853.1230 Ambulkar, S., Blackhurst, J., & Grawe, S. (2015). Firm's resilience to supply chain disruptions: Scale development and empirical examination. Journal of operations management, 33, 111-122. https://doi.org/10.1016/j.jom.2014.11.002 Azevedo, S. G., Carvalho, H., & Machado, V. C. (2011). The influence of green practices on supply chain performance: A case study approach. Transportation research part E: logistics and transportation review, 47(6), 850-871. https://doi.org/10.1016/j.tre.2011.05.017 Chandra, C., Grabis, J., Chandra, C., & Grabis, J. (2016). Conceptual modeling approaches. Supply Chain Configuration: Concepts, Solutions, and Applications, 137-150. https://doi.org/10.1007/978-1-4939-3557-4_7 Choi, S. B., Min, H., Joo, H. Y., & Choi, H. B. (2017). Assessing the impact of green supply chain practices on firm performance in the Korean manufacturing industry. International Journal of Logistics Research and Applications, 20(2), 129-145. https://doi.org/10.1080/13675567.2016.1160041 Ebrahimpour, M. and Zeinab Farjood Chokami, Z. (2023). Identification and Ranking of Supply Chain Resilience Indicators in Four Dimensions Using the Swara Method in Food Industry. Journal of Improvement Management, 17(2), 33-59. https://doi.org/10.22034/jmi.2023.246887.2337 Eslami Farsani, S. , Mazroui Nasrabadi, E. and Farhadian, A. (2024). Identify and analyze strategies for supply chain resilience. Journal of Strategic Management Studies, 14(56), 1-22. https://doi.org/10.22034/smsj.2023.166852 Izadi, E., Nikbakht, M., Feylizadeh, M., & Shahin, A. (2025). Ranking of criteria affecting humanitarian supply chain services based on blockchain platforms using multi-criteria decision-making methods. Engineering Management and Soft Computing, 10(2), 143-160. https://doi.org/10.22091/jemsc.2025.11552.1215 Kristianto, Y., Gunasekaran, A., Helo, P., & Hao, Y. (2014). A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path. Expert systems with applications, 41(1), 39-49. https://doi.org/10.1016/j.eswa.2013.07.009 Liu, S., Yao, Y., Jia, J., Casper, S., Baracaldo, N., Hase, P., ... & Liu, Y. (2025). Rethinking machine unlearning for large language models. Nature Machine Intelligence, 1-14. https://doi.org/10.1038/s42256-025-00985-0 Ma, J., Xie, R., Ayyadhury, S., Ge, C., Gupta, A., Gupta, R., ... & Wang, B. (2024). The multimodality cell segmentation challenge: toward universal solutions. Nature methods, 21(6), 1103-1113. https://doi.org/10.1038/s41592-024-02233-6 Mazroui nasrabadi E, Mohammadipour E. A conceptual model of critical success factors in improving the resilience of the health tourism supply chain: A case study. jha 2022; 25 (2) :9-25. https://doi.org/10.22034/25.2.9 Motevalli, S. H. , Nazarizadeh, F. and Mir Shahvelayati, F. (2024). Identifying and Evaluating Strategic Options for Advancing the Resilience of the Kaleh Dairy Company's Supply Chain. System Engineering and Productivity, 3(4), 106-135. https://doi/org/10.22034/msb.2024.2021449.1176 Piya, A. K., Yang, L., Omar, A. A. S., Emami, N., & Morina, A. (2024). Synergistic lubrication mechanism of nanodiamonds with organic friction modifier. Carbon, 218, 118742. https://doi.org/10.1016/j.carbon.2023.118742 Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The international journal of logistics management, 20(1), 124-143. Rahman, S., Montero, M. T. V., Rowe, K., Kirton, R., & Kunik Jr, F. (2021). Epidemiology, pathogenesis, clinical presentations, diagnosis and treatment of COVID-19: a review of current evidence. Expert review of clinical pharmacology, 14(5), 601-621. https://doi.org/10.1080/17512433.2021.1902303 Rajesh, R. (2021). Flexible business strategies to enhance resilience in manufacturing supply chains: An empirical study. Journal of Manufacturing Systems, 60, 903-919. https://doi.org/10.1016/j.jmsy.2020.10.010 Rajesh, R., & Ravi, V. (2015). Supplier selection in resilient supply chains: a grey relational analysis approach. Journal of cleaner production, 86, 343-359. https://doi.org/10.1016/j.jclepro.2014.08.054 Sawik, T. (2013). Selection of resilient supply portfolio under disruption risks. Omega, 41(2), 259-269. https://doi.org/10.1016/j.omega.2012.05.003 Soni, U., Jain, V., & Kumar, S. (2014). Measuring supply chain resilience using a deterministic modeling approach. Computers & Industrial Engineering, 74, 11-25. Wang, L., Zhang, X., Su, H., & Zhu, J. (2024). A comprehensive survey of continual learning: Theory, method and application. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2024.3367329 Xu, N. R., Liu, J. B., Li, D. X., & Wang, J. (2016). Research on evolutionary mechanism of agile supply chain network via complex network theory. mathematical Problems in engineering, 2016(1), 4346580. https://doi.org/10.1155/2016/4346580 Xu, Z., Cai, B., Yan, L., Pang, X., & Gao, K. (2024). Statistical analysis of metastable pitting behavior of 2024 aluminum alloy based on deep learning. Corrosion Science, 233, 112077. https://doi.org/10.1016/j.corsci.2024.112077 Zahra, R., & Rezaeenour, J. (2025). review community detection algorithms in multilayer networks; Traditional methods and deep learning. Engineering Management and Soft Computing, 10(2), 1-25. https://doi.org/10.22091/jemsc.2025.11154.1196 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 Yavari, M., Marvi, M., & Akbari, A. H. (2020). Semi-permutation-based genetic algorithm for order acceptance and scheduling in two-stage assembly problem. Neural Computing and Applications, 32, 2989-3003. https://doi.org/10.1007/s00521-019-04027-w Tanhaeean, M., Tavakkoli-Moghaddam, R., & Akbari, A. H. (2022). Boxing match algorithm: A new meta-heuristic algorithm. Soft Computing, 26(24), 13277-13299. https://doi.org/10.1007/s00500-022-07518-6 | ||
|
آمار تعداد مشاهده مقاله: 209 تعداد دریافت فایل اصل مقاله: 71 |
||