مدلسازی ریاضی دو هدفه برای طراحی شبکه‌های زنجیره تأمین تاب‌آور حلقه بسته
محورهای موضوعی : مدیریت صنعتی
مریم بهادران
1
,
مهدی فدایی اشکیکی
2
,
محمد طالقانی
3
,
مهدی همایون فر
4
1 - دانشجوی دکتری گروه مدیریت صنعتی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران
2 - استادیار گروه مدیریت صنعتی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران.
3 - دانشیار گروه مدیریت صنعتی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران.
4 - استادیار گروه مدیریت صنعتی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران.
کلید واژه: زنجیره تأمین, حلقه بسته, تاب‌آوری, بهینه‌سازی چند هدفه,
چکیده مقاله :
در محیط‌های عملیاتی پر اختلال و همراه با ریسک بالا، طراحی صحیح شبکه زنجیره تأمین می‌تواند عواملی مانند تأمین پایداری، کاهش اختلال و افزایش توان اطمینان را به موجب تأمین و استمرار فعالیت، بیشتر کند. برای جلوگیری از ناکارآمدی‌های ناشی از طراحی‌های مجزا، لازم است که طراحی شبکه‌های مستقیم و معکوس به هم ادغام شود. در زنجیره تأمین حلقه بسته، علاوه بر جریان عادی کالا که از تأمین‌کننده به مصرف‌کنندگان نهایی منتقل می‌شود، به جریان معکوس محصولات برای بازیافت، بازسازی یا انهدام نیز توجه می‌شود. در این تحقیق یک شبکه زنجیره تأمین حلقه بسته تاب‌آور تحت شرایط ریسک‌های احتمالی و اختلال در سطوح تولیدکنندگان، توزیع‌کنندگان و مشتریان طراحی شده است. مدل پیشنهاد شده قادر است به صورت همزمان تعداد گره‌ها را ماکزیمم و هزینه کل را کمینه نماید. مسئله بهینه‌سازی چند هدفه با استفاده از روش معیار جامع به ازای P = 1 و P = 2 حل شده است. بر اساس نتایج حاصل شده، مدل پیشنهادی قادر است مقدار محصولات تولید شده، میزان تولیدات با ظرفیت بالا، مسیر انتقال محصولات، میزان جریان محصولات در هر مسیر و مقدار توابع هدف را تعیین نماید. سرانجام، یک تحلیل حساسیت بر روی پارامتر احتمال رخداد خرابی در گره و مسیر انجام شده است. طبق نتایج تحلیل حساسیت مشاهده شده است که اگر بروز خرابی در هر مسیر کاهش یابد، بیشترین بهبود در تابع هدف دوم حاصل می‌شود.
In the closed loop supply chain, in addition to the normal flow of goods that is transferred from the supplier to the final consumers, attention is also paid to the reverse flow of products for recycling, restoration or destruction. In this research, a resilient closed loop supply chain network has been designed under the conditions of possible risks and disruptions at the levels of producers, distributors and customers. The proposed model is able to simultaneously maximize the number of nodes and minimize the total cost. The multi-objective optimization problem has been solved using the comprehensive criterion method for P = 1 and P = 2. Based on the obtained results, the proposed model is able to determine the amount of products produced, the amount of high-capacity products, the route of product transfer, the amount of flow of products in each route and the amount of objective functions. Finally, a sensitivity analysis has been carried out on the parameters of the failure probability in the node and path. According to the results of sensitivity analysis, it has been observed that if the occurrence of failure in each path is reduced, the greatest improvement in the second objective function is achieved.
Abolghasemian, M., Kanafi, A. G., & Daneshmand-Mehr, M. (2022). Simulation-based multiobjective optimization of open-pit mine haulage system: a modified-NBI method and meta modeling approach. Complexity, 2022. https://doi.org/10.1155/2022/3540736
Ambulkar, S., Blackhurst, J., & Grawe, S. (2015). Firm’s resilience to supply chain disruptions: Scale development and empirical examination. Journal of Operations Management, 33–34, 111–122. https://doi.org/10.1016/j.jom.2014.11.002
Asadia, F., & Abolghasemianb, M. (2018). Review coordination of advertising policy and its effect on competition between retailer and manufacture in the supply chain. Computational Research Progress in Applied Science and Engineering, 4(3), 62-66.
Baghersad, M. & Zobel, C.W. (2021). Assessing the extended impacts of supply chain disruptions on firms: An empirical study. International Journal of Production Economics, 231, Art. No. 107862. https://doi.org/10.1016/j.ijpe.2020.107862
Blackhurst, J., Dunn, K. S., & Craighead, C. W. (2011). An Empirically Derived Framework of Global Supply Resiliency. Journal of Business Logistics, 32(4), 374–391. https://doi.org/10.1111/j.0000-0000.2011.01032.x
Carvalho, Helena, Susana Garrido Azevedo, and Virgilio Cruz-Machado. “Agile and resilient approaches to supply chain management: influence on performance and competitiveness”. Logistics research 4(1-2),49-62, 2012. https://doi.org/10.1007/s12159-012-0064-2
Chobar, A. P., Adibi, M. A., & Kazemi, A. (2022). Multi-objective hub-spoke network design of perishable tourism products using combination machine learning and meta-heuristic algorithms. Environment, Development and Sustainability, 1-28. https://doi.org/10.1007/s10668-022-02350-2
Dixit, V. & Verma, P. & Tiwari, M.K. (2020). Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure. International Journal of Production Economics, 227. https://doi.org/10.1016/j.ijpe.2020.107655
Falasca, M., Zobel, C.W., & Cook, D., (2008). A decision support framework to assess supply chain resilience. The Proceedings of the 5th International ISCRAM Conference, Washington, DC, USA, pp. 596–605.
Farahani RZ, Rezapour S., Drezner T., Fallah S. (2013) Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega, 6(3), 14-29. https://doi.org/10.1016/j.omega.2013.08.006
Foroozesh, N., Karimi, B., S.M. Mousavi, S. M. (2022). Green-resilient supply chain network design for perishable products considering route risk and horizontal collaboration under robust interval-valued type-2 fuzzy uncertainty: A case study in food industry. Journal of Environmental Management. 307, 114470. https://doi.org/10.1016/j.jenvman.2022.114470
Li, W., Huang, S., Huang, K., Qi, Y., & An, H. (2024). The pricing and sourcing strategies of competitive retailers under supply disruption in the presence of liquidated damages. Computers & Industrial Engineering, 187, 109782. https://doi.org/10.1016/j.cie.2023.109782
Lotfi, R., Kargar, B., Rajabzadeh, M., Hesabi, F., & Özceylan, E. (2022). Hybrid fuzzy and data-driven robust optimization for resilience and sustainable health care supply chain with vendor-managed inventory approach. International Journal of Fuzzy Systems, 24(2), 1216-1231. https://doi.org/10.1007/s40815-021-01209-4
Piraveenan, M. & Jing, H. & Matous, P. & Todo, Y. (2020). Topology of international supply chain networks: A case study using factset revere datasets. IEEE Access, 8:154540–154559. https://doi.org/10.1109/ACCESS.2020.3015910
Raian, S., Siddiqua, T., Moktadir, M. A., & Rahman, T. (2023). An empirical model for identifying and controlling operational and environmental risks in spinning industry in an emerging economy. Computers & Industrial Engineering, 180, 109244. https://doi.org/10.1016/j.cie.2023.109244
Shi, H., & Ni, Y. (2024). Resilient supply chain network design under disruption and operational risks. Soft Computing, 28(4), 3283-3299. https://doi.org/10.1007/s00500-023-09338-8
Soni, U., Vipul, J., and Sameer, K. (2014). Measuring supply chain resilience using a deterministic modeling approach. Computers & Industrial Engineering74, 11-25, 2014. https://doi.org/10.1016/j.cie.2014.04.019
Starr, R., Newfrock, J., & Delurey, M. (2003). Enterprise resilience: Managing risk in the networked economy. Strategy and Business, 30, 70–79.
Toorajipour, R. & Sohrabpour, V. & Nazarpour, A. & d Oghazi, P. & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research,122:502–517. https://doi.org/10.1016/j.jbusres.2020.09.009
Vali-Siar, M.M. & Roghanian, E. & Jabbarzadeh, A. (2022). Resilient mixed open and closed-loop supply chain network design under operational and disruption risks considering competition: A case study. Computers & Industrial Engineering, 172: Part A, October 2022, 108513. https://doi.org/10.1016/j.cie.2022.108513
Waters, D. (2011). Supply chain risk management: vulnerability and resilience in logistics. Kogan Page Publishers.
Wieland, A., and Carl Marcus, W. (2013). The influence of relational competencies on supply chain resilience: a relational view. International Journal of Physical Distribution & Logistics Management, 43(4), 300-320. https://doi.org/10.1108/IJPDLM-08-2012-0243
Zhu, Y., Garai, A., Karmakar, R., Sarkar, B., & Mazumder, S. (2024). Customer-centric policies for environmentally sustainable manufacturing of deteriorating items with varying quality control practices under disruptions. Computers & Industrial Engineering, 109895. https://doi.org/10.1016/j.cie.2024.109895
Zsidisin, George A., & Wagner, S. M. (2010). Do Perceptions Become Reality? The Moderating Role of Supply Chain Resiliency on Disruption Occurrence.Journal of Business Logistics, 31(2), 1–20. https://doi.org/10.1002/j.2158-1592.2010.tb00140.x
