طراحی مدل دوهدفه شبکه زنجیره‌تأمین گوشت با درنظر گرفتن قابلیت ارتجاعی تحت شرایط عدم قطعیت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 . کارشناسی ارشد، گروه مهندسی صنایع، دانشکدۀ مهندسی، دانشکدگان فارابی، دانشگاه تهران، تهران، ایران

2 استادیار، گروه مهندسی صنایع، دانشکدۀ مهندسی، دانشکدگان فارابی، دانشگاه تهران، تهران، ایران

3 دانشجوی کارشناسی ارشد، گروه مهندسی صنایع، دانشکده مهندسی، دانشکدگان فارابی، دانشگاه تهران، تهران، ایران

چکیده

امروزه، زﻧﺠﻴﺮه‌تأمین ﮔﻮﺷﺖ ﻧﻘﺶ اساسی در ﺗﺄﻣﻴﻦ ﻧﻴﺎز ﻏﺬاﻳﻲ خانواده، سلامت و امنیت غذایی ﺟﺎﻣﻌﻪ دارد. محیط ذاتاً پویا و غیرقطعی و ریسک‌ها و اختلالات موجود در زﻧﺠﻴﺮه‌تأمین گوشت، بهینه‌سازی زﻧﺠﻴﺮه‌تأمین با قابلیت ارتجاعی (تاب‌آوری) را اجتناب‌ناپذیر کرده است. به‌دلایل مختلف، تأمین‌کنندگان امکان دارد دچار اختلال و آسیب از نوع جزئی شوند و نتوانند در موعد مناسب به مشتریان خود سرویس‌دهی کنند. بدین‌منظور در این مقاله به طراحی شبکه زﻧﺠﻴﺮه‌تأمین گوشت تازه به‌طور یکپارچه با درنظر گرفتن استراتژی‌های انعطاف‌پذیری از افزایش ظرفیت از عقد قرارداد با تأمین‌کنندگان قابل‌اطمینان تحت شرایط عدم قطعیت تقاضا با رویکرد استوار پرداخته‌ شده است. به حداقل رساندن کل هزینه‌های حمل‌ونقل و هزینه‌های ثابت و به حداکثر رساندن سطح سرویس زﻧﺠﻴﺮه‌تأمین از اهداف این پژوهش است. انتخاب مزارع از بین مزارع موجود، تخصیص مکان به کشتار‌گاه‌ها، انتخاب خرده‌فروشان به‌منظور فروش محصولات گوشتی و فرآورده گوشتی، تعیین جریان مواد انتخاب‌ شده بین امکانات در سطوح شبکه زﻧﺠﻴﺮه‌تأمین پیشنهادی، تعیین ریسک‌های شبکه زﻧﺠﻴﺮه‌تأمین گوشت و تعیین میزان سطح سرویس زﻧﺠﻴﺮه‌تأمین از مهم‌ترین تصمیمات کلیدی شبکه زﻧﺠﻴﺮه‌تأمین پیشنهادی است. مدل‌سازی این مسأله بر پایه‌ی برنامه‌ریزی عدد صحیح مختلط دوهدفه صورت گرفته ‌است. در پایان از روش حل اپسیلون-محدودیت تقویت‌شده2- به‌کار برده شده ‌است و عملکرد و کارایی مدل در قالب مثال‌های عددی مورد بررسی و تجزیه‌وتحلیل  قرارگرفته و با مدل پایه موجود در ادبیات پژوهش مقایسه شده‌ است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Designing a Bi-objective Meat Supply Chain Network Model with Resilience under Uncertainty

نویسندگان [English]

  • Hosein Darestani 1
  • Babak Javadi 2
  • Mohammad Mousazadeh 2
  • Mohammad Reza Abdali 3
1 MSc, Department of Industrial Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Tehran, Iran
2 Assistant professor, Department of Industrial Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Tehran, Iran
3 MSc, Department of Industrial Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Tehran, Iran
چکیده [English]

The meat supply chain is crucial in meeting household nutritional needs, public health, and food security. The meat supply chain's inherent dynamic and uncertain nature, along with existing risks and disruptions, has made optimizing the supply chain for resilience unavoidable. For various reasons, suppliers may experience partial disruptions and be unable to service their customers promptly. In this paper, we propose an integrated design of a fresh meat supply chain network that considers resilience strategies such as capacity expansion through contracts with reliable suppliers under demand uncertainty using a robust approach. The objectives of this research are to minimize total transportation costs and fixed costs and maximize the service level of the supply chain. Key decisions in the proposed supply chain network include selecting farms from available farms, allocating locations to slaughterhouses, selecting retailers for selling meat products and processed meat products, determining the flow of selected materials between facilities at different levels of the proposed supply chain network, identifying meat supply chain network risks, and determining the level of supply chain service. The problem is modeled based on a bi-objective mixed-integer programming approach. Finally, the augmented ε-constraint method is used to solve the problem, and the performance and efficiency of the model are evaluated and analyzed using numerical examples and compared to the base model in the research literature.

کلیدواژه‌ها [English]

  • Meat Supply Chain
  • Two Objective Optimization
  • Resiliency
  • Uncertainty
  • Service Level
  • Epsilon Constraint Augmented-2

مراجع

  • Gholami-Zanjani, S. M., Jabalameli, M. S. & Pishvaee, M. S., (2021). “A resilient-green model for multi-echelon meat supply chain planning”. Computers & Industrial Engineering, vol. 152, p. 107018, https://doi.org/10.1016/j.cie.2020.107018.
  • کوچک زاده، ز.، غلامی، س.و رحمانی، د.، (1401). «ارائه مدل بهینه‌سازی استوار برای طراحی شبکه زنجیره‌تأمین حلقه بسته سبز کالاهای فاسدشدنی». نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید، دوره  10 شماره 20، 131-113 https://doi.org/10.22084/ier.2023.26822.2095.
  • Mohebalizadehgashti, F., Zolfagharinia, H. & Amin, S. H., (2020). “Designing a green meat supply chain network: A multi-objective approach”. International Journal of Production Economics, vol. 219, https://doi.org/10.1016/j.ijpe.2019.07.007.
  • James, S. J., James, C. & Evans, J. A., (2006). “Modelling of food transportation systems - a review”. International Journal of Refrigeration, vol. 29, no. 6, pp. 947–957, https://doi.org/10.1016/j.ijrefrig.2006.03.017.
  • Soysal, M., (2012). “A Review on Quantitative Models for Sustainable Food Logistics Management”. International Journal on Food System Dynamics, vol. 3, no. 2, pp. 136–155, https://doi.org/10.18461/ijfsd.v3i2.324.
  • Rijpkema, W. A., Hendrix, E. M. T. Rossi, R. & van der Vorst, J. G. A. J., (2016). “Application of stochastic programming to reduce uncertainty in quality-based supply planning of slaughterhouses”. Annals of Operations Research, vol. 239, no. 2, pp. 613–624, https://doi.org/10.1007/s10479-013-1460-y.
  • Mogale, D. G., Kumar, M., Kumar, S. K. & Tiwari, M. K., (2018). “Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network”. Transportation Research Part E: Logistics and Transportation Review, vol. 111, no. January, pp. 40–69, https://doi.org/10.1016/j.tre.2018.01.004.
  • Villegas, J. G., Palacios, F. & Medaglia, A. L., (2006). “Solution methods for the bi-objective (cost-coverage) unconstrained facility location problem with an illustrative example”. Annals of Operations Research, vol. 147, no. 1, pp. 109–141, https://doi.org/10.1007/s10479-006-0061-4.
  • Paksoy, T., Pehlivan, N. Y. & Özceylan, E., (2012). “Application of fuzzy optimization to a supply chain network design: A case study of an edible vegetable oils manufacturer”. Applied Mathematical Modelling, vol. 36, no. 6, pp. 2762–2776, https://doi.org/10.1016/j.apm.2011.09.060.
  • Teimoury, E., Nedaei, H., Ansari, S. & Sabbaghi, M., (2013). “A multi-objective analysis for import quota policy making in a perishable fruit and vegetable supply chain: A system dynamics approach”. Computers and Electronics in Agriculture, vol. 93, pp. 37–45, https://doi.org/10.1016/j.compag.2013.01.010.
  • García-Flores, R., Higgins, A., Prestwidge, D. & McFallan, S., (2014). “Optimal location of spelling yards for the northern Australian beef supply chain”. Computers and Electronics in Agriculture, vol. 102, pp. 134–145, https://doi.org/10.1016/j.compag.2014.01.015.
  • Mohammed, A. & Wang, Q., (2015). “Integrity of an RFID-enabled HMSC Network”. 2015.
  • Mohammed, A. & Wang, Q., (2017). “Developing a meat supply chain network design using a multi-objective possibilistic programming approach”. British Food Journal, vol. 119, no. 3, pp. 690–706, https://doi.org/10.1108/BFJ-10-2016-0475.
  • Mohammed, A. & Wang, Q., (2017). “Multi-criteria optimization for a cost-effective design of an RFID-based meat supply chain”. British Food Journal, vol. 119, no. 3, pp. 676–689, https://doi.org/10.1108/BFJ-03-2016-0122.
  • Soysal, M., Bloemhof-Ruwaard, J. M. & van der Vorst, J. G. A. J., (2014). “Modelling food logistics networks with emission considerations: The case of an international beef supply chain”. International Journal of Production Economics, vol. 152, pp. 57–70, https://doi.org/10.1016/j.ijpe.2013.12.012.
  • Babbar, C. & Amin, S. H., (2018). “A multi-objective mathematical model integrating environmental concerns for supplier selection and order allocation based on fuzzy QFD in beverages industry”. Expert Systems with Applications, vol. 92, pp. 27–38, https://doi.org/10.1016/j.eswa.2017.09.041
  • Bortolini, M., Faccio, M., Ferrari, E., Gamberi, M. & Pilati, F., (2016). “Fresh food sustainable distribution: cost, delivery time and carbon footprint three-objective optimization”. Journal of Food Engineering, vol. 174, pp. 56–67, https://doi.org/10.1016/j.jfoodeng.2015.11.014.
  • Gholamian, N., Mahdavi, I., Tavakkoli-Moghaddam, R. & Mahdavi-Amiri, N., (2015). “Comprehensive fuzzy multi-objective multi-product multi-site aggregate production planning decisions in a supply chain under uncertainty”. Applied Soft Computing, vol. 37, pp. 585–607, https://doi.org/10.1016/j.asoc.2015.08.041.
  • Jeihoonian, M., Kazemi Zanjani, M. & Gendreau, M., (2017). “Closed-loop supply chain network design under uncertain quality status: Case of durable products”. International Journal of Production Economics, vol. 183, pp. 470–486, https://doi.org/10.1016/j.ijpe.2016.07.023.
  • Jabbarzadeh, A., Fahimnia, B. & Seuring, S., (2014). “Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application”. Transportation Research Part E: Logistics and Transportation Review, vol. 70, pp. 225–244, https://doi.org/10.1016/j.tre.2014.06.003.
  • Amin, S. H., Zhang, G. & Akhtar, P., (2017). “Effects of uncertainty on a tire closed-loop supply chain network”. Expert Systems with Applications, vol. 73, pp. 82–91, https://doi.org/10.1016/j.eswa.2016.12.024.
  • Liang, T.-F., (2006). “Distribution planning decisions using interactive fuzzy multi-objective linear programming”. Fuzzy Sets and Systems, vol. 157, no. 10, pp. 1303–1316, https://doi.org/10.1016/j.fss.2006.01.014.
  • Mirzapour Al-e-hashem, S. M. J., Malekly, H. & Aryanezhad, M. B., (2011). “A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty”. International Journal of Production Economics, vol. 134, no. 1, pp. 28–42, https://doi.org/10.1016/j.ijpe.2011.01.027.
  • Mirakhorli, A., (2014). “Fuzzy multi-objective optimization for closed loop logistics network design in bread-producing industries”. International Journal of Advanced Manufacturing Technology, vol. 70, no. 1–4, pp. 349–362, https://doi.org/10.1007/s00170-013-5264-7.
  • Yang, G. Q., Liu, Y. K. & Yang, K., (2015). “Multi-objective biogeography-based optimization for supply chain network design under uncertainty”. Computers and Industrial Engineering, vol. 85, pp. 145–156, https://doi.org/10.1016/j.cie.2015.03.008.
  • Azadeh, A., Shafiee, F., Yazdanparast, R., Heydari, J. & Fathabad, A. M., (2017). “Evolutionary multi-objective optimization of environmental indicators of integrated crude oil supply chain under uncertainty”. Journal of Cleaner Production, vol. 152, pp. 295–311, https://doi.org/10.1016/j.jclepro.2017.03.105.
  • Mohammed, A., Wang, Q. & Li, X., (2017). “A cost-effective decision-making algorithm for an RFID-enabled HMSC network design A multi-objective approach”. Industrial Management and Data Systems, vol. 117, no. 9, pp. 1782–1799, https://doi.org/10.1108/IMDS-02-2016-0074.
  • Rahimi, E., Paydar, M. M., Mahdavi, I., Jouzdani, J. & Arabsheybani, A., (2018). “A robust optimization model for multi-objective multi-period supply chain planning under uncertainty considering quantity discounts”. Journal of Industrial and Production Engineering, vol. 35, no. 4, pp. 214–228, https://doi.org/10.1080/21681015.2018.1441195.
  • Yu, J., Gan, M., Ni, S., & Chen, D., (2018). “Multi-objective models and real case study for dual-channel FAP supply chain network design with fuzzy information”. Journal of Intelligent Manufacturing, vol. 29, no. 2, pp. 389–403, 2018, https://doi.org/10.1007/s10845-015-1115-8.
  • Pishvaee, M. S. & Torabi, S. A., (2010). “A possibilistic programming approach for closed-loop supply chain network design under uncertainty”. Fuzzy Sets and Systems, vol. 161, no. 20, pp. 2668–2683, https://doi.org/10.1016/j.fss.2010.04.010.
  • Pishvaee, M. S., Rabbani, M. & Torabi, S. A., (2011). “A robust optimization approach to closed-loop supply chain network design under uncertainty”. Applied Mathematical Modelling, vol. 35, no. 2, pp. 637–649, https://doi.org/10.1016/j.apm.2010.07.013.
  • Ramezani, M., Bashiri, M. & Tavakkoli-Moghaddam, R., (2013). “A robust design for a closed-loop supply chain network under an uncertain environment”. International Journal of Advanced Manufacturing Technology, vol. 66, no. 5–8, pp. 825–843, https://doi.org/10.1007/s00170-012-4369-8.
  • Lalmazloumian, M., Wong, K. Y., Govindan, K. & Kannan, D., (2016). “A robust optimization model for agile and build-to-order supply chain planning under uncertainties”. Annals of Operations Research, vol. 240, no. 2, pp. 435–470, https://doi.org/10.1007/s10479-013-1421-5.
  • Ramezani, M., Kimiagari, A. M., Karimi, B. & Hejazi, T. H., (2014). “Closed-loop supply chain network design under a fuzzy environment”. Knowledge-Based Systems, vol. 59, pp. 108–120, https://doi.org/10.1016/j.knosys.2014.01.016.
  • Baghalian, A., Rezapour, S. & Farahani, R. Z., (2013). “Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case”. European Journal of Operational Research, vol. 227, no. 1, pp. 199–215, https://doi.org/10.1016/j.ejor.2012.12.017.
  • Sabouhi, F., Pishvaee, M. S. & Jabalameli, M. S., (2018). “Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain”. Computers & Industrial Engineering, vol. 126, pp. 657–672, https://doi.org/10.1016/j.cie.2018.10.001.
  • Zahiri, B., Zhuang, J. & Mohammadi, M., (2017). “Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study”. Transportation Research Part E: Logistics and Transportation Review, vol. 103, no. 2017, pp. 109–142, https://doi.org/10.1016/j.tre.2017.04.009.
  • Rezapour, S., Farahani, R. Z. & Pourakbar, M., (2017). “Resilient supply chain network design under competition: A case study”. European Journal of Operational Research, vol. 259, no. 3, pp. 1017–1035, https://doi.org/10.1016/j.ejor.2016.11.041.
  • Jabbarzadeh, A., Fahimnia, B., Sheu, J. B. & Moghadam, H. S., (2016). “Designing a supply chain resilient to major disruptions and supply/demand interruptions”. Transportation Research Part B: Methodological, vol. 94, pp. 121–149, https://doi.org/10.1016/j.trb.2016.09.004.
  • Kamalahmadi, M. & Mellat-Parast, M., (2016). “Developing a resilient supply chain through supplier flexibility and reliability assessment”. International Journal of Production Research, vol. 54, no. 1, pp. 302–321, https://doi.org/10.1080/00207543.2015.1088971.
  • Meena, P. L. & Sarmah, S. P., (2013). “Multiple sourcing under supplier failure risk and quantity discount: A genetic algorithm approach”. Transportation Research Part E: Logistics and Transportation Review, vol. 50, no. 1, pp. 84–97, https://doi.org/10.1016/j.tre.2012.10.001.
  • Jabbarzadeh, A., Fahimnia, B. & Sabouhi, F., (2018). “Resilient and sustainable supply chain design: sustainability analysis under disruption risks”. International Journal of Production Research, vol. 56, no. 17, pp. 5945–5968, https://doi.org/10.1080/00207543.2018.1461950.
  • Sawik, T., (2013). “Selection of resilient supply portfolio under disruption risks”. Omega (United Kingdom), vol. 41, no. 2, pp. 259–269, https://doi.org/10.1016/j.omega.2012.05.003.
  • Church, R. & Scaparra, M. P., (2007). “Analysis of Facility Systems’ Reliability When Subject to Attack or a Natural Disaster”. in Critical Infrastructure: Reliability and Vulnerability, A. T. Murray and T. H. Grubesic, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 221–241. https://doi.org/10.1007/978-3-540-68056-7_11.
  • تیکنی، ح.، ستاک، م.، شاکری، ز.، (1399). «مدل‌سازی و حل مسأله مکان‌یابی- مسیریابی برای محصولات فسادپذیر در گراف چندگانه با درنظر گرفتن آلودگی وسایل نقلیه و اختلال انبارها». نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید، دوره 8، شماره 16، 171-183.https://doi.org/10.22084/ier.2020.21281.1953
  • Listeş, O. & Dekker, R., (2005). “A stochastic approach to a case study for product recovery network design”. European Journal of Operational Research, vol. 160, no. 1, pp. 268–287, https://doi.org/10.1016/j.ejor.2001.12.001.
  • Zahiri, B., Suresh, N. C. & de Jong, J., (2020). “Resilient hazardous-materials network design under uncertainty and perishability”. Computers & Industrial Engineering, vol. 143, p. 106401, https://doi.org/10.1016/j.cie.2020.106401.
  • Sabri, E. H. & Beamon, B. M., (2000). “A multi-objective approach to simultaneous strategic and operational planning in supply chain design”. Omega, vol. 28, no. 5, pp. 581–598, https://doi.org/10.1016/S0305-0483(99)00080-8.
  • Chen, C.-L. & Lee, W.-C., (2004). “Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices”. Computers & Chemical Engineering, vol. 28, no. 6, pp. 1131–1144, https://doi.org/10.1016/j.compchemeng.2003.09.014.
  • Torabi, S. A. & Hassini, E., (2008). “An interactive possibilistic programming approach for multiple objective supply chain master planning”. Fuzzy Sets and Systems, vol. 159, no. 2, pp. 193–214, https://doi.org/10.1016/j.fss.2007.08.010.
  • Azaron, A., Brown, K. N., Tarim, S. A. & Modarres, M., (2008). “A multi-objective stochastic programming approach for supply chain design considering risk”. International Journal of Production Economics, vol. 116, no. 1, pp. 129–138, https://doi.org/10.1016/j.ijpe.2008.08.002.
  • Liu, S. & Papageorgiou, L. G., (2013). “Multiobjective optimisation of production, distribution and capacity planning of global supply chains in the process industry”. Omega, vol. 41, no. 2, pp. 369–382, https://doi.org/10.1016/j.omega.2012.03.007.
  • Kler, R. et al., (2022). “Optimization of Meat and Poultry Farm Inventory Stock Using Data Analytics for Green Supply Chain Network”. Discrete Dynamics in Nature and Society, vol. 2022, https://doi.org/10.1155/2022/8970549.
  • Arabsheybani, A., Arshadi Khamseh, A. & Pishvaee, M. S., (2023). “Optimizing green supply chain for perishable products considering nano-silver packaging under uncertain demand”. Environment, Development and Sustainability, https://doi.org/10.1007/s10668-023-03057-8.
  • Al Theeb, N., Abu-Aleqa, M. & Diabat, A., (2024). “Multi-objective optimization of two-echelon vehicle routing problem: Vaccines distribution as a case study”. Computers and Industrial Engineering, vol. 187, https://doi.org/10.1016/j.cie.2023.109590.
  • Li, Z. & Zhang, C., (2024). “Designing a two-stage model for the resilient agri-food supply chain network under dynamic competition”. British Food Journal, vol. 126, no. 2, https://doi.org/10.1108/BFJ-12-2022-1135.
  • Caglayan, N. & Satoglu, S. I., (2021). “Multi-objective two-stage stochastic programming model for a proposed casualty transportation system in large-scale disasters: A case study”. Mathematics, vol. 9, no. 4, pp. 1–22, https://doi.org/10.3390/math9040316.
نشریه پژوهش های مهندسی صنایع در سیستم های تولید
دوره 12، شماره 24
شهریور 1403
صفحه 147-165

فایل ها

هم رسانی

ارجاع به این مقاله

آمار