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طراحی شبکه زنجیره تأمین پایدار و قابل اطمینان تحت عدم قطعیت (مطالعه موردی: غرب کارتن) | ||
مدیریت زنجیره تأمین | ||
دوره 25، شماره 81، اسفند 1402، صفحه 87-115 اصل مقاله (1.37 M) | ||
نوع مقاله: پژوهشی | ||
نویسندگان | ||
سجاد امیریان* 1؛ مقصود امیری2؛ محمدتقی تقوی فرد2 | ||
1دکتری مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران، ایران | ||
2استاد گروه مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران، ایران | ||
تاریخ دریافت: 22 مهر 1402، تاریخ بازنگری: 03 آبان 1402، تاریخ پذیرش: 03 آبان 1402 | ||
چکیده | ||
مشتریان در حال حاضر بیش از هر زمان دیگری به پایداری و قابلیت اطمینان بستهبندی محصولات اهمیت میدهند. در این پژوهش یک مسئله طراحی شبکه زنجیره تأمین حلقه بسته به صورت چند محصولی و چند دورهای با سه هدف سودآوری، مسئولیتپذیری اجتماعی و قابلیت اطمینان تحت شرایط عدم قطعیت مد نظر قرار گرفته است. از اعداد فازی مثلثی برای پارامترهای غیرقطعی و از رویکرد برنامهریزی امکانی استوار با مقیاس Me برای مقابله با محدودیتهای فازی استفاده شده است. رویکرد پیشنهادی نیاز به بررسی تکراری توسط تصمیمگیرندگان را با ارائه انتخابهای نامحدودی از طیف خوشبینی- بدبینی مرتفع کرده است. مدل ریاضی توسعه یافته در این پژوهش از نوع برنامهریزی خطی عدد صحیح مختلط میباشد، که برای حل آن و یافتن جوابهای بهینه پارتو، روش محدودیت اپسیلون تقویت شده (AEC) در نرمافزار GAMS پیادهسازی شده است. صحت عملکرد کلی مدل پیشنهادی با چهار نمونه (بر اساس ضرایب توابع هدف) از یک مطالعه موردی در صنعت کارتنسازی مورد ارزیابی قرار گرفته است. نتایج به دست آمده از وجود تضاد بین سه تابع هدف حکایت دارد. با این حساب تصمیمگیرندگان باید در مقایسه با وضعیتی که فقط جنبه اقتصادی در نظر گرفته میشود، برای حفاظت بیشتر از محیط زیست و بهبود قابلیت اطمینان، سود کمتری مطالبه کنند. تغییرپذیری فضای موجه تصمیم در معیار Me از طریق امکان تبادل میان تابع هدف و سطح خطرپذیری مدیران به حل انعطافپذیرتر و نزدیک به واقعیت مسئله طراحی شبکه زنجیره تأمین کمک کرده است. | ||
کلیدواژهها | ||
پایداری؛ قابلیت اطمینان؛ شبکه زنجیره تأمین حلقه بسته؛ برنامهریزی امکانی استوار | ||
عنوان مقاله [English] | ||
Designing a Sustainable and Reliable Supply Chain Network Under Uncertainty (Case Study: West of Carton) | ||
نویسندگان [English] | ||
Sajad Amirian1؛ Maghsoud Amiri2؛ Mohammad Taghi Taghavifard2 | ||
1Allameh Tabataba'i University, Tehran, Iran | ||
2Professor, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran. | ||
چکیده [English] | ||
Customers now care more than ever about the sustainability and reliability of products packaging. This research considers a multi-product and multi-period closed-loop supply chain design problem with three objectives of profitability, social responsibility, and reliability under uncertain conditions. Triangular fuzzy numbers have been used for non-deterministic parameters and a robust probabilistic programming approach with Me scale has been used to deal with fuzzy constraints. The proposed approach eliminates the need for iterative consideration by decision-makers by providing unlimited choices from the optimism-pessimism spectrum. The mathematical model developed in this study is of mixed integer linear programming type, which is implemented Augmented Epsilon Constraint (AEC) method in GAMS software to solve it and find Pareto optimal solutions. The accuracy of the overall performance of the proposed model has been evaluated with four examples (based on the coefficients of the objective functions) from a case study in the Carton-Making Industry. The obtained results indicate the existence of a conflict between the three objective functions. With this account, decision-makers should demand lower profits for increased environmental protection and improved reliability compared to the situation where only the economic aspect is considered. The variability of the justified decision space in the Me criterion has helped to solve the supply chain network design problem more flexibly and closer to reality through the possibility of exchange between the objective function and the risk-taking level of the managers. | ||
کلیدواژهها [English] | ||
Sustainability, Reliability, Closed-Loop Supply Chain Network, Robust Possibilistic Programming | ||
مراجع | ||
[1] S. Luthra and S. K. Mangla, “Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies,” Process Saf. Environ. Prot., vol. 117, pp. 168–179, 2018. DOI: 10.1016/j.psep.2018.04.018 [2] A. A. Nand, R. Menon, A. Bhattacharya, and R. Bhamra, “A review of sustainability trade-offs affecting suppliers in developed and less developed countries,” J. Bus. Ind. Mark., vol. 38, no. 3, pp. 463–483, 2023. DOI: 10.1108/JBIM-04-2021-0213 [3] S. A. R. Khan, K. Zkik, A. Belhadi, and S. S. Kamble, “Evaluating barriers and solutions for social sustainability adoption in multi-tier supply chains,” Int. J. Prod. Res., vol. 59, no. 11, pp. 3378–3397, 2021. DOI: 10.1080/00207543.2021.1876271 [4] B. Fahimnia and A. Jabbarzadeh, “Marrying supply chain sustainability and resilience: A match made in heaven,” Transp. Res. Part E: Logist. Trans. Rev., vol. 91, pp. 306–324, 2016. DOI: 10.1016/j.tre.2016.02.007 [5] D. Ivanov and A. Das, “Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: a research note,” Int. J. Integr. Supply Manag., vol. 13, no. 1, p. 90, 2020. DOI: 10.1504/IJISM.2020.107780 [6] S. Singh, R. Kumar, R. Panchal, and M. K. Tiwari, “Impact of COVID-19 on logistics systems and disruptions in food supply chain,” Int. J. Prod. Res., vol. 59, no. 7, pp. 1993–2008, 2021. DOI: 10.1080/00207543.2020.1792000 [7] Y. Sheffi, The resilient enterprise: overcoming vulnerability for competitive advantage. Pearson Education India, 2007. [8] K. Govindan, H. Mina, and B. Alavi, “A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19),” Transp. Res. Part E: Logist. Trans. Rev., vol. 138, no. 101967, p. 101967, 2020. DOI: 10.1016/j.tre.2020.101967 [9] J. Moosavi and S. Hosseini, “Simulation-based assessment of supply chain resilience with consideration of recovery strategies in the COVID-19 pandemic context,” Comput. Ind. Eng., vol. 160, no. 107593, p. 107593, 2021. DOI: 10.1016/j.cie.2021.107593 [10] A. Chatterjee and A. Layton, “Mimicking nature for resilient resource and infrastructure network design,” Reliab. Eng. Syst. Saf., vol. 204, no. 107142, p. 107142, 2020. DOI: 10.1016/j.ress.2020.107142 [11] D. Ivanov, “Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case,” Transp. Res. Part E: Logist. Trans. Rev., vol. 136, no. 101922, p. 101922, 2020. DOI: 10.1016/j.tre.2020.101922 [12] Y. Esmizadeh and M. Mellat Parast, “Logistics and supply chain network designs: incorporating competitive priorities and disruption risk management perspectives,” Int. J. Logist., vol. 24, no. 2, pp. 174–197, 2021. DOI: 10.1080/13675567.2020.1744546 [13] K. Govindan, M. Fattahi, and E. Keyvanshokooh, “Supply chain network design under uncertainty: A comprehensive review and future research directions,” Eur. J. Oper. Res., vol. 263, no. 1, pp. 108–141, 2017. DOI: 10.1016/j.ejor.2017.04.009 [14] D. Ivanov, “An adaptive framework for aligning (re) planning decisions on supply chain strategy, design, tactics, and operations,” International journal of production research, vol. 48, no. 13, pp. 3999–4017, 2010. DOI: 10.1080/00207540902893417 [15] Y. Meepetchdee and N. Shah, “Logistical network design with robustness and complexity considerations,” Int. J. Phys. Distrib. Logist. Manag., vol. 37, no. 3, pp. 201–222, 2007. DOI: 10.1108/09600030710742425 [16] M. T. Melo, S. Nickel, and F. Saldanha-Da-Gama, “Facility location and supply chain management-A review,” European journal of operational research, vol. 196, no. 2, pp. 401–412, 2009. DOI: 10.1016/j.ejor.2008.05.007 [17] T. Davis, “Effective supply chain management,” Sloan management review, vol. 34, pp. 35–35, 1993. [18] A. Hamidieh, B. Naderi, M. Mohammadi, M. Fazli-Khalaf, and A. Yoshise, “A robust possibilistic programming model for a responsive closed loop supply chain network design,” Cogent Math., vol. 4, no. 1, p. 1329886, 2017. DOI: 10.1080/23311835.2017.1329886 [19] B. Zahiri, R. Tavakkoli-Moghaddam, and M. S. Pishvaee, “A robust possibilistic programming approach to multi-period location–allocation of organ transplant centers under uncertainty,” Comput. Ind. Eng., vol. 74, pp. 139–148, 2014. DOI: 10.1016/j.cie.2014.05.008 [20] D. Bertsimas and M. Sim, “The price of robustness,” Oper. Res., vol. 52, no. 1, pp. 35–53, 2004. DOI: 10.1287/opre.1030.0065 [21] M. S. Pishvaee, J. Razmi, and S. A. Torabi, “Robust possibilistic programming for socially responsible supply chain network design: A new approach,” Fuzzy Sets And Systems, vol. 206, pp. 1–20, 2012. DOI: 10.1016/j.fss.2012.04.010 [22] Z. Qin and X. Ji, “Logistics network design for product recovery in fuzzy environment,” Eur. J. Oper. Res., vol. 202, no. 2, pp. 479–490, 2010. DOI: 10.1016/j.ejor.2009.05.036 [23] J. Xu and X. Zhou, “Approximation based fuzzy multi-objective models with expected objectives and chance constraints: Application to earth-rock work allocation,” Inf. Sci. (Ny), vol. 238, pp. 75–95, 2013. DOI: 10.1016/j.ins.2013.02.011 [24] S. Amirian, M. Amiri, and M. T. Taghavifard, “Sustainable and reliable closed-loop supply chain network design: Normalized Normal Constraint (NNC) method application,” Journal of Industrial and Systems Engineering, vol. 14, no. 3, pp. 33–68, 2022. DOR: 20.1001.1.17358272.2022.14.3.2.1 [25] S. J. Hosseini Dehshiri, M. Amiri, L. Olfat, and M. S. Pishvaee, “Stone paper closed-loop supply chain network design using robust stochastic, possibilistic and flexible chance-constrained programming,” Journal of Industrial Management Perspective, vol. 12, no. 1, pp. 45–81, 2022. (In Persian) DOI: 10.52547/jimp.12.1.45 [26] S. Ghayebloo, M. J. Tarokh, U. Venkatadri, and C. Diallo, “Developing a bi-objective model of the closed-loop supply chain network with green supplier selection and disassembly of products: The impact of parts reliability and product greenness on the recovery network,” J. Manuf. Syst., vol. 36, pp. 76–86, 2015. DOI: 10.1016/j.jmsy.2015.02.011 [27] S. Khalifehzadeh, M. Seifbarghy, and B. Naderi, “A four-echelon supply chain network design with shortage: Mathematical modeling and solution methods,” J. Manuf. Syst., vol. 35, pp. 164–175, 2015. DOI: 10.1016/j.jmsy.2014.12.002 [28] D. Rahmani and V. Mahoodian, “Strategic and operational supply chain network design to reduce carbon emission considering reliability and robustness,” J. Clean. Prod., vol. 149, pp. 607–620, 2017. DOI: 10.1016/j.jclepro.2017.02.068 [29] Y. Li, B. Liu, and T.-C. (T C. ). Huan, “Renewal or not? Consumer response to a renewed corporate social responsibility strategy: Evidence from the coffee shop industry,” Tour. Manag., vol. 72, pp. 170–179, 2019. DOI: 10.1016/j.tourman.2018.10.031 [30] B. Zahiri, J. Zhuang, and M. Mohammadi, “Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study,” Transp. Res. Part E: Logist. Trans. Rev., vol. 103, pp. 109–142, 2017. DOI: 10.1016/j.tre.2017.04.009 [31] M. Fattahi and K. Govindan, “A multi-stage stochastic program for the sustainable design of biofuel supply chain networks under biomass supply uncertainty and disruption risk: A real-life case study,” Transp. Res. Part E: Logist. Trans. Rev., vol. 118, pp. 534–567, 2018. DOI: 10.1016/j.tre.2018.08.008 [32] A. Jabbarzadeh, B. Fahimnia, and F. Sabouhi, “Resilient and sustainable supply chain design: sustainability analysis under disruption risks,” Int. J. Prod. Res., vol. 56, no. 17, pp. 5945–5968, 2018. DOI: 10.1080/00207543.2018.1461950 [33] M. Fazli-Khalaf, B. Naderi, M. Mohammadi, and M. S. Pishvaee, “Design of a sustainable and reliable hydrogen supply chain network under mixed uncertainties: A case study,” Int. J. Hydrogen Energy, vol. 45, no. 59, pp. 34503–34531, 2020. DOI: 10.1016/j.ijhydene.2020.05.276 [34] E. B. Tirkolaee, A. Goli, A. Faridnia, M. Soltani, and G.-W. Weber, “Multi-objective optimization for the reliable pollution-routing problem with cross-dock selection using Pareto-based algorithms,” J. Clean. Prod., vol. 276, no. 122927, p. 122927, 2020. DOI: 10.1016/j.jclepro.2020.122927 [35] S. A. Yawar and S. Seuring, “Management of social issues in supply chains: A literature review exploring social issues, actions and performance outcomes,” J. Bus. Ethics, vol. 141, no. 3, pp. 621–643, 2017. DOI: 10.1007/s10551-015-2719-9 [36] Z. Mehrjerdi and Y. Lotfi, “Development of a mathematical model for sustainable closed-loop supply chain with efficiency and resilience systematic framework,” International Journal of Supply and Operations Management, vol. 6, no. 4, pp. 360–388, 2019. DOI: 10.22034/2019.4.6 [37] Y. Z. Mehrjerdi and M. Shafiee, “A resilient and sustainable closed-loop supply chain using multiple sourcing and information sharing strategies,” J. Clean. Prod., vol. 289, no. 125141, p. 125141, 2021. DOI: 10.1016/j.jclepro.2020.125141 [38] M. Zhou, Y. Duan, W. Yang, Y. Pan, and M. Zhou, “Capacitated multi-modal network flow models for minimizing total operational cost and CO2e emission,” Comput. Ind. Eng., vol. 126, pp. 361–377, 2018. DOI: 10.1016/j.cie.2018.09.049 [39] A. A. Taleizadeh, K. Ahmadzadeh, B. R. Sarker, and A. Ghavamifar, “Designing an optimal sustainable supply chain system considering pricing decisions and resilience factors,” J. Clean. Prod., vol. 332, no. 129895, p. 129895, 2022. DOI: 10.1016/j.jclepro.2021.129895 [40] S.-M. Hosseini-Motlagh, M. R. G. Samani, and V. Shahbazbegian, “Innovative strategy to design a mixed resilient-sustainable electricity supply chain network under uncertainty,” Appl. Energy, vol. 280, no. 115921, p. 115921, 2020. DOI: 10.1016/j.apenergy.2020.115921 [41] Y.-C. Tsao and V.-V. Thanh, “A multi-objective fuzzy robust optimization approach for designing sustainable and reliable power systems under uncertainty,” Appl. Soft Comput., vol. 92, no. 106317, p. 106317, 2020. DOI: 10.1016/j.asoc.2020.106317 [42] M. Fazli-Khalaf, B. Naderi, M. Mohammadi, and M. S. Pishvaee, “The design of a resilient and sustainable maximal covering closed-loop supply chain network under hybrid uncertainties: a case study in tire industry,” Environ. Dev. Sustain., vol. 23, no. 7, pp. 9949–9973, 2021. DOI: 10.1007/s10668-020-01041-0 [43] Govindan, K., & Gholizadeh, H. (2021). Robust network design for sustainable-resilient reverse logistics network using big data: A case study of end-of-life vehicles. Transportation Research Part E: Logistics and Transportation Review, 149, 102279. [44] Z. Sazvar, K. Tafakkori, N. Oladzad, and S. Nayeri, “A capacity planning approach for sustainable-resilient supply chain network design under uncertainty: A case study of vaccine supply chain,” Comput. Ind. Eng., vol. 159, no. 107406, p. 107406, 2021. DOI: 10.1016/j.cie.2021.107406 [45] S. Amirian, M. Amiri, and M. T. Taghavifard, “The emergence of a sustainable and reliable supply chain paradigm in supply chain network design,” Complexity, vol. 2022, pp. 1–29, 2022. DOI: 10.1155/2022/9415465 [46] S. Amirian, M. Amiri, and M. T. Taghavifard, “Integrating Sustainability and Reliability in the Supply Chain: a Systematic Literature Review,” Iranian Journal of Supply Chain Management, vol. 25, no. 79, pp. 123–151, 2023. (In Persian) DOR: 20.1001.1.20089198.1402.25.79.8.2 [47]R. Lotfi, Y. Z. Mehrjerdi, M. S. Pishvaee, A. Sadeghieh, and G.-W. Weber, “A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk,” Numer. Algebra Control Optim., vol. 11, no. 2, p. 221, 2021. DOI: 10.3934/naco.2020023 [48] Z. Sadeghi, O. Boyer, S. Sharifzadeh, and N. Saeidi, “A robust mathematical model for sustainable and resilient supply chain network design: Preparing a supply chain to deal with disruptions,” Complexity, vol. 2021, pp. 1–17, 2021. DOI: 10.1155/2021/9975071 [49] S. Salehi, Y. Zare Mehrjerdi, A. Sadegheih, and H. Hosseini-Nasab, “Designing a resilient and sustainable biomass supply chain network through the optimization approach under uncertainty and the disruption,” J. Clean. Prod., vol. 359, no. 131741, p. 131741, 2022. DOI: 10.1016/j.jclepro.2022.131741 [50] F. Goodarzian, P. Ghasemi, A. Gunasekaren, A. A. Taleizadeh, and A. Abraham, “A sustainable-resilience healthcare network for handling COVID-19 pandemic,” Ann. Oper. Res., vol. 312, no. 2, pp. 761–825, 2022. DOI: 10.1007/s10479-021-04238-2 [51] M. Mohammadi and A. Nikzad, “Sustainable and reliable closed-loop supply chain network design during pandemic outbreaks and disruptions,” Oper. Manag. Res., 2022. DOI: 10.1007/s12063-022-00312-5 [52] R. Eslamipoor and A. Nobari, “A reliable and sustainable design of supply chain in healthcare under uncertainty regarding environmental impacts,” J. Appl. Res. Ind. Eng., vol. 10, no. 2, pp. 256–272, 2023. DOI: 10.22105/jarie.2022.335389.1461 [53] M. Akbari-Kasgari, H. Khademi-Zare, M. B. Fakhrzad, M. Hajiaghaei-Keshteli, and M. Honarvar, “Designing a resilient and sustainable closed-loop supply chain network in copper industry,” Clean Technol. Environ. Policy, vol. 24, no. 5, pp. 1553–1580, 2022. DOI: 10.1007/s10098-021-02266-x [54] S. Mohamadi Nematabad, S. Pourmousa, A. Tajdini, A. Jahan Latibari, and A. Lashgari, “Identifying and prioritizing the components of sustainable production in the corrugated box making industry,” Iranian Journal of Wood and Paper Science Research, vol. 38, no. 1, pp. 21–36, 2023. (In Persian) DOI: 10.22092/ijwpr.2021.356457.1699 [55] G. Estegi, S. Pourmousa, and A. Tajdini, “Identification of in Cleaner Production Indicators in the Corrugated Box Making Industries by Multiple Criteria Decision-Making Methods,” vol. 12, pp. 34–41, 2021. (In Persian) DOR: 20.1001.1.22286675.1400.12.45.3.7 [56] M. Mehri Charvadeh, S. Pourmousa, A. Tajdini, A. Tamjidi, and V. Safdari, Presenting a management model for a multiobjective sustainable supply chain in the cellulosic industry and its implementation by the NSGA-II meta-heuristic algorithm. Discrete Dynamics in Nature and Society. 2022. DOI: 10.1155/2022/8794472 [57] T. Bektaş and G. Laporte, “The pollution-routing problem,” Trans. Res. Part B: Methodol., vol. 45, no. 8, pp. 1232–1250, 2011. DOI: 10.1016/j.trb.2011.02.004 [58] J.-F. Cordeau and G. Laporte, “The dial-a-ride problem: models and algorithms,” Ann. Oper. Res., vol. 153, no. 1, pp. 29–46, 2007. DOI: 10.1007/s10479-007-0170-8 [59] S. Bairamzadeh, M. S. Pishvaee, and M. Saidi-Mehrabad, “Multiobjective robust possibilistic programming approach to sustainable bioethanol supply chain design under multiple uncertainties,” Ind. Eng. Chem. Res., vol. 55, no. 1, pp. 237–256, 2016. DOI: 10.1021/acs.iecr.5b02875 [60] G. Mavrotas and K. Florios, “An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems,” Appl. Math. Comput., vol. 219, no. 18, pp. 9652–9669, 2013. DOI: 10.1016/j.amc.2013.03.002 [61] F. Mohammed, S. Z. Selim, A. Hassan, and M. N. Syed, “Multi-period planning of closed-loop supply chain with carbon policies under uncertainty,” Transp. Res. D Transp. Environ., vol. 51, pp. 146–172, 2017. DOI: 10.1016/j.trd.2016.10.033 | ||
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