نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه ‌مهندسی‌ صنایع، ‌واحد ‌تهران ‌مرکزی،‌ دانشگاه ‌آزاد ‌اسلامی، ‌تهران، ‌ایران.

2 گروه ‌مهندسی‌ صنایع،‌ واحد ‌علوم ‌و ‌تحقیقات،‌ ‌دانشگاه ‌آزاد ‌اسلامی،‌ تهران،‌ ایران.

3 گروه ‌مهندسی‌ صنایع،‌ واحد ‌علوم ‌و ‌تحقیقات،‌ ‌دانشگاه ‌آزاد ‌اسلامی،‌ تهران،‌ ایران

چکیده

هدف: ایجاد ساختار و گسترش زنجیره‌های تامین حلقه بسته پایدار برای برآوردن استانداردهای زیست‌محیطی، اقتصادی و اجتماعی در جهت تقویت موقعیت در بازارهای رقابتی بسیار حیاتی است. این مطالعه به‌منظور تصمیم‌گیری در سطوح عملیاتی و تاکتیکی برای پیکربندی شبکه زنجیره‌تامین حلقه بسته پایدار با هدف حداکثرسازی ارزش خالص فعلی و به دنبال حداقل‌سازی میزان انتشار کربن با حفظ سیاست‌های سازگار با محیط‌زیست و در نظر گرفتن تورم انجام شده است.
روش‌شناسی پژوهش: این مقاله رویکرد بهینه‌سازی فازی استوار برای مقابله با عدم قطعیت‌های موجود در زنجیره‌تامین حلقه بسته پایدار را در نظر می‌گیرد. هم‌چنین به دلیل پیچیدگی مدل و چندهدفه بودن آن از یک روش جدید ترکیبی الگوریتم اکتشافی و برنامه‌ریزی آرمانی چندگزینه‌ای با تابع مطلوبیت استفاده می‌شود. مدل برنامه‌ریزی خطی عدد صحیح مختلط پیشنهادی در صنعت الکترونیک اعمال می‌شود.
یافته‌ها: مدل پیشنهادی در چندین آزمایش ارزیابی شده و در سناریوهای مختلف مورد بحث قرار می‌گیرد تا کارایی و اعتبار مدل و روش پیشنهادی تایید شود. نتایج  با دو عامل شکاف بهینه و زمان حل مقایسه شد که عملکرد مناسب روش پیشنهادی را نشان داد. سپس، نتایج تاکتیکی و استراتژی مدل برای مطالعه موردی ارایه شد که در آن جریان بهینه بین تسهیلات، انتخاب تامین‌کنندگان مناسب، انتخاب نوع حمل‌ونقل و افتتاح تسهیلات ارایه شد. یافته‌ها نشان داد که در سناریوهای مختلف بهبود موثر راه‌حل‌های به‌دست‌آمده با کاهش زمان حل تا %20 می‌تواند برای مشکلات در مقیاس بزرگ پاسخگو باشد.
اصالت/ارزش‌افزوده علمی: این مقاله با در نظر گرفتن یک روش ترکیبی جدید الگوریتم اکتشافی و برنامه‌ریزی آرمانی چندگزینه‌ای با تابع مطلوبیت برای حل مشکل طراحی شبکه زنجیره‌تامین حلقه بسته پایدار تحت عدم قطعیت طراحی می‌شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Sustainable closed-loop supply chain network design: heuristic hybrid approach with considering inflation and carbon emission policies

نویسندگان [English]

  • Saeid Kalantari 1
  • Hamed Kazemipoor 1
  • Farzad Movahedi Sobhani 2
  • Seyyed Mohammad Hadji Molana 3

1 Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

2 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

چکیده [English]

Purpose: Establishing the structure and expansion of sustainable closed-loop supply chains is critical to meeting environmental, economic, and social standards to strengthen their position in competitive markets. This study aims to decide on operational and tactical levels to configure the Stable Closed Chain Supply Chain Network (SCLSC) to maximize Net Present Value (NPV) and seek to minimize carbon emissions while maintaining environmentally friendly policies and considering inflation.
Methodology: This paper considers a solid Fuzzy Robust Optimization (FRO) approach to deal with stable, closed-loop supply chain uncertainties. Also, due to the complexity of the model and its multi-objective, a new combined method of Heuristic algorithm (HA) and Multi-Choice Goal Programming with Utility Function (MCGP-UF) is used. The proposed Mixed Integer Linear Programming (MILP) model is applied in the electronics industry.
Findings: The proposed model is evaluated in several experiments and discussed in different scenarios to confirm the efficiency and validity of the proposed model and method. The results were compared with the two factors of optimal gap and solution time, which showed the proper performance of the proposed method. Then, the tactical results and model strategy were presented for a case study in which the optimal flow between facilities, selection of suitable suppliers, selection of transportation type, and opening of facilities were presented. The findings showed that in different scenarios, the effective improvement of the obtained solutions by reducing the solution time by twenty percent could address large-scale problems.
Originality/Value: By considering a new combined method of heuristic algorithm and multi-choice ideal programming with a utility function, this paper is done to solve the problem of designing a stable closed-loop supply chain network under uncertainty.

کلیدواژه‌ها [English]

  • Fuzzy robust optimization
  • Net present value
  • Multi-choice goal programming with utility function
  • Sustainable closed-loop supply chain
  • Heuristic algorithm
[1]     Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International journal of production research, 57(7), 2117–2135.
[2]     Ansari, Z. N., & Kant, R. (2017). A state-of-art literature review reflecting 15 years of focus on sustainable supply chain management. Journal of cleaner production, 142, 2524–2543. DOI:10.1016/j.jclepro.2016.11.023
[3]     Gimenez, C., Sierra, V., & Rodon, J. (2012). Sustainable operations: Their impact on the triple bottom line. International journal of production economics, 140(1), 149–159. DOI:10.1016/j.ijpe.2012.01.035
[4]     Acquaye, A., Ibn-Mohammed, T., Genovese, A., Afrifa, G. A., Yamoah, F. A., & Oppon, E. (2018). A quantitative model for environmentally sustainable supply chain performance measurement. European journal of operational research, 269(1), 188–205. DOI:10.1016/j.ejor.2017.10.057
[5]     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. DOI:10.1016/j.tre.2021.102279
[6]     Homayouni, Z., Pishvaee, M. S., Jahani, H., & Ivanov, D. (2023). A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty. Annals of operations research, 324(1–2), 395–435. DOI:10.1007/s10479-021-03985-6
[7]     Ayvaz, B., Bolat, B., & Aydin, N. (2015). Stochastic reverse logistics network design for waste of electrical and electronic equipment. Resources, conservation and recycling, 104, 391–404. DOI:10.1016/j.resconrec.2015.07.006
[8]     Dayhim, M., Jafari, M. A., & Mazurek, M. (2014). Planning sustainable hydrogen supply chain infrastructure with uncertain demand. International journal of hydrogen energy, 39(13), 6789–6801. DOI:10.1016/j.ijhydene.2014.02.132
[9]     Ramezanian, R., & Behboodi, Z. (2017). Blood supply chain network design under uncertainties in supply and demand considering social aspects. Transportation research part E: logistics and transportation review, 104, 69–82. DOI:10.1016/j.tre.2017.06.004
[10]   Motevalli, M. H. D., & Motamedi, M. (2020). Dynamic modeling to evaluate the efficiency of a sequential multilevel supply network. Journal of decisions & operations research, 5(3), 272-289. (In Persian). https://www.journal-dmor.ir/article_120313.html?lang=en
[11]   Jahani, H., Abbasi, B., & Alavifard, F. (2017). Supply chain network reconfiguration in new products launching phase. IEEE international conference on industrial engineering and engineering management (pp. 95–99). IEEE. DOI: 10.1109/IEEM.2017.8289858
[12]   Tofighi, S., Torabi, S. A., & Mansouri, S. A. (2016). Humanitarian logistics network design under mixed uncertainty. European journal of operational research, 250(1), 239–250.
[13]   Liu, M., Liu, R., Zhu, Z., Chu, C., & Man, X. (2018). A bi-objective green closed loop supply chain design problem with uncertain demand. Sustainability (Switzerland), 10(4), 967. DOI:10.3390/su10040967
[14]   Golpîra, H., Sadeghi, H., & Bahramara, S. (2021). Electricity supply chain coordination: Newsvendor model for optimal contract design. Journal of cleaner production, 278, 123368. DOI:10.1016/j.jclepro.2020.123368
[15]   Bolhasani, P., Fallah, M., Tavakkoli-Moghaddam, R., & Alam Tabriz, A. (2021). Presenting a multi-objective mathematical model of a location-routing-inventory problem for hazardous materials considering the concept elastic demand and queuing system. Journal of decisions and operations research, 6(2), 210-241. (In Persian). http://www.journal-dmor.ir/article_136500_495740a95cc285e0d82b225078bbe378.pdf?lang=en
[16]   Gholizadeh, H., Tajdin, A., & Javadian, N. (2020). A closed-loop supply chain robust optimization for disposable appliances. Neural computing and applications, 32(8), 3967–3985. DOI:10.1007/s00521-018-3847-9
[17]   Kiani, S., & Samouei, P. (2020). Multi-objective dynamic recycling-routing-inventory for different pharmaceutical items with considering discount in a closed-loop supply chain. Journal of decisions and operations research, 5(3), 290-311. (In Persian). https://www.journal-dmor.ir/article_120337.html?lang=en
[18]   Gholizadeh, H., & Fazlollahtabar, H. (2020). Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: Case study in melting industry. Computers and industrial engineering, 147, 106653. DOI:10.1016/j.cie.2020.106653
[19]   Paydar, M. M., Babaveisi, V., & Safaei, A. S. (2017). An engine oil closed-loop supply chain design considering collection risk. Computers and chemical engineering, 104, 38–55. DOI:10.1016/j.compchemeng.2017.04.005
[20]   Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European journal of operational research, 263(1), 108–141. DOI:10.1016/j.ejor.2017.04.009
[21]   Goudenege, G., Chu, C., & Jemai, Z. (2013). Reusable containers management: From a generic model to an industrial case study. Supply chain forum: an international journal, 14(2), 26-38. DOI: 10.1080/16258312.2013.11517313
[22]   Gholizadeh, H., Jahani, H., Abareshi, A., & Goh, M. (2021). Sustainable closed-loop supply chain for dairy industry with robust and heuristic optimization. Computers and industrial engineering, 157, 107324. DOI:10.1016/j.cie.2021.107324
[23]   Polo, A., Peña, N., Muñoz, D., Cañón, A., & Escobar, J. W. (2019). Robust design of a closed-loop supply chain under uncertainty conditions integrating financial criteria. Omega (United Kingdom), 88, 110–132. DOI:10.1016/j.omega.2018.09.003
[24]   Govindan, K., & Soleimani, H. (2017). A review of reverse logistics and closed-loop supply chains. Journal of cleaner production, 142, 371–384. DOI:10.1016/j.jclepro.2016.03.126
[25]   Zhen, L., Huang, L., & Wang, W. (2019). Green and sustainable closed-loop supply chain network design under uncertainty. Journal of cleaner production, 227, 1195–1209. DOI:10.1016/j.jclepro.2019.04.098
[26]   Morganti, E., & Gonzalez-Feliu, J. (2015). City logistics for perishable products. The case of the Parma’s Food Hub. Case studies on transport policy, 3(2), 120–128. DOI:10.1016/j.cstp.2014.08.003
[27]   Sel, Ç., & Bilgen, B. (2015). Quantitative models for supply chain management within dairy industry: A review and discussion. European journal of industrial engineering, 9(5), 561–594. DOI:10.1504/EJIE.2015.071772
[28]   Yavari, M., & Geraeli, M. (2019). Heuristic method for robust optimization model for green closed-loop supply chain network design of perishable goods. Journal of cleaner production, 226, 282–305. DOI:10.1016/j.jclepro.2019.03.279
[29]   Mohtashami, Z., Aghsami, A., & Jolai, F. (2020). A green closed loop supply chain design using queuing system for reducing environmental impact and energy consumption. Journal of cleaner production, 242, 118452. DOI:10.1016/j.jclepro.2019.118452
[30]   Pavlo, S., Fabio, C., Hakim, B., & Mauricio, C. (2018, June). 3D-Printing based distributed plastic recycling: A conceptual model for closed-loop supply chain design. 2018 IEEE international conference on engineering, technology and innovation (ICE/ITMC) (pp. 1-8). IEEE. DOI: 10.1109/ICE.2018.8436296
[31]   Liao, H., & Li, L. (2021). Environmental sustainability EOQ model for closed-loop supply chain under market uncertainty: A case study of printer remanufacturing. Computers and industrial engineering, 151, 106525. DOI:10.1016/j.cie.2020.106525
[32]   Saedinia, R., Vahdani, B., Etebari, F., & Afshar Nadjafi, B. (2019). Robust gasoline closed loop supply chain design with redistricting, service sharing and intra-district service transfer. Transportation research part E: logistics and transportation review, 123, 121–141. DOI:10.1016/j.tre.2019.01.015
[33]   Yavari, M., & Zaker, H. (2020). Designing a resilient-green closed loop supply chain network for perishable products by considering disruption in both supply chain and power networks. Computers and chemical engineering, 134, 106680. DOI:10.1016/j.compchemeng.2019.106680
[34]   Salehi-Amiri, A., Zahedi, A., Akbapour, N., & Hajiaghaei-Keshteli, M. (2021). Designing a sustainable closed-loop supply chain network for walnut industry. Renewable and sustainable energy reviews, 141, 110821. DOI:10.1016/j.rser.2021.110821
[35]   Yun, Y., Chuluunsukh, A., & Gen, M. (2020). Sustainable closed-loop supply chain design problem: A hybrid genetic algorithm approach. Mathematics, 8(1), 84. DOI:10.3390/math8010084
[36]   Ahmed, M. M., Salauddin Iqbal, S. M., Priyanka, T. J., Arani, M., Momenitabar, M., & Billal, M. M. (2021). An environmentally sustainable closed-loop supply chain network design under uncertainty: application of optimization. Progress in intelligent decision science: proceeding of IDS 2020 (pp. 343-358). Springer International Publishing. DOI: 10.1007/978-3-030-66501-2_28
[37]   Mohammed, F., Selim, S. Z., Hassan, A., & Syed, M. N. (2017). Multi-period planning of closed-loop supply chain with carbon policies under uncertainty. Transportation research part D: transport and environment, 51, 146–172. DOI:10.1016/j.trd.2016.10.033
[38]   Fu, R., Qiang, Q. P., Ke, K., & Huang, Z. (2021). Closed-loop supply chain network with interaction of forward and reverse logistics. Sustainable production and consumption, 27, 737–752.
[39]   Wan, N., & Hong, D. (2019). The impacts of subsidy policies and transfer pricing policies on the closed-loop supply chain with dual collection channels. Journal of cleaner production, 224, 881–891. DOI:10.1016/j.jclepro.2019.03.274
[40]   Nayeri, S., Paydar, M. M., Asadi-Gangraj, E., & Emami, S. (2020). Multi-objective fuzzy robust optimization approach to sustainable closed-loop supply chain network design. Computers and industrial engineering, 148, 106716. DOI:10.1016/j.cie.2020.106716
[41]   Talaei, M., Farhang Moghaddam, B., Pishvaee, M. S., Bozorgi-Amiri, A., & Gholamnejad, S. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: A numerical illustration in electronics industry. Journal of cleaner production, 113, 662–673. DOI:10.1016/j.jclepro.2015.10.074
[42]   Sazvar, Z., Zokaee, M., Tavakkoli-Moghaddam, R., Salari, S. A. sadat, & Nayeri, S. (2022). Designing a sustainable closed-loop pharmaceutical supply chain in a competitive market considering demand uncertainty, manufacturer’s brand and waste management. Annals of operations research, 315(2), 2057–2088. DOI:10.1007/s10479-021-03961-0
[43]   Soleimani, H., Govindan, K., Saghafi, H., & Jafari, H. (2017). Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Computers and industrial engineering, 109, 191–203. DOI:10.1016/j.cie.2017.04.038
[44]   Sahebjamnia, N., Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2018). Sustainable tire closed-loop supply chain network design: Hybrid metaheuristic algorithms for large-scale networks. Journal of cleaner production, 196, 273–296. DOI:10.1016/j.jclepro.2018.05.245
[45]   Subulan, K., Taşan, A. S., & Baykasoğlu, A. (2015). Designing an environmentally conscious tire closed-loop supply chain network with multiple recovery options using interactive fuzzy goal programming. Applied mathematical modelling, 39(9), 2661–2702. DOI:10.1016/j.apm.2014.11.004
[46]   Amin, S. H., Zhang, G., & Akhtar, P. (2017). Effects of uncertainty on a tire closed-loop supply chain network. Expert systems with applications, 73, 82–91. DOI:10.1016/j.eswa.2016.12.024
[47]   Liu, Z., Li, K. W., Li, B. Y., Huang, J., & Tang, J. (2019). Impact of product-design strategies on the operations of a closed-loop supply chain. Transportation research part E: logistics and transportation review, 124, 75–91. DOI:10.1016/j.tre.2019.02.007
[48]   Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., & Mirjalili, S. (2018). Hybrid optimizers to solve a tri-level programming model for a tire closed-loop supply chain network design problem. Applied soft computing journal, 70, 701–722. DOI:10.1016/j.asoc.2018.06.021
[49]   Cheraghalipour, A., Paydar, M. M., & Hajiaghaei-Keshteli, M. (2018). A bi-objective optimization for citrus closed-loop supply chain using Pareto-based algorithms. Applied soft computing journal, 69, 33–59. DOI:10.1016/j.asoc.2018.04.022
[50]   Farrokh, M., Azar, A., Jandaghi, G., & Ahmadi, E. (2018). A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty. Fuzzy sets and systems, 341, 69–91.
[51]   Pedram, A., Pedram, P., Yusoff, N. Bin, & Sorooshian, S. (2017). Development of closed--loop supply chain network in terms of corporate social responsibility. PloS one, 12(4), e0174951. https://doi.org/10.1371/journal.pone.0174951
[52]   Govindan, K., Mina, H., Esmaeili, A., & Gholami-Zanjani, S. M. (2020). An integrated hybrid approach for circular supplier selection and closed loop supply chain network design under uncertainty. Journal of cleaner production, 242, 118317. DOI:10.1016/j.jclepro.2019.118317
[53]   Zahedi, A., Salehi-Amiri, A., Hajiaghaei-Keshteli, M., & Diabat, A. (2021). Designing a closed-loop supply chain network considering multi-task sales agencies and multi-mode transportation. Soft computing, 25, 6203–6235.
[54]   Pourmehdi, M., Paydar, M. M., & Asadi-Gangraj, E. (2020). Scenario-based design of a steel sustainable closed-loop supply chain network considering production technology. Journal of cleaner production, 277, 123298. DOI:10.1016/j.jclepro.2020.123298
[55]   Chang, C. Ter. (2011). Multi-choice goal programming with utility functions. European journal of operational research, 215(2), 439–445. DOI:10.1016/j.ejor.2011.06.041