طراحی شبکه‌ی زنجیره‌ی تأمین پایا برای محصول ماژولار در شرایط عدم قطعیت مطالعه‌ی موردی: پمپ‌های کرایوژنیک صادرات LPG

نوع مقاله : پژوهشی

نویسندگان

1 کارشناسی ارشد مهندسی صنایع، دانشگاه پیام نور، تهران، ایران

2 استادیار گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه پیام نور، تهران، ایران

چکیده

امروزه شرکت‌ها با توجه به ویژگی‌های اقتصادی تجارت مدرن در مقیاس جهانی و عملیات‌های پیچیده زنجیره تامین سعی دارند به تقاضای مشتریان پاسخ دهند. عدم قطعیت تقاضای جهانی چالش بزرگی است که با وقوع اختلالات تشدید می شود. ‌ در این مطالعه، یک مدل برنامه ریزی ریاضی غیرخطی تصادفی فازی چند محصولی برای طراحی شبکه زنجیره تامین پایا با در نظر گرفتن محصول با تکنولوژی تولید ماژولار تحت ریسک اختلال پیشنهاد شده است. همچنین، یک سیستم پشتیبانی تصمیم‌گیری برای تولید محصول ماژولار طراحی شده. که نقشی عمده در افزایش قابلیت استفاده مجدد محصولات و کاهش ضایعات ایفا می کند. مطالعه موردی بر روی تولید پمپ‌های کرایوژنیک صادرات LPG تمرکز دارد که از تجهیزات حیاتی در صادرات پروپان و بوتان مایع است. ساختار این شبکه زنجیره تامین شامل سطوح تامین‌کنندگان ماژول‌ها، مراکز تولید اولیه، مراکز بازرسی، مراکز تعمیر، مراکز بازتولید و مشتریان است. برای مواجه با اختلالات یک رویکرد تصادفی سناریو محور مورد استفاده قرار گرفته و عدم قطعیت پارامتری با رویکرد ترکیبی برنامه‌ریزی امکانی- استوار مدیریت شده است.  ارزیابی مدل با رویکرد دقیق در نرم افزار GAMS با حل کننده CPLEX و تجزیه و تحلیل حساسیت بر پارامترهای غیر قطعی انجام شده است. نتایج تحقیق نشان می دهد که رویکرد حاضر ضمن کنترل عدم قطعیت، جریان بهینه تسهیلات را تضمین می کند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Design of a Reliable Supply Chain Network for Modular Products in Conditions of Uncertainty: A Case Study of LPG Export Cryogenic Pumps

نویسندگان [English]

  • Alireza vahabi 1
  • Alireza Hamidieh 2
1 Master student of Payame Noor University of North Tehran
2 univ.Payam Noor
چکیده [English]

Nowadays, companies are trying to meet customer demand due to the economic characteristics of modern global business and complex supply chain operations. Global demand uncertainty is a major challenge that is exacerbated by the occurrence of disruptions. In this study, a multi-product fuzzy stochastic nonlinear mathematical programming model is proposed to design a reliable supply chain network considering a product with modular manufacturing technology at the risk of disruption. Also, a decision support system is designed for modular production, which plays a major role in increasing the reusability of products and reducing waste. The case study, focuses on the production of cryogenic pumps used for the export of LPG, which is a critical equipment in the export of liquid propane and butane. The structure of this supply chain network includes levels of module suppliers, primary production centers, inspection centers, repair centers, reproduction centers, and customers. To deal with the disturbances, a scenario-based stochastic approach has been used and parametric uncertainty has been managed with the possibilistic-robust hybrid programming approach. The model evaluation has been performed with a precise approach in GAMS software with CPLEX solver and the sensitivity analysis has addressed the uncertain parameters. The results show that the current approach while controlling the uncertainty, ensures the optimal flow of facilities.

کلیدواژه‌ها [English]

  • Reliable Supply Chain
  • Modular Products
  • Uncertainty
  • Possibilistic-Robust Stochastic Programming
[1]           K. Sarrafha, S. H. A. Rahmati, S. T. A. Niaki, and A. Zaretalab, "A bi-objective integrated procurement, production, and distribution problem of a multi-echelon supply chain network design: A new tuned MOEA," Computers & Operations Research, vol. 54, pp. 35-51, 2015.
[2]           P. N. K. Phuc, F. Y. Vincent, and S.-Y. Chou, "Optimizing the fuzzy closed-loop supply chain for electrical and electronic products," in 2012 International conference on Fuzzy Theory and Its Applications (iFUZZY2012), 2012, pp. 316-321: IEEE.
[3]           B. Vahdani, R. Tavakkoli-Moghaddam, M. Modarres, and A. Baboli, "Reliable design of a forward/reverse logistics network under uncertainty: a robust-M/M/c queuing model," Transportation Research Part E: Logistics and Transportation Review, vol. 48, no. 6, pp. 1152-1168, 2012.
[4]           H.-F. Wang and H.-W. Hsu, "A possibilistic approach to the modeling and resolution of uncertain closed-loop logistics," Fuzzy Optimization and Decision Making, vol. 11, no. 2, pp. 177-208, 2012.
[5]           Y. Zhang, A. Diabat, and Z.-H. Zhang, "Reliable closed-loop supply chain design problem under facility-type-dependent probabilistic disruptions," Transportation Research Part B: Methodological, vol. 146, pp. 180-209, 2021.
[6]           M. S. Pishvaee and S. A. Torabi, "A possibilistic programming approach for closed-loop supply chain network design under uncertainty," Fuzzy sets and systems, vol. 161, no. 20, pp. 2668-2683, 2010.
[7]           R. C. Allen, S. Avraamidou, and E. N. Pistikopoulos, "Production Scheduling of Supply Chains Comprised of Modular Production Units," IFAC-PapersOnLine, vol. 53, no. 2, pp. 11452-11457, 2020.
[8]           A. Hamidieh, B. Naderi, M. Mohammadi, and M. Fazli-Khalaf, "A robust possibilistic programming model for a responsive closed loop supply chain network design," Cogent Mathematics, vol. 4, no. 1, p. 1329886, 2017.
[9]           E. B. Tirkolaee, A. Mardani, Z. Dashtian, M. Soltani, and G.-W. Weber, "A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design," Journal of Cleaner Production, vol. 250, p. 119517, 2020.
[10]         R. Babazadeh, J. Razmi, and R. Ghodsi, "Supply chain network design problem for a new market opportunity in an agile manufacturing system," Journal of Industrial Engineering International, vol. 8, no. 1, pp. 1-8, 2012.
[11]         K. N. Rao, K. V. Subbaiah, and G. V. P. Singh, "Design of supply chain in fuzzy environment," Journal of Industrial Engineering International, vol. 9, no. 1, pp. 1-11, 2013.
[12]         Q. Cheng, S. Wang, and C. Yan, "Robust optimal design of chilled water systems in buildings with quantified uncertainty and reliability for minimized life-cycle cost," Energy and Buildings, vol. 126, pp. 159-169, 2016.
[13]         N. Gupta, S. Haseen, and A. Bari, "Reliability optimization problems with multiple constraints under fuzziness," Journal of Industrial Engineering International, vol. 12, no. 4, pp. 459-467, 2016.
[14]         M. Bevilacqua, F. Ciarapica, and G. Marcucci, "A modular analysis for the supply chain resilience triangle," IFAC-PapersOnLine, vol. 51, no. 11, pp. 1528-1535, 2018.
[15]         H. Akkermans and L. N. Van Wassenhove, "Supply chain tsunamis: research on low probability, high‐impact disruptions," Journal of Supply Chain Management, vol. 54, no. 1, pp. 64-76, 2018.
[16]         W. W. Lowrance, "Of acceptable risk: Science and the determination of safety," 1976.
[17]         N. Ni, B. J. Howell, and T. C. Sharkey, "Modeling the impact of unmet demand in supply chain resiliency planning," Omega, vol. 81, pp. 1-16, 2018.
[18]         P. Peng, L. V. Snyder, A. Lim, and Z. Liu, "Reliable logistics networks design with facility disruptions," Transportation Research Part B: Methodological, vol. 45, no. 8, pp. 1190-1211, 2011.
[19]         Z. Drezner, "Heuristic solution methods for two location problems with unreliable facilities," Journal of the Operational Research Society, vol. 38, no. 6, pp. 509-514, 1987.
[20]         L. V. Snyder and M. S. Daskin, "Reliability models for facility location: the expected failure cost case," Transportation Science, vol. 39, no. 3, pp. 400-416, 2005.
[21]         L. V. Snyder, M. P. Scaparra, M. S. Daskin, and R. L. Church, "Planning for disruptions in supply chain networks," in Models, methods, and applications for innovative decision making: INFORMS, 2006, pp. 234-257.
[22]         L. V. Snyder and M. S. Daskin, "Models for reliable supply chain network design," in Critical infrastructure: Springer, 2007, pp. 257-289.
[23]         C.-I. Hsu and H.-C. Li, "Reliability evaluation and adjustment of supply chain network design with demand fluctuations," International Journal of Production Economics, vol. 132, no. 1, pp. 131-145, 2011.
[24]         B. Vahdani, R. Tavakkoli-Moghaddam, F. Jolai, and A. Baboli, "Reliable design of a closed loop supply chain network under uncertainty: An interval fuzzy possibilistic chance-constrained model," Engineering Optimization, vol. 45, no. 6, pp. 745-765, 2013.
[25]         T. Cui, Y. Ouyang, and Z.-J. M. Shen, "Reliable facility location design under the risk of disruptions," Operations research, vol. 58, no. 4-part-1, pp. 998-1011, 2010.
[26]         Q. Li, B. Zeng, and A. Savachkin, "Reliable facility location design under disruptions," Computers & Operations Research, vol. 40, no. 4, pp. 901-909, 2013.
[27]         O. Berman, D. Krass, and M. B. Menezes, "Location and reliability problems on a line: Impact of objectives and correlated failures on optimal location patterns," Omega, vol. 41, no. 4, pp. 766-779, 2013.
[28]         J. Razmi, A. Zahedi-Anaraki, and M. Zakerinia, "A bi-objective stochastic optimization model for reliable warehouse network redesign," Mathematical and Computer Modelling, vol. 58, no. 11-12, pp. 1804-1813, 2013.
[29]         X. Wang and Y. Ouyang, "A continuum approximation approach to competitive facility location design under facility disruption risks," Transportation Research Part B: Methodological, vol. 50, pp. 90-103, 2013.
[30]         Y. An, B. Zeng, Y. Zhang, and L. Zhao, "Reliable p-median facility location problem: two-stage robust models and algorithms," Transportation Research Part B: Methodological, vol. 64, pp. 54-72, 2014.
[31]         L. V. Snyder and M. S. Daskin, "Stochastic p-robust location problems," Iie Transactions, vol. 38, no. 11, pp. 971-985, 2006.
[32]         Z.-J. M. Shen, R. L. Zhan, and J. Zhang, "The reliable facility location problem: Formulations, heuristics, and approximation algorithms," INFORMS Journal on Computing, vol. 23, no. 3, pp. 470-482, 2011.
[33]         L. Benyoucef, X. Xie, and G. A. Tanonkou, "Supply chain network design with unreliable suppliers: a Lagrangian relaxation-based approach," International Journal of Production Research, vol. 51, no. 21, pp. 6435-6454, 2013.
[34]         X. Feng, I. Moon, and K. Ryu, "Revenue-sharing contracts in an N-stage supply chain with reliability considerations," International Journal of Production Economics, vol. 147, pp. 20-29, 2014.
[35]         S. H. R. Pasandideh, S. T. A. Niaki, and K. Asadi, "Optimizing a bi-objective multi-product multi-period three echelon supply chain network with warehouse reliability," Expert Systems with Applications, vol. 42, no. 5, pp. 2615-2623, 2015.
[36]         G. Li, L. Zhang, X. Guan, and J. Zheng, "Impact of decision sequence on reliability enhancement with supply disruption risks," Transportation Research Part E: Logistics and Transportation Review, vol. 90, pp. 25-38, 2016.
[37]         D. Rahmani and V. Mahoodian, "Strategic and operational supply chain network design to reduce carbon emission considering reliability and robustness," Journal of Cleaner Production, vol. 149, pp. 607-620, 2017.
[38]         C. Ha, H.-B. Jun, and C. Ok, "A mathematical definition and basic structures for supply chain reliability: A procurement capability perspective," Computers & Industrial Engineering, vol. 120, pp. 334-345, 2018.
[39]         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," International Journal of Hydrogen Energy, vol. 45, no. 59, pp. 34503-34531, 2020.
[40]         Y. Kristianto and P. Helo, "Reprint of “Product architecture modularity implications for operations economy of green supply chains”," Transportation Research Part E: Logistics and Transportation Review, vol. 74, pp. 63-80, 2015.
[41]         F. Rossi, F. Manenti, and G. Reklaitis, "A general modular framework for the integrated optimal management of an industrial gases supply-chain and its production systems," Computers & Chemical Engineering, vol. 82, pp. 84-104, 2015.
[42]         J. O. Aguila, W. ElMaraghy, and H. ElMaraghy, "Impact of risk attitudes on the concurrent design of supply chains and product architectures," Procedia CIRP, vol. 81, pp. 974-979, 2019.
[43]         P.-Y. Hsu, M. Aurisicchio, and P. Angeloudis, "Risk-averse supply chain for modular construction projects," Automation in Construction, vol. 106, p. 102898, 2019.
[44]         P.-Y. Hsu, M. Aurisicchio, and P. Angeloudis, "Optimal logistics planning for modular construction using multi-stage stochastic programming," Transportation Research Procedia, vol. 46, pp. 245-252, 2020.
[45]         A. Bhosekar and M. Ierapetritou, "A framework for supply chain optimization for modular manufacturing with production feasibility analysis," Computers & Chemical Engineering, vol. 145, p. 107175, 2021.
[46]         S. D. Budiman and H. Rau, "A stochastic model for developing speculation-postponement strategies and modularization concepts in the global supply chain with demand uncertainty," Computers & Industrial Engineering, vol. 158, p. 107392, 2021.
[47]         M. Talaei, B. F. Moghaddam, M. S. Pishvaee, A. Bozorgi-Amiri, and S. Gholamnejad, "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, vol. 113, pp. 662-673, 2016.
[48]         H. Soleimani, K. Govindan, H. Saghafi, and H. Jafari, "Fuzzy multi-objective sustainable and green closed-loop supply chain network design," Computers & industrial engineering, vol. 109, pp. 191-203, 2017.
[49]         Y.-C. Tsao, V.-V. Thanh, J.-C. Lu, and V. Yu, "Designing sustainable supply chain networks under uncertain environments: Fuzzy multi-objective programming," Journal of Cleaner Production, vol. 174, pp. 1550-1565, 2018.
[50]         R. G. Yaghin, P. Sarlak, and A. Ghareaghaji, "Robust master planning of a socially responsible supply chain under fuzzy-stochastic uncertainty (A case study of clothing industry)," Engineering Applications of Artificial Intelligence, vol. 94, p. 103715, 2020.
[51]         P. R. Burgess and F. T. Sunmola, "Prioritising Requirements of Informational Short Food Supply Chain Platforms Using A Fuzzy Approach," Procedia Computer Science, vol. 180, pp. 852-861, 2021.
[52]         A. De and S. P. Singh, "Analysis of fuzzy applications in the agri-supply chain: A literature review," Journal of Cleaner Production, p. 124577, 2020.
[53]         C. Lima, S. Relvas, and A. Barbosa-Póvoa, "Designing and planning the downstream oil supply chain under uncertainty using a fuzzy programming approach," Computers & Chemical Engineering, vol. 151, p. 107373, 2021.
[54]         A. K. Nasr, M. Tavana, B. Alavi, and H. Mina, "A novel fuzzy multi-objective circular supplier selection and order allocation model for sustainable closed-loop supply chains," Journal of Cleaner Production, vol. 287, p. 124994, 2021.
[55]         S. Nayeri, S. A. Torabi, M. Tavakoli, and Z. Sazvar, "A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network," Journal of Cleaner Production, p. 127691, 2021.
[56]         J. Shu, C.-P. Teo, and Z.-J. M. Shen, "Stochastic transportation-inventory network design problem," Operations Research, vol. 53, no. 1, pp. 48-60, 2005.
[57]         M. Bundschuh, D. Klabjan, and D. L. Thurston, "Modeling robust and reliable supply chains," Optimization Online e-print, 2003.