طراحی یک زنجیره تأمین پایدار با در نظر گرفتن عدم‌قطعیت در ریسک مربوط به تأمین‌کنندگان

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد دانشگاه علم و صنعت ایران، تهران، ایران

2 استادیار دانشکده مهندسی صنایع، دانشگاه علم و صنعت

3 استاد/دانشگاه علم و صنعت ایران

10.22084/ier.2019.14188.1644

چکیده

مدیریت ریسک، مسئله‌ای مهم در مدیریت زنجیره تأمین و تدارکات است. بهبود قابلیت پاسخگویی در قالب کاهش (احتمال) رخداد ریسک، شرکت را قادر می‌سازد تا ضمن سبقت از رقبای خود، آسیب طولانی‌مدت مورد انتظار را کاهش دهد. در این مقاله، یک مدل برنامه‌ریزی خطی مختلط به‌منظور طراحی زنجیره تأمین سبز ارائه می­گردد. این مدل، به‌دنبال کمینه‌سازی هزینه، میزان انتشار گازهای گلخانه‌ای و ریسک است. برای اولین­بار، ریسک تأمین مواد خام و ریسک حمل‌ونقل در سطوح مختلف زنجیره تأمین با عدم‌قطعیت روبه‌رو هستند و هزینه مواد خام براساس مدل تخفیف نموی از سوی تأمین‌کنندگان به تولیدکنندگان ارائه می­گردد. پارامترهای مورداستفاده در مدل‌سازی اولیه دارای عدم‌قطعیت است و با استفاده از رویکرد استوار سازی به حالت قطعی تبدیل می­شود و حل آن با نرم‌افزار گمز، صورت می­پذیرد. علاه­براین، در مورد پارامتر عدم قطعیت، تحلیل حساسیت انجام می­شود و تحت مقادیر مختلف از این پارامتر، نتیجه به‌دست‌آمده مورد ارزیابی و اعتبارسنجی قرار می­گیرد. نتایج نشان می­دهند که مهم‌ترین عامل تأثیرگذار در مقدار تابع هدف، تابع ریسک است، زیرا پارامترهای آن با عدم‌قطعیت روبه‌رو هستند.

کلیدواژه‌ها


عنوان مقاله [English]

Sustainable Supply Chain Design with Considering Uncertainty in Suppliers’ Risk

نویسندگان [English]

  • Mojtaba Nouri 1
  • Emran Mohammadi 2
  • Mohammad Jabalameli 3
1 Master's degree student, Faculty of Industrial Engineering, Iran University of Science and Technology
2 Assistant Professor, Faculty of Industrial Engineering, Iran University of Science and Technology
3 professor/Iran University of Science and Technology
چکیده [English]

Risk management is a significant issue in supply chain management. Improving the ability to control and manage the risk, enables the companies to be more successful in competing with other companies and decrease the expected long-term loss. In this manuscript, a mixed integer linear programming model for designing the green supply chain is presented. This model aims to minimize the cost, greenhouse gas emissions, and risk. Risk of supplying the raw materials and transportation in all levels of supply chain are under uncertainty. Furthermore, cost of raw materials is suggested by suppliers to producers with an incremental discount. The initial modelling is turned into a deterministic one using Bertsimas and Sim budget of uncertainty approach and consequently solved by GAMS software to manage risk. Furthermore, the uncertain parameter is analyzed and using various amounts the obtained result has been assessed and evaluated. The results show that the risk function is the most important factor in objective function, because parameters of risk function are subject to uncertainty.

کلیدواژه‌ها [English]

  • Green supply chain
  • Risk
  • Uncertainty
  • Robust optimization
  • Discount
]1[ قهرمانی­نهر، جاوید، قدرت­نما، ایزدبخش، حمیدرضا، توکلی­مقدم، رضا (1397). «طراحی یک شبکه زنجیره تأمین سبز چندهدفه چند محصولی و چند دوره ای با در نظر گرفتن تخفیف در شرایط عدم قطعیت»، نشریه پژوهش­های مهندسی صنایع در سیستم­های تولید، 5(11): 193-209.

]2[ کریمی، بهروز و جنابی، مسعود (1392). «برنامه­ریزی و کنترل تولید و موجودی­ها- جلد اول: سیستم­های با تقاضای مستقل»، انتشارات جهاد دانشگاهی(دانشگاه اصنعتی امیرکبیر)، چاپ سوم، 180-182.

]3[ نیکوفکر، محمد هادی و عبداله زاده، وحید (1393). «برنامه­ریزی و کنترل تولید و موجودی­ها»، انتشارات نگاه دانش، چاپ دوم، ص 347-350.

 [4] 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.

 [5] Aqlan, F., & Lam, S. S. (2015). Supply chain optimization under risk and uncertainty: A case study for high-end server manufacturing. Computers & Industrial Engineering, doi: http://dx.doi.org/10.1016/j.cie.2015.12.025.

[6] Handfield, R.B., Walton, S.V., Swwgers, L.K., Melnyk, S.A. (1997). “Green” value chain practices in the future industry. Journal of Operations Management 1997, 15 (4), 293-315.

 [7] Stivastava, S.K. (2007) Green supply-chain management: a state-of-the art literature review. International Journal of Management Reviews 2007, 9 (1), 53-80.

[8] Farahani, R.Z., Rezapour, S., Drezner, T., Fallah, S. (2014). Competitive supply chain network design: An overview of classification, models, solution techniques and applications. Omega 2014, 45, 92-118.

[9] Shen, Z.J. (2007). Integrated supply chain models: a survey and future research directions. Journal of Industrial Management and Optimization, 3(1), 1-27.

[10] Wang, F., Lai, X.F., Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision Support System, 51, 262-269.

[11] Mansini, R., Savelsbergh, M. W., & Tocchella, B. (2012). The supplier selection problem with quantity discounts and truckload shipping. Omega, 40(4), 445-455.

[12] Lee, A. H., Kang, H. Y., Lai, C. M., & Hong, W. Y. (2013). An integrated model for lot sizing with supplier selection and quantity discounts. Applied Mathematical Modelling, 37(7), 4733-4746.

[13] Meena, P.L. and 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, 50, 84-97.

[14] Hammami, R., C. Temponi, and Y. Frein, A. (2014).scenario-based stochastic model for supplier selection in global context with multiple buyers, currency fluctuation uncertainties, and price discounts. European Journal of Operational Research, 233(1): 159-170.

[15] Ayhan, M.B. and Kilic, H.S. (2015). A two stage approach for supplier selection problem in multi-item/multi-supplier environment with quantity discounts. Computers & Industrial Engineering, 85: 1-12.

[16] Chai, J. and Ngai, E.W.T. (2015). Multi-perspective strategic supplier selection in uncertain environments. International Journal of Production Economics, 166: 215-225.

[17] Moghaddam, K.S., (2015). Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty. Expert Systems with Applications, 42(15–16): 6237-6254.

[18] Torabi, S.A., Baghersad, M. and Mansouri, S.A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 79: 22-48.

[19] Jain, V., et al., (2015). A Chaotic Bee Colony approach for supplier selection-order allocation with different discounting policies in a coopetitive multi-echelon supply chain. Journal of Intelligent Manufacturing, 26(6): 1131-1144.

[20] Çebi, F. and Otay, İ. (2016). A two-stage fuzzy approach for supplier evaluation and order allocation problem with quantity discounts and lead time. Information Sciences, 339: 143-157.

[21] Rezaei, J., Nispeling, T., Sarkis, J., & Tavasszy, L. (2016). A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production, 135, 577-588.

[22] Mohammaditabar, D., Ghodsypour, S. H., & Hafezalkotob, A. (2016). A game theoretic analysis in capacity-constrained supplier-selection and cooperation by considering the total supply chain inventory costs. International Journal of Production Economics, 181, 87-97.

[23] Meena, P.L. and Sarmah, S.P. (2016). Supplier selection and demand allocation under supply disruption risks. The International Journal of Advanced Manufacturing Technology, 83(1): 265-274.

[24]         Amin, SH, Baki, F. (2017). A facility location model for global closed-loop supply chain network design. Applied Mathematical Modelling; 41: 316-30.

[25] Arabsheybani, A., Paydar, M. M., & Safaei, A. S. (2018). An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier's risk. Journal of cleaner production, 190, 577-591.

[26] Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations research, 52(1), 35-53.

[27]       Sheu, J.-B., Lin, A.Y.-S. (2012). Hierarchical facility network planning model for global logistics network configuration. Applied Mathematical Modelling, 36, 3066-3053.

[28] Yu, H., Solvang, W.D., Yuan, S. (2012). A multi-objective decision support system for simulation and optimization of municipal solid waste management system. Proceeding of the 3rd IEEE International Conference on Cognitive Info communications. Kosice, Slovakia, 199-193.

[29] Nema, A.K., Gupta, S.K. (1999). Optimization of regional hazardous waste management systems: an improved formulation. Waste Management 1999, 19, 451-441.

[30] Sheu, J.-B. (2007). A coordinated reverse logistics system for regional management of multi-source hazardous wastes. Computers & Operations Research, 34, 1462-1442.

[31] Li, Zukui, Qiuhua Tang, and Christodoulos A. (2012). Floudas. "A comparative theoretical and computational study on robust counterpart optimization: II. Probabilistic guarantees on constraint satisfaction." Industrial & engineering chemistry research 51.19 (2012): 6769-6788.

[32] D. J., Morabito, R. (2012). Production planning in furniture settings via robust optimization." Computers & Operations Research, Vol. 39, 139-150.

[33] Soyster, A. (1973). Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research, Vol. 21, 1154–7.

[34] Ben-Tal, A. and Nemirovski, A. (1998). Robust convex optimization." Mathematics of Operations Research, Vol. 23, 769-805.

[35] Yu, H, Solvang, WD, Chen, C. (2014). A green supply chain network design model for enhancing competitiveness and sustainability of companies in high north arctic regions. International Journal of Energy and Environment; 5(4): 403-18.