انتخاب تأمین‌کننده پایدار محصولات پالایشی تحت ریسک و قرارداد اختیار معامله با استفاده از ارزش در معرض ریسک شرطی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، دانشگاه صنعتی شاهرود.

2 استادیار، دانشگاه صنعتی شاهرود.

3 دانشیار، دانشگاه صنعتی شاهرود.

چکیده

هدف انتخاب تأمین‌کنندگان و تخصیص سفارش بر اساس ابعاد پایداری و ریسک پایداری، در شرایط عدم‌­قطعیت برخی پارامترها است؛ همچنین با محدودیت ریسک و استراتژی کاهش برای مدیریت ریسک همراه است. با انتخاب معیارهای پایداری و ریسک، امتیاز تأمین‌کنندگان توسط روش‌های تاپسیس فازی و تجزیه‌وتحلیل آثار شکست محاسبه شده است و سپس توسط برنامه تصادفی چندمرحله‌ای و ایجاد محدودیت ریسک توسط معیار ارزش در معرض ریسک شرطی، پیرامون استراتژی تأمین منابع و تخصیص سفارش در یک فضای چند­دوره‌ای برنامه‌‎ریزی شده است. برای دستیابی به یک برنامه‌ریزی انعطاف‌پذیر از قرارداد اختیار معامله به‌عنوان استراتژی کاهش استفاده شده است. برای مدل‌سازی عدم‌قطعیت پارامترهای تصادفی، مجموعه سناریوها توسط رویکرد شبیه‌سازی تولید و سپس یک روش کاهش سناریو برای ساخت درخت سناریوی مناسب استفاده ‌شده است. مدل پیشنهادی در یک مطالعه موردی شامل چندین پالایشگاه به‌عنوان تأمین‌کننده و «شرکت پالایش نفت جی» با چندین سایت تولید و ذخیره پیاده‌سازی شده است. درنهایت، عملکرد سیاست‌های اندازه‌گیری ریسک و اهمیت استراتژی کاهش برای ارائه برخی بینش‌های مدیریتی و کاربردی بررسی و جدول تخصیص بهینه ارائه شده است. نتایج نهایی حاکی از کاهش هزینه‌های خرید و کاهش ریسک پایداری است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Sustainable Supplier Selection of Refined Products under Risk and Options Contract using Conditional Value at Risk

نویسندگان [English]

  • Ahmad Reza Karami 1
  • Mohammad Fattahi 2
  • Aliakbar Hasani 3
1 Master's Student, Shahrood University of Technology.
2 Assistant Professor, Shahrood University of Technology.
3 Associate Professor, Shahrood University of Technology.
چکیده [English]

Considering the importance of selecting suppliers based on the dimensions of sustainability in the supply chain, after identifying and selecting sustainability and risk criteria in accordance with Jey Oil Refining Company, by developing a multi-stage stochastic program and creating a risk constraint by the CVaR risk value criterion for quantitative criteria. Also, in terms of points calculated by FTOPSIS and FMEA methods for quality criteria, the optimal selection of suppliers, sourcing strategy and order allocation in a multi-period supply chain planning under operational risk and disruption were discussed. In order to reduce supply risk and achieve a flexible planning as a mitigation strategy, the option contract and the trading market were considered as two options to supply raw materials. The product demand, the market price of the materials, the purchase price and the apply price of the option contract, the supply quantity and the supply quantity of the option contract are random. To model uncertainty, discrete scenarios are generated through a simulation approach, and then, a scenario reduction method is used to construct a scenario tree. The application of the stochastic model, the performance of risk measurement policies, and the importance of mitigation strategies to provide some managerial insights have been investigated.

کلیدواژه‌ها [English]

  • Sustainable Supply Chain
  • Risk Management
  • FMEA
  • CVaR
  • Multi-Stage Stochastic Programming
  • Option Contract

مراجع

  1. Ahmadi, M., Molana, S. M. H., & Sajadi, S. M. (2017). A hybrid FMEA-TOPSIS method for risk management, case study: Esfahan Mobarakeh Steel Company. International Journal of Process Management and Benchmarking, 7(3), 397-408.
  2. Alikhani, R., Torabi, S.A., & Altay, N. (2019). Strategic supplier selection under sustain-ability and risk criteria. International Journal of Production Economics, 208, 69-82.
  3. Azar, Adel; Gholamzadeh, Rasool; Qanavati, Mehdi (2012). "Structural-structural modeling in management-application of SmartPLS software" Negah Danesh Publications, first edition.
  4. Cheng, L., Wan, Z., & Wang, G. (2009). Bilevel newsvendor models considering retailer with CVaR objective. Computers & Industrial Engineering, 57(1), 310-318.
  5. Dupačová, J., Gröwe-Kuska, N., & Römisch, W. (2003). Scenario reduction in stochastic programming. Mathematical programming, 95(3), 493-511.
  6. Dupačová, J. (1995). Multistage stochastic programs: The state-of-the-art and selected bibliography. Kybernetika, 31(2), 151-174.
  7. Dweiri, F., Kumar, S., Khan, S. A., & Jain, V. (2016). Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications, 62, 273-283.
  8. Eydi, A., & Bakhtiari, M. (2016). Evaluating and Selecting Two-Layers of Suppliers in Green Supply Chain using Hierarchical Fuzzy Topsis based on Alpha Levels. The Journal of Industrial Management Perspective, 6(2), 91-121. (In Persian)
  9. Fallahi, A., Azimi-Dastgerdi, M., & Mokhtari, H. (2021). A sustainable production-inventory model joint with preventive maintenance and multiple shipments for imperfect quality items. Scientia Iranica.
  10. Fallahi, A., Mokhtari, H., & Niaki, S. T. A. (2021). Designing a closed-loop blood supply chain network considering transportation flow and quality aspects. Sustainable Operations and Computers, 2, 170-189.
  11. Fattahi, M. (2017). Resilient procurement planning for supply chains: A case study for sourcing a critical mineral material. Resources Policy,
  12. Feng-ge, Y. A. O., & Ping, Z. H. A. N. G. (2012). The measurement of operational risk based on CVaR: a decision engineering technique. Systems Engineering Procedia, 4, 438-447.
  13. Ghadge, A., Dani, S., Ojha, R., & Caldwell, N. (2017). Using risk sharing contracts for supply chain risk mitigation: A buyer-supplier power and dependence perspective. Computers & Industrial Engineering, 103, 262-270.
  14. Ghahremani Nahr, J., Pasandideh, S. H. R., & Niaki, S. T. A. (2020). A robust optimization approach for multi-objective, multi-product, multi-period, closed-loop green supply chain network designs under uncertainty and discount. Journal of industrial and production engineering, 37(1), 1-22.
  15. Giannakis, M., & Papadopoulos, T. (2016). Supply chain sustainability: A risk management approach. International Journal of Production Economics, 171, 455-470
  16. Gogus, O., & Boucher, T. O. (1998). Strong transitivity, rationality and weak monotonicity in fuzzy pairwise comparisons. Fuzzy Sets and Systems,94(1),133-144
  17. Hassani Goodarzi, Asefeh and Seyed Hesamuddin Zogardi, 2015, Modeling Uncertainty in the Car Supply Chain Using Value at Risk, National Conference on Organizational Risk Management, Tehran, Narkish Information Institute. (In Persian)
  18. Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk–Definition, measure and modeling. Omega, 52, 119-132.
  19. Heitsch, H., & Romisch, W. (2005, June). Generation of multivariate scenario trees to model stochasticity in power management. In 2005 IEEE Russia Power Tech (pp. 1-7). IEEE.
  20. Heitsch, H., & Römisch, W. (2009). Scenario tree reduction for multistage stochastic programs. Computational Management Science, 6(2), 117-133.
  21. Hudson, A. (2017). The law on financial derivatives (No. 6).Sweet and Maxwell Ltd
  22. Inderfurth, K., & Kelle, P. (2011). Capacity reservation under spot market price uncertainty. International Journal of Production Economics, 133(1), 272-279.
  23. Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers & industrial engineering, 116, 178-191.
  24. Jain, N., & Singh, A. R. (2020). Sustainable supplier selection under must-be criteria through Fuzzy inference system. Journal of Cleaner Production, 248,
  25. Jokar, Mustafa, Mozaffari, Marzieh, Akbari, Ali. (2020). A Weighted Robust Two-Stage Stochastic Optimization Model for Supplier Selection and Order Allocation under Uncertainty. The Journal of Industrial Management Perspective, 38, 111-135. (In Persian)
  26. Karsak, E. E., & Dursun, M. (2016). Taxonomy and review of non-deterministic analytical methods for supplier selection. International Journal of Computer Integrated Manufacturing, 29(3), 263-286.
  27. Keyvanloo, M., Kimiagari, A. M., & Esfahanipour, A. (2014). Risk analysis of sourcing problem using stochastic programming. Scientia Iranica, 21(3), 1034-1043.
  28. Keyvanloo, M., Kimiagari, A. M., & Esfahanipour, A. (2015). A hybrid approach to select the best sourcing policy using stochastic programming. Journal of Manufacturing Systems, 36, 115-127.
  29. Khan, S. A., Kusi-Sarpong, S., Arhin, F. K., & Kusi-Sarpong, H. (2018). Supplier sustainability performance evaluation and selection: A framework and methodology. Journal of Cleaner Production, 205, 964-979.
  30. Li, S., & Zeng, W. (2016). Risk analysis for the supplier selection problem using failure modes and effects analysis (FMEA). Journal of Intelligent Manufacturing, 27(6), 1309-1321.
  31. Pongsakdi, A., Rangsunvigit, P., Siemanond, K., & Bagajewicz, M. J. (2006). Financial risk management in the planning of refinery operations. International Journal of Production Economics, 103(1), 64-86.
  32. Rabieh, M., Fadaei Rafsanjani, A., Babaei, L., & Esmaeili, M. (2019). Sustainable supplier selection and order allocation: An integrated delphi method, fuzzy TOPSIS and multi-objective programming model. Scientia Iranica, 26(4), 2524-2540.
  33. Reyhani Yamchi, H., Jabarzadeh, Y., Ghaffarinasab, N., Kumar, V., & Garza-Reyes, J. A. (2020). A multi-objective linear optimization model for designing sustainable closed-loop agricultural supply chain.
  34. Saberi Rabar, Maryam and Forghani, Mohammad Ali and Kazemi, Mehdi, 2014, Evaluation and selection of suppliers in the supply chain using a combined model of fuzzy hierarchical analysis and fuzzy TOPSIS, International Management Conference, Tehran, https://civilica.com / doc / 343584. (In Persian)
  35. Sajedi, S., Sarfaraz, A. H., Bamdad, Sh., Khalili Damghani, K. (2021). Mathematical Model of Location, Multi-Commodity and Multi-Period in Sustainable Closed-Loop Supply Chain Considering Risk and Demand and Quality Uncertainty (A case Study). The Journal of Industrial Management Perspective, 42, 271-304. (In Persian)
  36. Salimi, F., & Vahdani, B. (2018). Designing a bio-fuel network considering links reliability and risk-pooling effect in bio-refineries. Reliability Engineering & System Safety, 174, 96-107.
  37. Sawik, T. (2011). Selection of supply portfolio under disruption risks. Omega, 39(2), 194-208.
  38. Sawik, T. (2013). Selection of resilient supply portfolio under disruption risks. Omega, 41(2), 259-269.
  39. Sodhi, M. S. (2005). Managing demand risk in tactical supply chain planning for a global consumer electronics company. Production and Operations management, 14(1), 69-79.
  40. Taleizadeh, A. A., Niaki, S. T. A., & Alizadeh-Basban, N. (2021). Cost-sharing contract in a closed-loop supply chain considering carbon abatement, quality improvement effort, and pricing strategy. RAIRO-Operations Research, 55, S2181-S2219.
  41. Torabi, S. A., Baghersad, M., & 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.
  42. Van Delft, C., & Vial, J. P. (2004). A practical implementation of stochastic programming: an application to the evaluation of option contracts in supply chains. Automatica, 40(5), 743-756.
  43. Verma, R., & Pullman, M. E. (1998). An analysis of the supplier selection process. Omega, 26(6), 739-750.
  44. Vilko, J., Ritala, P., & Hallikas, J. (2019). Risk management abilities in multimodal maritime supply chains: Visibility and control perspectives. Accident Analysis & Prevention, 123, 469-481.
  45. Xu, N., & Nozick, L. (2009). Modeling supplier selection and the use of option contracts for global supply chain design. Computers & Operations Research, 36(10), 2786-2800.
  46. Yaghoobi, M. A., & Tamiz, M. (2007). A method for solving fuzzy goal programming problems based on MINMAX approach. European journal of operational research, 177(3), 1580-1590.
  47. Yun, Y., Chuluunsukh, A., & Gen, M. (2020). Sustainable closed-loop supply chain design problem: A hybrid genetic algorithm approach. Mathematics, 8(1),

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