مدل سازی و حل مسئله طراحی شبکه زنجیره تامین حلقه بسته پایدار برای محصولات پتروشیمی تحت شرایط عدم قطعیت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت تولید و عملیات، گروه مدیریت صنایع دانشکده مدیریت و حسابداری دانشگاه شهید بهشتی تهران، ایران

2 استاد گروه مدیریت صنایع،دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی تهران، ایران

3 استاد گروه مدیریت صنایع دانشگاه شهید بهشتی تهران، ایران

4 استادیار، دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران

چکیده

صنعت پتروشیمی از مهمترین صنایع در جهان است که مدیریت و تصمیم‌گیری بهینه در فعالیت‌های آن موجب کسب منافع اقتصادی فراوان و همچنین رونق و توسعه صنایع وابسته است. در این مقاله به مسئله مدیریت زنجیره تامین محصولات پتروشیمی پرداخته می‌شود. یک مدل بهینه‌سازی چندهدفه توسعه داده می‌شود که در آن با اخذ تصمیمات استراتژیک، سه هدف بلند مدت اقتصادی، اجتماعی و زیست محیطی در صنعت پتروشیمی تحقق می‌یابد. برای این منظور، ابتدا با استفاده از روش اپسیلون محدودیت تکامل یافته، هدف اقتصادی به عنوان تابع هدف و اهداف اجتماعی و زیست محیطی به عنوان اپسیلون قیود در نظر گرفته می‌شوند. سپس جبهه پارتویی از جواب‌های کارا بدست آورده می شود و در این جبهه، جوابی که دارای کمترین انحراف از ایده‌آل است به عنوان کاراترین جواب انتخاب و به مدیران صنعت پیشنهاد می‌شود. عدم‌قطعیت داده‌ در مدل پیشنهادی با استفاده از رویکرد برنامه‌ریزی امکانی استوار کنترل شده است. نتایج عددی نشان می‌دهد که نه تنها نوسان بهینگی در رویکرد استوار پیشنهادی بسیار کمتر از رویکرد مقدار اسمی است بلکه به طور قابل ملاحظه‌ای نقص قیود کمتر می‌شود که موجب کاهش ریسک در تصمیم‌گیری است. به منظور حل مسئله پیشنهادی در ابعاد بزرگ، روش تجزیه بندرز بر مبنای روش حل چندهدفه اپسیلون محدودیت تکامل یافته به کار گرفته شده است. نتایج عددی نشان می‌دهند که رویکرد پیشنهادی به طور قابل ملاحظه در سه معیار کمی میانگین انحراف از ایده‌آل، کیفیت جواب‌ها و زمان اجرا بهبود ایجاد می‌کند و حل مسئله در ابعاد بزرگ را نیز میسر می‌سازد.

کلیدواژه‌ها


عنوان مقاله [English]

Modeling and Solving Problem Sustainable Closed Loop Supply Chain Network Design for Petrochemical Products under Uncertainty Conditions

نویسندگان [English]

  • mahmoud ahmadiazar 1
  • behroz dorri 2
  • Akbar Alem Tabriz 3
  • massoud kassai 4
1 PhD. student, Industrial Management Department Shahid Beheshti University, Tehran, Iran
2 Professor, faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.
3 Professor, faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.
4 Associate Professor, faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.
چکیده [English]

The petrochemical industry is one of the most important industries in the world where optimal management and decision-making in its activities will bring about great economic benefits as well as prosperity and development of related industries. This paper deals with the issue of petrochemical product supply chain management. A multi-objective optimization model is developed in which the strategic, long-term economic, social and environmental goals of the petrochemical industry are achieved. For this purpose, first using the Epsilon constrained evolution method, economic objective is considered as objective function and social and environmental goals are constrained as Epsilon. Then, the Pareto front is obtained from efficient solutions and in this front, the solution with the least deviation from the ideal is selected as the most efficient solution and recommended to industry managers. The data uncertainty in the proposed model is controlled using a robust feasibility planning approach. The numerical results show that not only the optimal fluctuation in the proposed robust approach is much less than the nominal value approach but it also significantly reduces the constraint flaw which reduces risk in decision making. In order to solve the proposed large-scale problem, the Banders decomposition method is applied based on the Epsilon multiple-constraint evolution method. Numerical results show that the proposed approach significantly improves the mean, standard deviation, and runtime in three quantitative measures and enables large-scale problem solving.

کلیدواژه‌ها [English]

  • Petrochemical Product Supply Chain
  • Multi-Objective Optimization
  • Robust Possibilistic Programming
  • Augmented Epsilon Constraint
  • Benders decomposition
[1]      D. Simchi-Levi, P. Kaminsky, and E. Simchi-Levi, Managing The Supply Chain: Definitive Guide. Tata McGraw-Hill Education, 2004.
[2]      A. Yousefi-Babadi, R. Tavakkoli-Moghaddam, A. Bozorgi-Amiri, and S. Seifi, "Designing a Reliable Multi-Objective Queuing Model of a Petrochemical Supply Chain Network under Uncertainty: A Case Study," Computers & Chemical Engineering, vol. 100, pp. 177-197, 2017/05/08/ 2017.
[3]      E. Hofmann, "Supply Chain Management: Strategy, Planning and Operation, S. Chopra, P. Meindl," Journal of Purchasing and Supply Management
[4]      K. Govindan, H. Soleimani, and D. Kannan, "Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future," European Journal of Operational Research, vol. 240, no. 3, pp. 603-626, 2015.
[5]      A. Jabbarzadeh, M. Haughton, and A. Khosrojerdi, "Closed-loop supply chain network design under disruption risks: A robust approach with real world application," Computers & Industrial Engineering,.
[6]      J. Quariguasi Frota Neto, G. Walther, J. Bloemhof, J. Van Nunen, and T. Spengler, "From closed-loop to sustainable supply chains: the WEEE case," International Journal of Production Research, 2010.
[7]      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, 2012.
[8]      S. Seuring, "A review of modeling approaches for sustainable supply chain management," Decision Support Systems, 2013.
[9]      m. farrokh, a. azar, and g. jandaghi, "A novel robust fuzzy programming approach for closed loop supply chain design," Modern Research in Decision Making, vol. 1, no. 3, pp. 131-160, 2016.
[10]   S. Mansoori, A. Bozorgi-Amiri, and F. Bayatloo, "A Bi-Objective Robust Optimization Model for Emergency Blood Supply Network Design under Uncertainty," Modern Research in Decision Making, 2018.
[11]   R. Cruz-Rivera and J. Ertel, "Reverse logistics network design for the collection of End-of-Life Vehicles in Mexico," European Journal of Operational Research, vol. 196, no. 3, pp. 930-939, 2009/08/01/ 2009.
[12]   M. S. Pishvaee and M. Rabbani, "A graph theoretic-based heuristic algorithm for responsive supply chain network design with direct and indirect shipment," Advances in Engineering Software,  2011.
[13]   S. Elhedhli and R. Merrick, "Green supply chain network design to reduce carbon emissions," Transportation Research Part D: Transport and Environment, vol. 17, no. 5, pp. 370-379, 2012.
[14]   T.-S. Su, "Fuzzy multi-objective recoverable remanufacturing planning decisions involving multiple components and multiple machines," Computers & Industrial Engineering, vol. 72, pp. 72-83, 2014.
[15]   M. Zohal and H. Soleimani, "Developing an ant colony approach for green closed-loop supply chain network design: a case study in gold industry," Journal of Cleaner Production, vol. 133, pp. 314-337, 2016/10/01/ 2016.
[16]   R. Babazadeh, J. Razmi, M. S. Pishvaee, and M. Rabbani, "A sustainable second-generation biodiesel supply chain network design problem under risk," Omega, vol. 66, pp. 258-277, 2017/01/01/ 2017.
[17]   A. M. F. Fard and M. Hajaghaei-Keshteli, "A tri-level location-allocation model for forward/reverse supply chain," Applied Soft Computing 2018.
[18]   M. Farrokh, A. Azar, G. Jandaghi, and E. Ahmadi, "A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty," Fuzzy Sets and Systems, 2018.
[19]   R. Más and J. M. Pinto, "A mixed-integer optimization strategy for oil supply in distribution complexes," Optimization and Engineering, 2003.
[20]   E. Schulz, S. Diaz, and A. Bandoni, "Supply chain optimisation in a petrochemical complex," in Computer Aided Chemical Engineering, vol. 18: Elsevier, 2004, pp. 997-1002.
[21]   A. Szklo, G. Machado, R. Schaeffer, J. Mariano, J. Sala, and M. Tavares, "The impacts of a GTL plant on Brazil's oil products supply and refinery expansion, 2005.
[22]   W. B. E. Al-Othman, H. M. S. Lababidi, I. M. Alatiqi, and K. Al-Shayji, "Supply chain optimization of petroleum organization under uncertainty in market demands and prices," European Journal of Operational Research, 008.
[23]   G. Ribas, A. Leiras, and S. Hamacher, "Tactical planning of the oil supply chain: optimization under uncertainty," in Simposio Brasileiro de Pesquisa Operacional, 2011, pp. 1-12.
[24]   K. Tong, Y. Feng, and G. Rong, "Planning under demand and yield uncertainties in an oil supply chain," Industrial & Engineering Chemistry Research, v2011.
[25]   L. J. Fernandes, S. Relvas, and A. P. Barbosa-Póvoa, "Strategic network design of downstream petroleum supply chains: single versus multi-entity participation," Chemical Engineering Research and Desig2013.
[26]   H. An and W. E. Wilhelm, "An exact solution approach based on column generation and a partial-objective constraint to design a cellulosic biofuel supply chain," Computers & chemical engineering, vol. 71, pp. 11-23, 2014.
[27]   C. Dai, Y. Cai, W. Ren, Y. Xie, and H. Guo, "Identification of optimal placements of best management practices through an interval-fuzzy possibilistic programming model," Agricultural Water Management, 2016.
[28]   S.-P. Wan and J.-Y. Dong, "Possibility linear programming with trapezoidal fuzzy numbers," Applied Mathematical Modelling, 2014.
[29]   H. Tanaka†, T. Okuda, and K. Asai, "On Fuzzy-Mathematical Programming," Journal of Cybernetics, vol. 3, no. 4, pp. 37-46, 1973/01/01 1973.
[30]   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, 11/1/ 2012.
[31]   G. Mavrotas, "Effective implementation of the ε-constraint method in multi-objective mathematical programming problems," Applied mathematics and computation, vol. 213, no. 2, pp. 455-465, 2009.