نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکترا
2 دانشکده مدیریت وحسابداری ، دانشگاه علامه طباطبایی
3 هیئت علمی و مدیر گروه مدیریت صنعتی دانشگاه سمنان
4 دانشگاه علم و صنعت ایران
چکیده
در سال‌های اخیر پیچیدگی‌های محیطی، رقابت‌های شدید سازمان‌ها و فشار دولت‌ها بر تولیدکنندگان برای مدیریت پسماند محصولات، فشارهای زیست‌محیطی و از همه مهم‌تر سود ناشی از بازیافت محصولات، بر اهمیت طراحی شبکه زنجیره تأمین معکوس و حلقه بسته افزوده است. همچنین وجود عدم قطعیت‌های ذاتی در پارامترهای ورودی، یکی دیگر از موارد مهمی است که عدم توجه به آن می‌تواند تصمیمات استراتژیک، تاکتیکی و عملیاتی سازمان را تحت تأثیر قرار دهد. به همین جهت این پژوهش به طراحی یک مدل شبکه زنجیره تأمین حلقه بسته چند محصولی و چند دوره‌ای در شرایط عدم قطعیت می‌پردازد. در همین راستا ابتدا یک مدل برنامه‌ریزی خطی عدد صحیح به منظور حداقل سازی هزینه‌های زنجیره تأمین ارائه می‌گردد. سپس جهت در نظر گرفتن عدم قطعیت‌های ترکیبی مدل که شامل عدم قطعیت شناختی و تصادفی می‌باشد، پنج مدل استوار امکانی- تصادفی مختلف توسعه داده‌شده و نقاط ضعف، قوت و کاربرد هر یک مورد ارزیابی و تحلیل قرار می‌گیرد و مناسب‌ترین مدل جهت پاسخگویی به عدم قطعیت‌های موجود در مدل پیشنهاد می‌شود. در پایان عملکرد و کاربردی بودن مدل پیشنهادی، از طریق مطالعه موردی در یک صنعت روغن خوراکی مورد ارزیابی قرار می‌گیرد.
کلیدواژه‌ها
عنوان مقاله [English]
A closed-loop supply chain network in the edible oil industry using a novel robust stochastic-possibilistic programming
نویسندگان [English]
- maghsoud Amiri 2
- mohsen shafiei nikabadi 3
- Armin Jabbarzadeh 4
1
2 Department of Industrial Management, Management and Accounting Faculty, Allame Tabataba’i University
3 Department of Industrial Management, Faculty of Economic and Management, Semnan University,Semnan, Iran
4 Department of Industrial Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
چکیده [English]
In recent years, the complexity of the environment, the intense competition of organizations, the pressure of governments on producers to manage waste products, environmental pressures and most importantly, the benefits of recycling products have added to the importance of designing a closed loop supply chain network. Also, the existence of inherent uncertainties in the input parameters is another important factor that the lack of attention them can affect the strategic, tactical and operational decisions of organizations. Given these reasons, this research aims to design a multi-product and multi period closed loop supply chain network model in uncertainty conditions. To this aim, first a mixed-integer linear programming model is proposed to minimize supply chain costs. Then, for coping with hybrid uncertain parameters effectively, randomness and epistemic uncertainty, a novel robust stochastic-possibilistic programming (RSPP) approach is proposed. Furthermore, several varieties of RSPP models are developed and their differences, weaknesses, strengths and the most suitable conditions for being used are discussed. Finally, usefulness and applicability of the RSPP model are tested via the real case study in an edible oil industry.
کلیدواژه‌ها [English]
- edible oil supply chain
- closed loop supply chain
- Possibilistic Programming
- Stochastic Programming
- Robust Programming
Azaron, A., Brown, K. N., Tarim, S. A., & Modarres, M. (2008). A multi-objective stochastic programming approach for supply chain design considering risk. International Journal of Production Economics, 116 (1), 129-138.
Babazadeh, R., Razmi, J., Pishvaee, M. S., & Rabbani, M. (2017). A sustainable second-generation biodiesel supply chain network design problem under risk. Omega, 66, 258-277.
Ben-Tal, A., & Nemirovski, A. (1998). Robust solutions of uncertain linear programs. Operations research letters, 25(1), 1-13.
Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical programming, 88(3), 411-424.
Ben-Tal, A., & Nemirovski, A. (2009). Selected topics in robust convex optimization. Mathematical Programming, 112(1), 125-158.
Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations research, 52(1), 35-53.
Chouinard, M., D’Amours, S., & Aït-Kadi, D. (2008). A stochastic
programming approach for designing supply loops. International Journal of Production Economics, 113(2), 657-677
Cruz-Rivera, R., & Ertel, J. (2009). Reverse logistics network design for the
collection of end-of-life vehicles in Mexico. European Journal of Operational Research, 196(3), 930-939.
Dubois, D., & Prade, H. (1987). The mean value of a fuzzy number. Fuzzy sets and systems, 24(3), 279-300.
El-Sayed, M., Afia, N., & El-Kharbotly, A. (2010). A stochastic model for forward–reverse logistics network design under risk. Computers & Industrial Engineering, 58(3), 423-431.
Farrokh, M., Azar, A., Jandaghi, G., & Ahmadi, E. (2017). A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty. Fuzzy Sets and Systems. 1(3), 131-160.
Fleischmann, M., Beullens, P., BLOEMHOF‐RUWAARD, J. M., & Wassenhove, L. N. (2001). The impact of product recovery on logistics network design. Production and operations management, 10(2), 156-173.
Gaur, J., Amini, M., & Rao, A. K. (2017). Closed-loop supply chain configuration for new and reconditioned products: An integrated optimization model. Omega, 66, 212-223.
Govindan, K., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603-626.
Hasani, A., & Hosseini, S.M.H., (2015). A Comprehensive Robust Biobjective Model and a Memetic Solution Algorithm for Designing Reverse Supply. Journal of Indusrial Mangement Perspective, 16, 31-54 (In Persion).
Hatefi, S. M., & Jolai, F. (2014). Robust and reliable forward–reverse
logistics network design under demand uncertainty and facility disruptions. Applied Mathematical Modelling, 38(9), 2630-2647.
Inuiguchi, M., & Ramık, J. (2000). Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy sets and systems, 111(1), 3-28.
Inuiguchi, M., & Sakawa, M. (1998). Robust optimization under softness in a fuzzy linear programming problem. International Journal of Approximate Reasoning, 18(1-2), 21-34.
Jayaraman, V., Patterson, R. A., & Rolland, E. (1999). The design of reverse distribution networks: Models and solution procedures. European journal of operational research, 150(1), 128-149.
Keyvanshokooh, E., Ryan, S. M., & Kabir, E. (2016). Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition. European Journal of Operational Research, 249(1), 76-92.
Klibi, W., & Martel, A. (2012). Scenario-based supply chain network risk modeling. European Journal of Operational Research, 223(3), 644-658.
Ko, H. J., & Evans, G. W. (2007). A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Computers & Operations Research, 34(2), 346-366.
Lee, D. H., & Dong, M. (2009). Dynamic network design for reverse
logistics operations under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 45(1), 61-71.
Liu, B., & Iwamura, K. (1998). Chance constrained programming with fuzzy parameters. Fuzzy sets and systems, 94(2), 227-237.
Liu, B., & Liu, Y. K. (2002). Expected value of fuzzy variable and fuzzy expected value models. IEEE transactions on Fuzzy Systems, 10(4), 445-450.
Melo, M. T., Nickel, S., & Saldanha-Da-Gama, F. (2009). Facility location and supply chain management–A review. European journal of operational research, 196(2), 401-412
Min, H., & Ko, H. J. (2008). The dynamic design of a reverse logistics
network from the perspective of third-party logistics service providers. International Journal of Production Economics, 113(1), 176-192
Min, H., Ko, H. J., & Ko, C. S. (2006). A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns. Omega, 34(1), 56-69.
Mousazadeh, M., Torabi, S. A., & Pishvaee, M. S. (2014). Green and reverse logistics management under fuzziness in Supply Chain Management Under Fuzziness (pp. 607-637). Springer Berlin Heidelberg.
Mula, J., Poler, R., & Garcia, J. P. (2006). MRP with flexible constraints: A fuzzy mathematical programming approach. Fuzzy sets and systems, 157(1), 74-97.
Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations research, 43(2), 264-281.
Nurjanni, K. P., Carvalho, M. S., & Costa, L. (2017). Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model. International Journal of Production Economics, 183, 421-432.
Paksoy, T., Pehlivan, N. Y., & Özceylan, E. (2012). Application of fuzzy optimization to a supply chain network design: a case study of an edible vegetable oils manufacturer. Applied Mathematical Modelling, 36(6), 2762-2776.
Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems, 161(20), 2668-2683.
Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012a). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems, 206, 1-20.
Pishvaee, M. S., Torabi, S. A., & Razmi, J. (2012b). Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty. Computers & Industrial Engineering, 62(2), 624-632.
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Qin, Z., & Ji, X. (2010). Logistics network design for product recovery in fuzzy environment. European Journal of Operational Research, 202(2), 479-490.
Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A robust design for a closed-loop supply chain network under an uncertain environment. The International Journal of Advanced Manufacturing Technology, 66(5-8), 825-843.
Sahinidis, N. V. (2004). Optimization under uncertainty: state-of-the-art and opportunities. Computers & Chemical Engineering, 28(6), 971-983.
Soyster, A. L. (1973). Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations research, 21(5), 1154-1157.
Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy sets and systems, 159(2), 193-214.
Üster, H., Easwaran, G., Akçali, E., & Çetinkaya, S. (2007). Benders decomposition with alternative multiple cuts for a multi‐product closed‐loop supply chain network design model. Naval Research Logistics (NRL), 54(8), 890-907.
Validi, S., Bhattacharya, A., & Byrne, P. J. (2015). A solution method for a two-layer sustainable supply chain distribution model. Computers & Operations Research, 54, 204-217.
Winkler, H. (2011). Closed-loop production systems—A sustainable supply
chain approach. CIRP Journal of Manufacturing Science and Technology, 4(3), 243- 246.
Xu, J., & Zhou, X. (2013). Approximation based fuzzy multi-objective models with expected objectives and chance constraints: Application to earth-rock work allocation. Information Sciences, 238, 75-95.
Yu, C. S., & Li, H. L. (2000). A robust optimization model for stochastic logistic problems. International journal of production economics, 64(1), 385-397.
Zeballos, L. J., Méndez, C. A., Barbosa-Povoa, A. P., & Novais, A. Q. (2014). Multi-period design and planning of closed-loop supply chains with uncertain supply and demand. Computers & Chemical Engineering, 66, 151-164.
Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Mohammadi, M.
(2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 89, 182-214.
Zhu, H., & Zhang, J. (2009, November). A credibility-based fuzzy programming model for APP problem. In Artificial Intelligence and Computational Intelligence, 2009. AICI'09. International Conference on (Vol. 1, pp. 455-459). IEEE.
Zohal, M., & Soleimani, H. (2016). Developing an ant colony approach for green closed-loop supply chain network design: a case study in gold industry. Journal of Cleaner Production, 133, 314-337.
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