توسعه مدل برنامه‌ریزی تصادفی مبتنی بر سناریو برای مسأله طراحی شبکه زنجیره تأمین حلقه بسته با در نظر گرفتن فروش و اجاره مجدد

نوع مقاله: مقاله پژوهشی

نویسندگان

1 گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه خوارزمی، تهران، ایران

2 استادیار دانشگاه خوارزمی

3 گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه خورزمی، تهران، ایران

10.22084/ier.2020.17234.1791

چکیده

امروزه موضوع لجستیک معکوس و زنجیره تأمین حلقه بسته موردتوجه پژوهشگران زیادی قرار گرفته است. مهم­ترین دلایل این امر را می‌توان تأکید بر ملاحظات زیست‌محیطی، افزایش رضایت مشتری و مزایای اقتصادی حاصل از آن دانست. در پژوهش حاضر، یک مدل برنامه‌ریزی تصادفی مبتنی بر سناریو برای مسأله طراحی شبکه زنجیره تأمین حلقه بسته با درنظرگرفتن فروش و اجاره مجدد دارایی‌ها ارائه می‌شود. فروش و اجاره مجدد دارایی‌های ثابت می‌تواند نقدشوندگی کل دارایی‌ها را افزایش دهد و نقدینگی لازم برای سایر فعالیت‌ها را فراهم کند. مدل بهینه‌سازی تصادفی ارائه‌شده به‌دنبال بیشینه‌سازی سود زنجیره پس از کسر مالیات است و منطبق بر استانداردهای حسابداری فروش و اجاره مجدد طراحی می‌شود. پس از مدل‌سازی مسأله، حل آن به‌صورت دقیق انجام می‌شود و تأثیر فروش و اجاره مجدد بر شبکه ارزیابی می‌گردد. در نهایت، تحلیل حساسیت پارامترهای ضریب ذخیره اطمینان، ارزش منصفانه هر مرکز بالقوه به‌صورت مجزا، نرخ بهره ضمنی اجاره و نرخ استقراضی افزایش مستأجر صورت می‌گیرد.

کلیدواژه‌ها


عنوان مقاله [English]

A scenario-based stochastic optimization model for designing a closed-loop supply chain network considering sale and leaseback transactions

نویسندگان [English]

  • Shima Haghighatpanah 1
  • Hamed Davari-Ardakani 2
  • Ali Ghodratnama 3
1 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
2 Department of Industrial Engineering, Kharazmi University, Tehran, Iran
3 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
چکیده [English]

Nowadays, many researchers focus on reverse logistics and closed-loop supply chain network design. The main reason is the ever-increasing importance of environmental issues, customer satisfaction and economic benefits. In this paper, a scenario-based optimization model is proposed to deal with the closed-loop supply chain network design problem considering sale and leaseback (SLB) transactions. SLB transactions increase the liquidity of total assets and provide monetary resources for other activities. The proposed model, which is formulated based on sales accounting standards, maximizes the supply chain’s benefit after tax. The proposed model is solved to optimality. Finally, a sensitivity analysis on the safety stock coefficient, the fair value of the leased asset, the interest rate implicit in the lease and lessee's incremental borrowing rate is performed to assess the impact of these parameters on the expected value of supply chain’s benefit after tax.

کلیدواژه‌ها [English]

  • Closed-loop supply chain network design
  • Scenario-based stochastic optimization
  • Sale and leaseback
[1] Vahdani, B., Razmi, J., Tavakkoli-Moghadam, R. (2012). “Fuzzy possibilistic modeling for Closed loop recycling collection networks”, Environmental Modeling & Assessment, 17: 623-637.

[2] 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: 2668-2683.

[3] Longinidis, P., Georgiadis, M.C. (2013). “Integration of sale and leaseback in the optimal design of supply chain networks”, Omega, 47: 73-89.

[4] Pishvaee, M.S., Jolai, F., Razmi, J. (2009). “A stochastic optimization model for integrated forward/reverse logistics network design”, Journal of Manufacturing Systems, 28: 107-114.

[5] Pokharel, S., Mutha, A. (2009). “Perspectives in reverse logistics: a review, Resources”, Conservation and Recycling, 53: 175-182.

[6] Fleischmann, M., Beullens, P., Bloemhof-Ruwaard, J.M., Wassenhove, L. (2001). “The impact of product recovery on logistics network design”, Production and Operations Management, 10: 156-173.

[7] Pishvaee, M.S., Zanjirani Farahani, R., Dullaert, W. (2008). “A memetic algorithm for bi-objective integrated forward/reverse logistics network design”, Computers & Operations Research, 37: 1100-1112.

[8] Salema, M.I.G., Barbosa-Povoa, A.P., Novais, A.Q. (2007). “An optimization model for the design of a capacitated multi-product reverse logistics network, with uncertainty”, European Journal of Operational Research, 179:1063-1077.

[9] Lu, Z., Bostel, N. (2007). “A facility location model for logistics systems including reverse flows: The case of remanufacturing activities”, Computers & Operations Research, 34: 299-323.

[10] Qin, Zh., Ji X. (2009). “Logistics network design for product recovery in fuzzy environment”, European Journal of Operational Research, 202: 479-490.

[11] El-Sayed, M., Afia, N., El-Kharbotly A. (2010). “A stochastic model for forward-reverse logistics network design under risk”, Computers & Industrial Engineering, 58: 423-431.

[12] Subrata, M. (2012). “Inventory management in a two-echelon closed-loop supply chain with correlated demands and returns”, Computers and Industrial Engineering, 62: 870-879.

[13] Garg, K., Kannan, D., Diabat, A., Jha, P.C. (2015). “A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design”, Journal of Cleaner Production, 100: 297–314.

[14] Rumin, M.A., Lifei, Y.A.O., Maozhu, J.I.N., Peiyu, R.E.N. (2016). “Robust environmental closed-loop supply chain design under uncertainty”, Chaos, Solitons & Fractals, 89: 195-202.

[15] Badri, H., Fatemi Ghomi, S.M.T., Hejazi, T.H. (2017). “A two-stage stochastic programming approach for value-based closed-loop supply chain network design”, Transportation Reasearch Part E: Logistics and Transportation Review, 105: 1-17.

[16] Yan Cui, Y., Guan, Z., Saif, U., Zhang, L., Zhang, F., Mirza, J., (2017). “Close loop supply chain network problem with uncertainty in demand and returned products: Genetic artificial bee colony algorithm approach”, Journal of Cleaner Production, 162: 717-742.

[17] Pant, K., Singh, A.R., Pandey, U., Purohit, R. (2018). “A Multi echelon mixed integer linear programming model of a close loop supply chain network design”, Materials Today: Proceedings, 5: 4838-4846.

[18] Asim, Z., Jalil, S., Javaid, S. (2019). “An uncertain model for integrated production-transportation closed-loop supply chain network with cost reliability”, Sustainable Production and Consumption, 17: 298-310.

[19] Guillén, G., Badell, M., Puigjaner, L. (2007). “A holistic framework for short-term supply chain management integrating production and corporate financial planning”, International Journal of ProductionEconomics, 106: 288-306.

[20] Puigjaner, L., Guillén- Gosálbez, G. (2008). “Towards an integrated framework for supply chain management in the batch chemical process industry”, Computer & Chemical Engineering, 32: 650-670.

[21] Sodhi, M.S., Tang, C.S. (2009). “Modeling supply-chain planning under demand uncertainty using stochastic programming: a survey motivated by asset-liability Management”, International Journal of Production Economics, 121: 728-738.

[22] Naraharisetti, P.K., Karimi, I.A., Srinivasan, R. (2008). “Supply chain redesign through optimal asset management and capital budgeting”, Computers & Chemical Engineering, 32: 3153-3169.

[23] Laínez, J.M., Puigjaner, L., Reklaitis, G.V. (2009). “Financial and financial engineering considerations in supply chain and product development pipeline management”, Computers and Chemical Engineering, 33: 1999-2011.

[24] Longinidis, P., Georgiadis, M.C. (2011). “Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty”, International Journal of Production Economics, 129: 262-276.

[25] Nickel, S., Saldanha-da-Gama, F., Ziegler, H.P. (2012). “A multi-stage stochastic supply network design problem with financial decisions and risk management”, Omega, 40: 511-524.

[26] Ramezani, M., Kimiagari, A.M., Karimi, B. (2014). “Closed-loop supply chain network design: a financial approach”, Applied Mathematical Modeling, 38: 4099-4119.

[27] Kalantari, M., Pishvaee, M., Yaghoubi, S. (2015). “A multi objective model integrating financial and material flow in supply chain master planning”, Industrial Management Prespective, 19: 9-31. (In Persion).

[28] Mohammadi, A., Abbasi, A., Alimohammadlou, M., Eghtesadifard, M., Khalifeh, M. (2017). “Optimal design of a multi-echelon supply chain in a system thinking framework: An integrated financial-operational approach”, Computers & Industrial Engineering, 114: 297-315.

[29] Vafa Arani, H., Torabi, S.A., (2018). “Integrated material-financial supply chain master planning under mixed uncertainty”,Information Sciences, 423: 96-114.

[30] Yanga, H., Sun, F., Chen, J., Chen, B., (2019). “Financing decisions in a supply chain with a capital-constrained manufacturer as new entrant”,International Journal of Production Economics, 216: 321-332.

[31] Ling, N.L.F.J. (2012). “Analysis of Factors and the Impacts of Sale and Leaseback Transaction”, Procdia-Social and Behavioral Sciences, 36: 502-510.