IJE TRANSACTIONS A: Basics Vol. 31, No. 7 (July 2018) 1427-1435    Article in Press

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M. Hajiaghaei-Keshteli, K. S. Abdallah and A. M. Fathollahi Fard
( Received: December 23, 2017 – Accepted: February 15, 2018 )

Abstract    Recent papers in the concept of Supply Chain Network Design (SCND) have seen a rapid development of using uncertain models to get closer to real applications. According to the type of the products, e.g. tire, the structure of supply chain network varies. In tire industry, the difficulties in degradation of scrapped tires and the difficulties in recovering material and energy costs lead to recycling scraped tires through a closed-loop supply chain network. This paper proposes a two-stage stochastic model for tire closed-loop SCND. In the first stage the model optimizes the expected cost, then, the financial risk is incorporated as an objective function in the second stage of the model to control the uncertainty variables leading to a robust solution. To solve the problem, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used. To enhance the efficiency of algorithms, Response Surface method (RSM) is utilized. The proposed model is evaluated using different problems with different complexity, and different metrics of the Pareto front are used to compare the proposed model.


Keywords    Two-stage stochastic programming, Tire closed-loop supply chain, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Response Surface Method (RSM).


چکیده    مقاله های اخیر در مورد طراحی شبکه زنجیره تامین به سرعت در حال توسعه با استفاده از مدل های تحت عدم قطعیت برای نزدیک شدن به کاربردهای دنیای واقعی است. با توجه به نوع محصولات مانند چرخ خودرو، ساختار زنجیره تامین تغییر می کند. در صنعت تایر، سختی ها و باز استفاده تایرهای کهنه و همه این سختی ها در بازسازی انرژی و هزینه های اضافه منجر به بازیافت چرخ های خودرو استفاده شده در میان یک طراحی شبکه زنجیره تامین حلقه بسته می شود. این مقاله، یک مدل دو مرحله ای تصادفی را برای زنجیره تامین حلقه بسته تایر طراحی می کند. در مرحله اول، مدل هزینه انتظاری را بهینه سازی می‌کند. سپس، ریسک مالی در یک تابع هدف مجزا در مرحله دوم مدل اقدام به کنترل عدم قطعیت متغیرهای تصمیم گیری برای رسیدن به یک جواب پایدار می کند. برای حل این مساله، الگوریتم بهینه سازی ازدحام ذرات و الگوریتم ژنتیک به کار گرفته شده ‌اند. برای افزایش اثربخشی الگوریتم ها، روش سطح پاسخ به کار گرفته شده است. مدل پیشنهاد شده با مسائل مختلف با سطح های دشواری متفاوت و پارامترهای ارزیاب مختلف برای جواب های بهینه پارتو با یکدیگر مقایسه شده اند.

References    References: Amin, G. R., & Toloo, M. (2007). Finding the most efficient DMUs in DEA: An improved integrated model. Computers & Industrial Engineering52(1), 71-77. Amin, S. H., Zhang, G., & Akhtar, P. (2017). Effects of uncertainty on a tire closed-loop supply chain network. Expert Systems with Applications73, 82-91. Arabani, A. B., Zandieh, M., & Ghomi, S. F. (2011). Multi-objective genetic-based algorithms for a cross-docking scheduling problem. Applied Soft Computing11(8), 4954-4970. Cheraghalipour, A., Paydar, M. M., & Hajiaghaei-Keshteli, M. (2017). An integrated approach for collection center selection in reverse logistics. Int J Eng Trans A Basics, 30(7), 1005-1016. Chopra, S. (2003). Designing the distribution network in a supply chain. Transportation Research Part E: Logistics and Transportation Review39(2), 123-140. Chopra, S., Meindl, P. (2015). Supply Chain Management: Strategy, Planning and Operation, 6th Edition, Pearson, New York. Devika, K., Jafarian, A., & Nourbakhsh, V. (2014). Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques. European Journal of Operational Research235(3), 594-615. Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on (pp. 39-43). IEEE. Farahani, R. Z., Rezapour, S., Drezner, T., & Fallah, S. (2014). Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega45, 92-118. Fathollahi Fard, A. M., Gholian-Jouybari, F., Paydar, M. M., & Hajiaghaei-Keshteli, M., (2017). A Bi-Objective Stochastic Closed-loop Supply Chain Network Design Problem Considering Downside Risk. Industrial Engineering & Management Systems16(3), 342-362. Fathollahi Fard, A. M., Hajiaghaei-Keshteli, M., & Paydar, M. M., (2018). A Location-Allocation-Routing Model for Home Health Care Supply Chain Problem. World Academy of Science, Engineering and Technology, International Journal of Industrial and Manufacturing Engineering5(3). Fathollahi Fard, A. M., & Hajiaghaei-Keshteli, M. (2018). A tri-level location-allocation model for forward/ reverse supply chain. Applied Soft Computing, 62, 328-346. Ferrer, G. (1997). The economics of tire remanufacturing. Resources, conservation and recycling19(4), 221-255. Govindan, K., & Soleimani, H. (2017). A review of reverse logistics and closed-loop supply chains: a Journal of Cleaner Production focus. Journal of Cleaner Production142, 371-384. 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 Research240(3), 603-626. Hajiaghaei-Keshteli, M., & Aminnayeri, M. (2013). Keshtel Algorithm (KA); a new optimization algorithm inspired by Keshtels’ feeding. In Proceeding in IEEE Conference on Industrial Engineering and Management Systems, (pp. 2249-2253). Hajiaghaei-Keshteli, M., & Aminnayeri, M. (2014). Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm. Applied Soft Computing25, 184-203. Hajiaghaei-Keshteli, M., Aminnayeri, M., & Ghomi, S. F. (2014). Integrated scheduling of production and rail transportation. Computers & Industrial Engineering74, 240-256. Hajiaghaei-Keshteli, M., & Sajadifar, S. M. (2010). Deriving the cost function for a class of three-echelon inventory system with N-retailers and one-for-one ordering policy. The International Journal of Advanced Manufacturing Technology50(1-4), 343-351. Hajiaghaei-Keshteli, M., Molla-Alizadeh-Zavardehi, S., & Tavakkoli-Moghaddam, R. (2010). Addressing a nonlinear fixed-charge transportation problem using a spanning tree-based genetic algorithm. Computers & Industrial Engineering59(2), 259-271. Hiremath, N. C., Sahu, S., & Tiwari, M. K. (2013). Multi objective outbound logistics network design for a manufacturing supply chain. Journal of Intelligent Manufacturing24(6), 1071-1084. Holland, J. H. (1975). Adaptation in natural and artificial systems. An introductory analysis with application to biology, control, and artificial intelligence. Ann Arbor, MI: University of Michigan Press. Jo, J-B., Li Y., Gen M., (2007). Nonlinear fixed charge transportation problem by spanning tree-based genetic algorithm. Computers and Industrial Engineering, 53, (2), 290–298. Kannan, G., Noorul Haq, A., & Devika, M. (2009). Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation. International Journal of Production Research47(5), 1175-1200. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science220(4598), 671-680. Lee, D. H., Cao, Z., & Meng, Q., (2007). Scheduling of two-transtainer systems for loading outbound containers in port containers terminals with simulated annealing algorithm. International Journal of Production Economics, 107(1), 115-124. Maiti, T., & Giri, B. C. (2017). Two-way product recovery in a closed-loop supply chain with variable markup under price and quality dependent demand. International Journal of Production Economics183, 259-272. Mirakhorli, A. (2014). Fuzzy multi-objective optimization for closed loop logistics network design in bread-producing industries. The International Journal of Advanced Manufacturing Technology70(1-4), 349-362. Nasiri, E., Afshari, A. J., Hajiaghaei-Keshteli, M., (2017). Addressing the Freight Consolidation and Containerization Problem by Recent and Hybridized Metaheuristic Algorithms, IJE TRANSACTIONS C: Aspects Vol. 30, No. 3 (March 2017) 403-410. Nourmohamadi Shalke, P., Paydar, M. M., & Hajiaghaei-Keshteli, M. (2017). Sustainable supplier selection and order allocation through quantity discounts. International Journal of Management Science and Engineering Management, 1-13. Sasikumar, P., Kannan, G., & Haq, A. N. (2010). A multi-echelon reverse logistics network design for product recovery—a case of truck tire remanufacturing. The International Journal of Advanced Manufacturing Technology49(9-12), 1223-1234. Souza, G. C. (2013). Closed‐loop supply chains: a critical review, and future research. Decision Sciences44(1), 7-38. Subulan, K., Baykasoğlu, A., Özsoydan, F. B., Taşan, A. S., & Selim, H. (2015b). A case-oriented approach to a lead/acid battery closed-loop supply chain network design under risk and uncertainty. Journal of Manufacturing Systems37, 340-361. Subulan, K., Taşan, A. S., & Baykasoğlu, A. (2015a). Designing an environmentally conscious tire closed-loop supply chain network with multiple recovery options using interactive fuzzy goal programming. Applied Mathematical Modelling39(9), 2661-2702. Syarif, A., Yun Y., Gen, M., (2002). Study on multi-stage logistic chain network: A spanning tree-based genetic algorithm approach. Computers and Industrial Engineering, 43, (1–2), 299–314.

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