مدل‌سازی چندهدفه زنجیره تأمین معکوس به روش استوار در شرایط عدم قطعیت تقاضا با بهره‌گیری از الگوریتم فرا ابتکاریNSGA-II در صنعت فولاد

نوع مقاله : مقاله پژوهشی

نویسندگان

1 عضو هیئت علمی دانشگاه تهران

2 عضو هیئت علمی دانشگاه خاتم

3 پردیس البرز دانشگاه تهران، تهران، ایران

چکیده

در طراحی زنجیره تأمین استفاده از محصولات برگشتی و چرخه دوباره آن‌ها در شبکه تولید و مصرف در قالب لجستیک معکوسمطرح می‌شود.مدل پیشنهادی به بهینه سازی جریان مواد در شبکه زنجیره‌­تاٌمین، تعیین مقدار و مکان تسهیلات و برنامه‌ریزی حمل­‌ونقل در شرایط عدم قطعیت تقاضا می‌پردازد به گونه‌ای‌که: کل سود عملیاتی زنجیره حداکثر، اثرات نامساعد زیست محیطی حداقل و سطح خدمت‌دهی به مشتریان و تاٌمین کنندگان زنجیره حداکثر شود.برای مواجهه با عدم قطعیت مدل از برنامه ریزی استوار مبتنی بر سناریو و برای حل مدل با داده‌های واقعی در صنعت فولاد از الگوریتم فراابتکاری NSGA-II استفاده شده است.نتایج بدست آمده مدل از مجموعه داده‌های واقعی و اعتبارسنجی انجام شده نشان می‌دهد که مدل می‌تواند بصورت یکپارچه نسبت به بهینه سازی اهداف و تعیین تعداد و مکان تسهیلات لازم برای صنعت فولاد، کارایی لازم را به همراه داشته باشد. 

کلیدواژه‌ها

عنوان مقاله [English]

Multi-Objective Modeling of a Reverse Supply Chain by Robust in the Uncertainty of Demand Conditions Using a Meta-Heuristic Algorithm (NSGA-II) in Steel Industry

نویسندگان [English]

  • Ahmad Jafarnejad Chaghooshi 1
  • Hannan Amoozad Mahdiraji 1
  • Seyedhossein Razavi Hajiagha 2
  • Amir Karegar Soltanabad 3
1 Dr. Faculty Member of Tehran University
2 Dr.Faculty Member of Khatam University
3 Student at Tehran University, Alborz Campus
چکیده [English]

Abstract: In design of the supply chain, the use of returned products and their re-cycles in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network, determining the amount and location of facilities and planning of transportation in conditions of uncertainty of demand. So that: Maximize total profit of operation, Minimize Adverse environmental effects, Maximize customer & supplier service level. In order to deal with the uncertainty of the model, a scenariobased robust planning is used and to solve the model with the actual data of the case study in the steel industry, a meta-heuristic algorithm (NSGA-II) is utilized. The results of the model obtained from the actual data set and data validation indicate that the model can be integrated in optimizing the objectives and determining the amount and location of the necessary facilities in the steel industry. 

کلیدواژه‌ها [English]

  • Multi Objective Planning
  • Reverse Supply Chain
  • Robust Optimization
  • Meta Heuristic Algorithm
  • Steel Making Industry 

مراجع

[1] Lee, D.H. & Dong, M. (2009). A heuristic approach to logistics network design for end-of-lease computer products recovery. Transportation Research E, 44, 455-474
[2] Chaabane, A., Ramudhin, A., & Paquet, M. (2012). Design of sustainable supply chains under the emission trading scheme. International Journal of Production Economics, 135(1), 37-49
[3] Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637-649.
[4] Lasunon, P., Remsungnen, T., & Akararungruangkul, R. (2011). A stochastic tri-level programming model to minimize total cost in a supply chain planning with uncertainty demand. In Proceeding of 2nd international conference on logistics and transport, Queenstown, New Zealand,  693-701
[5] Boukherroub, T., Ruiz, A., Guinet, A., & Fondrevelle, J. (2015). An integrated approach for sustainable supply chain planning. Computers & Operations Research, 54, 180-194
[6] Talaei, M., Moghaddam, B. F., Pishvaee, M. S., Bozorgi-Amiri, A., & Gholamnejad, S. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry. Journal of Cleaner Production, 113, 662-673
[7] Nenes, G., & Nikolaidis, Y. (2012). A multi-period model for managing used product returns. International Journal of Production Research, 50(5), 1360–1376
[8] Das, K., & Chowdhury, A. H. (2012). Designing a reverse logistics network for optimal collection, recovery and quality-based product-mix planning. International Journal of Production Economics, 135(1), 209–221
[9] Guo, S., Aydin, G., & Souza, G. C. (2014). Dismantle or remanufacture? European Journal of Operational Research, 233(3), 580–583.
[10] Niknejad, A., & Petrovic, D. (2014). Optimisation of integrated reverse logistics networks with different product recovery routes. European Journal of Operational Research, 238(1), 143-154.
[11] Santibanez-Gonzalez, E. D., & Diabat, A. (2016). Modeling logistics service providers in a non-cooperative supply chain. Applied Mathematical Modelling, 40, 6340-6358
[12] Azadeh, A., Raoofi, Z., & Zarrin, M. (2015). A multi-objective fuzzy linear programming model for optimization of natural gas supply chain through a greenhouse gas reduction approach. Journal of Natural Gas Science and Engineering, 26, 702-710
[13] Memari, A., Rahim, A. R. A., & Ahmad, R. B. (2015). An integrated production-distribution planning in green supply chain: a multi-objective evolutionary approach. Procedia Cirp, 26, 700-705
[14] 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
[15] Ramudhin, A., Chaabane, A., & Parquet, A.M. (2010). On the design of sustainable green supply chains”, International Conference on Computers and Industrial Engineering,CIE, 979-984
[16] Sarrafha, K., Rahmati, S. H. A., Niaki, S. T. A., & Zaretalab, A. (2015). A bi-objective integrated procurement, production, and distribution problem of a multi-echelon supply chain network design: A new tuned MOEA. Computers & Operations Research, 54, 35-51
[17] Pishvaee, M. S., & Khalaf, M. F. (2016). Novel robust fuzzy mathematical programming methods. Applied Mathematical Modelling, 40(1), 407-418
[18] Feitó-Cespón, M., Sarache, W., Piedra-Jimenez, F., & Cespón-Castro, R. (2017). Redesign of a sustainable reverse supply chain under uncertainty: A case study. Journal of Cleaner Production, 151, 206-217.
[19] Rafie-Majd, Z., Pasandideh, S. H. R., & Naderi, B. (2018). Modelling and solving the integrated inventory-location-routing problem in a multi-period and multi-perishable product supply chain with uncertainty: Lagrangian relaxation algorithm. Computers & Chemical Engineering, 109, 9-22
[20] Musavi, M., & Rayat, F. (2017). A Bi-Objective Green Truck Routing and Scheduling Problem in a Cross Dock with the Learning Effect. Iranian Journal of Operations Research, 8(1), 2-14
[21] Mogale, D. G., Kumar, M., Kumar, S. K., & Tiwari, M. K. (2018). Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network. Transportation Research Part E: Logistics and Transportation Review, 111, 40-69
[22] Dai, Z., Aqlan, F., Zheng, X., & Gao, K. (2018). A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints. Computers & Industrial Engineering, 119, 338-352.
[23] Khodaparasti, S., Bruni, M. E., Beraldi, P., Maleki, H. R., & Jahedi, S. (2018). A multi-period location-allocation model for nursing home network planning under uncertainty. Operations Research for Health Care. https://doi.org/10.1016/j.orhc.2018.01.005
[24] Doolun, I. S., Ponnambalam, S. G., Subramanian, N., & Kanagaraj, G. (2018). Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence. Computers & Operations Research. https://doi.org/10.1016/j.cor.2018.01.008
 
نشریه علمی پژوهشی مهندسی و مدیریت کیفیت
دوره 8، شماره 4
اسفند 1397
صفحه 242-258

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