طراحی شبکه زنجیره تأمین پت با تقاضای وابسته به قیمت: الگوریتم فراابتکاری با روش نمایش تطبیق یافته

نوع مقاله : مقاله پژوهشی

نویسندگان

1 پژوهشگر پسادکتری، مرکز هنری فایول، دانشکده ریاضی و مهندسی صنایع، دانشگاه اکول دمین، سنت اتین، فرانسه

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

3 استادیار، گروه مدیریت صنعتی، دانشکده مدیریت، واحد الکترونیکی،دانشگاه آزاد اسلامی ، تهران، ایران

4 استادیار، گروه مدیریت صنعتی، دانشکده مدیریت، واحد الکترونیکی، دانشگاه آزاد اسلامی، تهران، ایران

چکیده

در ایران صنعت بازیافت پت نوپاست و گرچه به تازگی شکل گرفته اما به دلیل برگشت اقتصادی مناسب، افراد و شرکت‌های زیادی به این صنعت روی آورده‌اند. ایجاد ساختار مناسب در شبکه توزیع و جمع‌آوری این محصولات می‌تواند زمینه مناسب جهت کاهش مصرف آن و در عوض استفاده از کالاهای دوست‌دار محیط زیست را فراهم آورد. مقاله حاضر به ارائه یک مدل برنامه-ریزی خطی عدد صحیح آمیخته برای طراحی شبکه زنجیره تأمین می‌پردازد که در آن تقاضای مشتریان وابسته به قیمت عرضه‌شده از سوی مراکز توزیع است. در مدل ارائه‌شده به دو روند اصلی جهانی در این صنعت توجه شده است: 1-اقتصادی کردن جمع‌آوری و بازیافت بطری، 2-هزینه‌دار کردن مصرف بطری‌های پلاستیکی.
در ادامه از الگوریتم بهینه‌سازی یادگیری و آموزش برای حل مدل پیشنهادی بهره جسته‌ایم. ازآنجایی‌که این الگوریتم برای روش‌های نمایش پیوسته معرفی‌شده و روش نمایش مورداستفاده این مقاله روش اولویت محور و گسسته است، اصلاحاتی برای تطبیق این الگوریتم با روش اولویت محور صورت گرفته است. درنهایت عملکرد این الگوریتم ازلحاظ کیفت جواب‌ها و سرعت همگرایی با دو الگوریتم فراابتکاری دیگر مقایسه می‌شود.

کلیدواژه‌ها


عنوان مقاله [English]

PET Supply Chain Network design: A Metaheuristic Algorithm with a modified representation

نویسندگان [English]

  • Ehsan Yadegari 1
  • Akbar Alemtabriz 2
  • Mostafa Zandieh 2
  • Fariba Salahi 3
  • Amir Daneshvar 4
1 Postdoctoral fellow, Henry FAYOL Center, Department of Mathematical and industrial engineering, École des mines, 158 cours Fauriel, Saint-Etienne France.
2 Professor, Department of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.
3 Assistant Professor, Department of Industrial Management, Faculty of Management, Electronic Branch, Islamic Azad University, Tehran, Iran.
4 Assistant Professor, Department of Industrial Management, Faculty of Management, Electronic Unit, Islamic Azad University, Tehran, Iran
چکیده [English]

In Iran, Although the PET recycling industry is new, due to the appropriate economic return, many individuals and companies have turned to this industry. Creating a proper structure in the distribution and collection network of these products can provide a good base to replace its consumption with more environmentally friendly products. This paper aims to present a mixed integer linear programming (MILP) model for designing a supply chain network in which customer demand is dependent on the price offered by distribution centers. The proposed model addresses two main global trends in this industry: 1- Economizing the collection and recycling of bottles, 2- Price sensitivity analysis on demand for PET bottles.
In addition, we applied a recently developed optimization algorithm (TLBO) to this problem with significant modifications. Since the standard version of TLBO is introduced for continuous representation methods and the representation method used in this paper (priority-based encoding) is and discrete method, modifications have been made to convert this method to a continuous one. Finally, the performance of this algorithm is assessed in terms of the quality of answers and the convergence speed and compared with two other meta-heuristic algorithms.

کلیدواژه‌ها [English]

  • Teaching-learning based optimization
  • Logistics
  • Linear Programming
  • Price-dependent demand
[1]      Jayaraman, V. and H. Pirkul, Planning and coordination of production and distribution facilities for multiple commodities. European journal of operational research, 2001. 133(2): p. 394-408.
[2]      Üster, H., et al., Benders decomposition with alternative multiple cuts for a multi‐product closed‐loop supply chain network design model. Naval Research Logistics (NRL), 2007. 54(8): p. 890-907.
[3]      Lee, D.-H. and M. Dong, Dynamic network design for reverse logistics operations under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 2009. 45(1): p. 61-71.
[4]      Yadegari, E., M. Zandieh, and H. Najmi, A hybrid spanning tree-based genetic/simulated annealing algorithm for a closed-loop logistics network design problem. International Journal of Applied Decision Sciences, 2015. 8(4): p. 400-426.
[5]      Kazemi, A. and F. Sarvandi, Mathematical Modeling of Resource-Constrained Project Scheduling Problem and Solving It by Using Metaheuristic Algorithms. Modern Research in Decision Making, 2019. 3(4): p. 28-50.
[6]      Govindan, K., H. Soleimani, and D. Kannan, Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 2015. 240(3): p. 603-626.
[7]      Govindan, K., M. Fattahi, and E. Keyvanshokooh, Supply chain network design under uncertainty: A comprehensive review and future research directions. European Journal of Operational Research, 2017.
[8]      Ahmadi, E., M.H. Maleki, and M.R. Fathi, Presenting a model for future study of supply chain in oil industry with soft approach. Management Research in Iran, 2020. 24(1): p. 59-79.
[9]      Karimi, T., Service Supply Chain Risk Assessment Applying Rough Set Theory Approach: Case of Payment Service Providers. Management Research in Iran, 2018. 22(1): p. 69-94.
[10]   Teng, J.-T., et al., An optimal replenishment policy for deteriorating items with time-varying demand and partial backlogging. Operations Research Letters, 2002. 30(6): p. 387-393.
[11]    Wee, H.-M. and S.-T. Law, Economic production lot size for deteriorating items taking account of the time-value of money. Computers & Operations Research, 1999. 26(6): p. 545-558.
[12]   Valliathal, M. and R. Uthayakumar, An EOQ model for rebate value and selling-price-dependent demand rate with shortages. International Journal of Mathematics in Operational Research, 2011. 3(1): p. 99-123.
[13]   Maihami, R. and I. Nakhai Kamalabadi, Joint pricing and inventory control for non-instantaneous deteriorating items with partial backlogging and time and price dependent demand. International Journal of Production Economics, 2012. 136(1): p. 116-122.
[14]   Hill, R.M., Inventory models for increasing demand followed by level demand. Journal of the Operational Research Society, 1995: p. 1250-1259.
[15]   Skouri, K., et al., Inventory models with ramp type demand rate, partial backlogging and Weibull deterioration rate. European Journal of Operational Research, 2009. 192(1): p. 79-92.
[16]   Tua, C., et al., Life cycle assessment of reusable plastic crates (RPCs). Resources, 2019. 8(2): p. 110.
[17]   Li, J., J. Chen, and S. Wang, Introduction, in Risk Management of Supply and Cash Flows in Supply Chains. 2011, Springer. p. 1-48.
[18]   Tsiakis, P. and L.G. Papageorgiou, Optimal production allocation and distribution supply chain networks. International Journal of Production Economics, 2008. 111(2): p. 468-483.
[19]   Fattahi, M., M. Mahootchi, and S.M. Husseini, Integrated strategic and tactical supply chain planning with price-sensitive demands. Annals of operations research, 2016. 242(2): p. 423-456.
[20]   Min, H. and H.-J. Ko, The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers. International Journal of Production Economics, 2008. 113(1): p. 176-192.
[21]   Aras, G. and D. Crowther, Governance and sustainability: An investigation into the relationship between corporate governance and corporate sustainability. Management Decision, 2008. 46(3): p. 433-448.
[22]   Syarif, I., A. Prugel-Bennett, and G. Wills. Unsupervised clustering approach for network anomaly detection. in International Conference on Networked Digital Technologies. 2012. Springer.
[23]   Wang, H.-F. and H.-W. Hsu, A closed-loop logistic model with a spanning-tree based genetic algorithm. Computers & operations research, 2010. 37(2): p. 376-389.
[24]   Devika, K., A. Jafarian, and V. Nourbakhsh, Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques. European Journal of Operational Research, 2014. 235(3): p. 594-615.
[25]   Coit, D.W., Genetic Algorithms and Engineering Design. THE ENGINEERING ECONOMIST, 1998. 43(4): p. 379-381.
[26]   Pishvaee, M.S., R.Z. Farahani, and W. Dullaert, A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computers & Operations Research, 2010. 37(6): p. 1100-1112.
[27]   ayough, a., Intra- and inter-Serus job rotation scheduling through Teaching and Learning Based Optimization approach. Modern Research in Decision Making, 2019. 3(4): p. 153-175.
[28]   Tang, Q., et al., Balancing stochastic two-sided assembly line with multiple constraints using hybrid teaching-learning-based optimization algorithm. Computers & Operations Research, 2017. 82: p. 102-113.
[29]   Yu, K., et al., Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization. Energy Conversion and Management, 2017. 145: p. 233-246.
[30]   Chen, X., et al., Parameters identification of solar cell models using generalized oppositional teaching learning based optimization. Energy, 2016. 99: p. 170-180.
[31]   Sahu, B.K., et al., A novel hybrid LUS–TLBO optimized fuzzy-PID controller for load frequency control of multi-source power system. International Journal of Electrical Power & Energy Systems, 2016. 74: p. 58-69.