مدل چند هدفه فازی شبکه زنجیره تامین حلقه بسته در صنعت خودرو با رویکرد مدیریت شهری
محورهای موضوعی : مطالعات مدیریت شهری
سعید امین پور
1
(دانشجوی دکتری مدیریت صنعتی، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران.
(نویسنده مسئول) aminpour0saeed@gmail.com)
علیرضا ایرج پور
2
(استادیار گروه مدیریت صنعتی ، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران)
مهدی یزدانی
3
(استادیار گروه مهندسی صنایع ، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران)
کلید واژه: شبکه زنجیره تامین حلقه بسته, صنعت خودرو, بازده انرژی و زمان, مدیریت شهری,
چکیده مقاله :
مقدمه و هدف پژوهش: هدف تحقیق حاضر طراحی مدل چند هدفه فازی شبکه زنجیره تامین حلقه بسته در صنعت خودرو با رویکرد مدیریت شهری می‌باشد. تولید خودرو فرآیندی پیچیده و پرانرژی است که مقدار قابل توجهی مواد اولیه و آب را مصرف می‌کند. برای ادامه رقابت، تولیدکنندگان تجهیزات اصلی خودرو باید با بهبود مستمر روند تولید خود و هدایت تولید گازهای گلخانه ای با میزان کم کربن و افزایش پایداری، برای کیفیت بهتر محصول تلاش کنند. در همین راستا شبکه های زنجیره تامین معکوس و زنجیره های حلقه بسته دارای ویژگی های خاصی هستند که در صنعت مورد بررسی بسیار مفید می‌باشد. روش پژوهش: در تحقیق حاضر به منظور رسیدن به اهداف تحقیق از روش تحقیق کمی استفاده خواهد شد و براساس هدف به صورت کاربردی تعریف شده است. در این مطالعه، از روش MOPSO برای تسهیل اجرای آن و توانایی آن در ارائه هم‌گرایی خوب و همچنین الگوریتم ژنتیک NSGA II استفاده می‌کنیم. یافته‌ها: در بررسی یافته های الگوریتم های پیشنهادی مشخص شد که میانگین خطای حاصل از این الگوریتم‌ها کمتر از 04/0 است. همچنین نتایج نشان می‌دهد که الگوریتم های پیشنهادی کارایی لازم را در حل این مسائل دارند نتیجه گیری:نتایج قابل توجه مدل خود را چنین ذکر می‌کنیم: (1) شبکه ی حلقه بسته ی کارآمدی که مزایای اقتصادی با توجه به در نظر گرفتن ارزش زمان را با توجه به بازیافت محصول فرسوده نشان می‌دهد. (2) این توانایی را دارد که ظرفیت را برای دستیابی به حداکثر مزایا از نظر ارزش هزینه و نیز چشم انداز محیطی نشان دهد که چه ظرفیتی را باید حفظ کند.
Introduction & Objective: The purpose of this study is to design a multi-objective model of fuzzy closed-loop supply chain network in the automotive industry with an urban management approach. Automobile manufacturing is a complex and energetic process that consumes significant amounts of raw materials and water. To continue the competition, major automotive equipment manufacturers must strive for better product quality by continuously improving their production process and directing low-carbon emissions and increasing sustainability. In this regard, reverse supply chain networks and closed loop chains have special features that are very useful in the industry under study. Method: In the present study, in order to achieve the research objectives, a quantitative research method will be used and based on the purpose, it is defined in a practical way. In this study, we use the MOPSO method to facilitate its implementation and its ability to provide good convergence, as well as to maintain a proper balance between exploitation and exploration, as well as the NSGA II genetic algorithm. Results: In the study of the findings of the proposed algorithms, it found that the average error resulting from these algorithms is less than 0.04. The results also show that the proposed algorithms have the necessary efficiency in solving these problems. Conclusion: The notable results of our model are as follows: (1) an efficient closed-loop network that demonstrates the economic benefits of considering the value of time over the recycling of a worn product. (2) It has the ability to show the capacity to achieve maximum benefits in terms of cost value as well as the environmental perspective of what capacity it should maintain.
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Alomar, M., & Pasek, z. J. (2014). Linking supply chain strategy and processes to performance improvement Variety Management in Manufacturing. Proceedings of the 47th CIRP Conference on Manufacturing Systems, 17, 6. doi:https://doi.org/10.1016/j.procir.2014.01.144
Amin, S. H., & Baki, F. (2017). A facility location model for global closed-loop supply chain network design. Applied Mathematical Modelling, 41. doi:http://dx.doi.org/10.1016/j.apm.2016.08.030
Armin Jabbarzadeh , M. H., Amir Khosrojerdi. (2018). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers & Industrial Engineering, 118, 178. doi:https://doi.org/10.1016/j.cie.2017.12.025
Ashfari, H., Sharifi, M., ElMekkawy, T. Y., & Peng, Q. (2014). Facility Location Decisions Within Integrated Forward/Reverse Logistics under Uncertainty. Procedia CIRP, 17, 5. doi: 10.1016/j.procir.2014.01.092
Barbosa-Póvoa, A. P., Silva, C., & Carvalho, A. (2018). Opportunities and challenges in sustainable supply chain: An operations research perspective. European Journal of Operational Research, 268(2), 32. doi:https://doi.org/10.1016/j.ejor.2017.10.036
Bavarsad, Belqis; Rezaei Jarivand, Batool; Rahimi, Farajollah; Sinaei, Hassan Ali (2016) "Study of the Alignment of Supply Chain Strategies and Information System Strategies on Supply Chain Management Performance (Case Study: Manufacturing Companies in Khuzestan Province)" Journal of Technology Management Tehran University Information, Winter 2016, No. 29 (In Persian)
Cai, X., Gao, L., & Li, F. (2019). Sequential approximation optimization assisted particle swarm optimization for expensive problems. Applied Soft Computing Journal, 83. doi:https://doi.org/10.1016/j.asoc.2019.105659
Chen, Y.-W., Wang, L.-C., Wang, A., & Chen, T.-L. (2017). A particle swarm approach for optimizing a multi-stage closed loop supply chain for the solar cell industry. Robotics andComputer-Integrated Manufacturing, 43, 12. doi:http://dx.doi.org/10.1016/j.rcim.2015.10.006
Dekker, R., Fleischmann, M., & Inderfurth, K. (2004). Quantitative Models for Closed-Loop Supply Chains: Springer-Verlag Berlin Heidelberg.
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 SetsandSystems, 3, 23. doi:http://dx.doi.org/10.1016/j.fss.2017.03.019
Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., & Mirjalili, S. (2018). Multi-objective stochastic closed-loop supply chain network designwith social considerations. Applied Soft Computing, 71, 20. doi:https://doi.org/10.1016/j.asoc.2018.07.025
Fallah, H., Eskandari, H., Zgardi, S. Chaharsooqi, S. (1396). Presenting a two-level model of closed-loop supply chain network design in conditions of uncertainty and inter-chain competition: a solution with the Banders analysis approach. Journal of Modeling in Engineering, 49, 15. (In Persian
Fleischmann, M., Beullens, P., Bloemhof-Ruwaard, J., & Wassenhove, L. (2001). The impact of product recovery on logistics network design. Production and Operations Management,
Giovanni, P. D. (2018). A joint maximization incentive in closed-loop supply chains with competing retailers: The case of spent-battery recycling. European Journal of Operational Research, 1. doi:10.1016/j.ejor.2018.01.003
Haddadsisakht, A., & Ryan, S. M. (2018). Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax. International Journal of Production Economics, 195, 13. doi:https://doi.org/10.1016/j.ijpe.2017.09.009
Hajian, Sima; Afshar Kazemi, Mohammad Ali; Seyed Hosseini, Seyed Mohammad; Tolouei Ashlaghi, Abbas (2019), Presenting a Multi-Objective Model for the Problem of Location-Routing-Inventory in the Multi-Periodic Green Ring Supply Chain Network and Multi-Product for Perishable Goods, Industrial Management, 11 (1), 83 - 110(In Persian)
Hassanzadeh, A. S., & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37, 12. doi:http://dx.doi.org/10.1016/j.apm.2012.09.039
Hsiao-Fan Wang , H.-W. (2010). A closed-looplogisticmodelwithaspanning-treebasedgeneticalgorithm. Computers &OperationsResearch, 37, 13. doi:10.1016/j.cor.2009.06.001
Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018a). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers & Industerial Engineering, 116, 13. doi:https://doi.org/10.1016/j.cie.2017.12.025
Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018b). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers & Industrial Engineering, 116, 14. doi:https://doi.org/10.1016/j.cie.2017.12.025
Jawad, H., Jaber, M., & Nuwayhid, R. (2018). Improving supply chain sustainability using exergy analysis. European Journal of Operational Research, 269, 13. doi:https://doi.org/10.1016/j.ejor.2017.10.007
Kadambala, D. K., Subramanian , N., Tiwari , M. K., Abdulrahman , M., & Liu, C. (2017). Closed loop supply chain networks: Designs for energy and time value efficiency. Int. J.ProductionEconomics, 183, 382. doi:http://dx.doi.org/10.1016/j.ijpe.2016.02.004
Kaya, O., & Urek, B. (2016). A mixed integer nonlinearprogramming model and heuristic solutions for location , inventory and pricing decisions in a closed loop supply chain. Computers &OperationsResearch, 65, 10.
Liao, S.-H., Hu, D.-C., & Ding, L.-W. (2017). Assessing the influence of supply chain collaboration value innovation, supply chain capability and competitive advantage in Taiwan's networking communication industry. International Journal of Production Economics, 191, 10.
Lu, Z., & Bostel, N. (2007). A facility location model for logistics systems including reverse flows: The case of remanufacturing activities. Computers & Operations Research, 34, 25.
Özceylan, E., Demirel, N., Çetinkaya, C., & Demirel, E. (2017). A closed-loop supply chain network design for automotive industry in Turkey. Computers & Industrial Engineering, 113,
Nambajemariya, F. and Wang, Y. (2021) Excavation of the Internet of Things in Urban Areas Based on an Intelligent Transportation Management System. Advances in Internet of Things, 11, 113-122. doi: 10.4236/ait.2021.113008.
Pishvaee, M. S., Kianfar, K., & Karimi, B.. (2010). Reverse logistics network design using simulated annealing. The International Journal of Advanced Manufacturing Technology, 47, 12. doi:https://doi.org/10.1007/s00170-009-2194-5
Shakourloo, A., Kazemi, A., & Javad, M. O. M. (2016). A new model for more effective supplier selection and remanufacturing process in a closed-loop supply chain. Applied Mathematical Modelling, 40, 17. doi:http://dx.doi.org/10.1016/j.apm.2016.06.039
Soleimani, H., Govindan, K., Saghafi, H., & Jafari, H. (2017). Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Computers & Industrial Engineering, 109, 191. doi:http://dx.doi.org/10.1016/j.cie.2017.04.038
Taleizadeh, A. A., Haghighi, F., & Niaki, S. T. A. (2019). Modeling and solving a sustainable closed loop supply chain problem with pricing decisions and discounts on returned products. Journal of Cleaner Production, 207, 18. doi:https://doi.org/10.1016/j.jclepro.2018.09.198
Ekhtiari, M., Zandieh, M., Alem Tabriz, A., & Rabieh, M. (2019). Proposing a Bi-level Programming Model for Multi-echelon Supply Chain with an Emphasis on Reliability in Uncertainty. Industrial Management Journal, 11(2), 177-206. (in Persian)
Giampieri, Alessandro; Ling-Chin, Janie; Ma, Zhiwei; Smallbone, Andrew; Roskilly, Antony (2020) “A review of the current automotive manufacturing practice from an energy perspective” Applied Energy 261 (2020) 114074
Jabbarzadeh, Armin; Haughton, Michael; Khosrojerdi, Amir (2018) “Closed-loop supply chain network design under disruption risks: A robust approach with real world application” Computers & Industerial Engineering, Volume 116 , 178- 191 ,
Soleimani, Hamed; Govindan Kannan (2015) “A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks” Applied Mathematical Modelling, Volume 39, 3990-4010,
Tarokh, J., Mahsa, A. G., & Shohreh, T. (2014). General model for optimizing reverse logistics network design under uncertainty. Specialized Journal of Industrial Engineering, 2, 15. (In Persian)
Yavari, Mohammad., Geraeli, Mohaddase., 2019. Heuristic method for robust optimization model for green closed-loop supply chain network design of perishable goods. J. Clean. Prod. 226, 282e305
Zhou, X., & Zhou, Y. (2015). Designing a multi-echelon reverse logistics operation and network:A case study of office paper in Beijing. Resources, Conservation and Recycling, 100, 12. doi:http://dx.doi.org/10.1016/j.resconrec.2015.04.009
