بهینه‌سازی چند هدفه زنجیره تأمین حلقه بسته سبز با تأکید بر عوامل انرژی در حمل‌ونقل

نوع مقاله : علمی - پژوهشی

نویسندگان
محسن شفیعی نیک ابادی 1
مهسا اخوان راد 2
پریسا دهقان پور 3
1 دانشیار.گروه مدیریت صنعتی، دانشکده اقتصاد و مدیریت، دانشگاه سمنان، سمنان، ایران
2 دانشجوی دکتری مدیریت صنعتی، دانشکده علوم اقتصادی اداری، دانشگاه فردوسی مشهد، ایران
3 کارشناسی ارشد مدیریت صنعتی، موسسه آموزش عالی بینالود مشهد، مشهد، ایران
10.22119/jte.2023.321377.2601
چکیده
امروزه دستیابی به منافع رقابتی بازار، امری ضروری است و ازآنجاکه توجه به مسائل زیست‌محیطی و انرژی افزایش‌یافته است، طراحی شبکه‌ی زنجیره تأمین مناسب می‌تواند کمک بزرگی به این مهم کند. به همین منظور در این پژوهش یک مدل برنامه ریزی برای زنجیره تأمین حلقه بسته‌ی سبز چندمحصولی، چند دوره‌ای طراحی‌شده است. جنبه‌ی نوآوری تحقیق برقراری توابع هدف چندگانه اختصاصی در حوزه‌ی مدیریت انرژی مبتنی بر حمل و نقل می‌باشد که یک مدل بهینه‌سازی برای زنجیره تأمین حلقه بسته سبز با چهار هدف بهینه‌سازی کربن‌دی‌اکسید، هزینه حمل‌ونقل، انرژی و ضایعات طراحی‌شده است و مدل مربوطه توسط الگوریتم‌های فرا ابتکاری ژنتیک و شبیه‌سازی تبرید حل گردیده است. در راستای یکپارچه‌سازی توابع هدف از روش وزن دهی به اهداف استفاده‌شده است، وزن اهداف با توجه به نظر خبرگان انتخاب‌شده است. نتایج حاصل از بررسی کارایی الگوریتم‌ها نشان می‌دهد زمانی که چهار تابع هدف را به‌صورت هم‌زمان در نظر می‌گیریم، الگوریتم ژنتیک بهتر عمل می‌کند همچنین با مقایسه ی نتایج حاصل از این دو الگوریتم کارایی الگوریتم ژنتیک برای دو تابع هدف انرژی و هزینه ثابت شد و کارایی الگوریتم شبیه سازی تبرید برای دو تابع هدف کربن دی اکسید و ضایعات مشخص گردید. این مقاله کمک می‌کند تا مدیران از حمل‌ونقل سبز و بهبود عملکرد زیست‌محیطی و کاهش هزینه‌ها در کل زنجیره تأمین به‌عنوان یک استراتژی مکمل، به‌منظور کسب مزیت رقابتی پایدار سود ببرند.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

A Multi-Objective Optimization Model for Closed Loop Supply Chain with Emphasis on Energy Factors in Transportation

نویسندگان English

mohsen shafiei nik abadi 1
mahsa akhavan rad 2
parisa dehghanpoor 3
1 Associate Professor .Department Of Industrial Management, Semnan University, Semnan, Iran
2 Phd Student In Industrial Management, Ferdowsi University Of Mashhad, Iran
3 M.Sc.Managment Department.Binalood Higher Education Institue.Mashhad. Iran
چکیده English

Today, it is essential to achieve competitive market interests and since attention to environmental issues and reduced raw resources and energy has increased, design of a suitable supply chain network can be a great help in this regard. To this end, in this study, a multi-product closed-loop green supply chain has been designed. The innovative aspect of the research is the establishment of specific multiple objective functions in the field of transportation-based energy management, which is an optimization model for the green closed loop supply chain with four optimization objectives of carbon dioxide, transportation cost, energy, and waste, and the relevant model is designed by innovative genetic algorithms. And the refrigeration simulation is solved. In order to integrate the target functions, the weighting method of the targets has been used, the weight of the targets has been chosen according to the experts' opinion. The results of the algorithms' efficiency show that when we consider the four objective functions at the same time, the genetic algorithm performs better. Also, by comparing the results of these two algorithms, the efficiency of the genetic algorithm for the two objective functions of energy and cost was proven, and the efficiency of the refrigeration simulation algorithm was determined for the two objective functions of carbon dioxide and waste. This paper helps managers to benefit from green transportation and improving environmental performance and reducing costs in the entire supply chain as a complementary strategy in order to gain a sustainable competitive advantage.

کلیدواژه‌ها English

refrigeration simulation algorithm
genetic algorithm
transportation optimization
green closed-loop supply chain
mathematical programming model
  • Rabieh M., Azar A., Modarres Yazdi M., Fetanat Fard Haghighi M. (2011) “Desigening a multi-objective robust multi-sourcing mathematical model, An approach for reducing the risk of supply chain (Case study: Supply Chan of IRAN KHODRO COMPANY)”; Indstrial managements Perspective, 1: 57-77, (in Persian). http://journal.saim.ir/article_26787_en.html

 

  • Saneyi, M. Tavakoli Moghaddam,R.(2013).Bi-Objective Mathematical Modeling for a Closed-Loop Supply Chain Network with Risk-Pooling and Uncertain Demands. Iranian Journal of Supply Chain Management, 2014; 16(43): 4-15.

 

  • Govindan K, Soleimani H, Kannan D, (2015), Reverse logistics and closed-loop supply chain: a comprehensive review to explore the future. Eur. J. Oper. Res. 240, 603–626.

 

  • Altiparmak,F., Gen,M., Lin,L., Karaoglan,I.( 2009), “A steady-state genetic algorithm for multi-product supply chain network design”, Computers & Industrial Engineering, 56, 521–537. https://doi.org/10.1016/j.cie.2007.05.012

 

  • Beheshti Nia, M., & Khatibi, S. (2017). Analyzing of Three Different Scenario’s To Optimize Energy Consumption and Scheduling in Supply Chain. Journal of Energy Management, 7(1), 36-47.

 

  • Zhang H.C, Kuo T.C, Lu H, Huang S.H, (1997), Environmentally conscious design and manufacturing; a state-of-the-art survey, Journal of Manufacturing Systems 16 (5).

 

  • Linton, J.D., Klassen, R. & Jayaraman, V. (2007). “Sustainable supply chains: an introduction”. Journal of Operations Management, 25(1): 1075-82. https://doi.org/10.1016/j.jom.2007.01.012

 

  • Srivastara S.K, (2007), Green supply-chain management: a state-of-the-art literature review. International Journal of Management Reviews 9 (1), 53–80.

 

  • Sundarakani B, De Souza R, Goh M, Shun C, (2008), Measuring carbon footprints across the supply chain. In; Proceedings of the 13th International Symposium on Logistics, July 2008, Bangkok, Thailand, pp. 555–562.

 

 

  • Beamon, B. (1999). Designing the green supply chain. Logistics Information Management, 12(4), 332-342. http://dx.doi.org/10.1108/09576059910284159
  • Jamshidi S., Boyerhassani Omid. Development of A Mathematical Model for Facility Location in Green Closed-Loop Supply Chain with Learning Effect. Journal of Transportation Research. 2019 [Cited 2021april24]; 16(2 (59)):91-105.

 

  • Fakhrzad, M., Talebzadeh, P., Goodarzian, F. (2019). The green Closed-Loop Supply Chain Network Design Considering Supply Centers Reliability under Uncertainty. Journal of Industrial Engineering Research in Production Systems, 7(14), 179-197.

 

  • Hajian, S., & Afshar Kazemi, M., & Seyed Hosseini, S., & Toloie Eshlaghy, A. (2019). Developing A Multi-Objective Model for Locating-Routing-Inventory Problem in A Multi-Period and Multi-Product Green Closed-Loop Supply Chain Network for Perishable Products. Journal of Industrial Management (Management Knowledge), 11(1), 83-110.

 

  • Shafiei Nikabadi, M., Molayi, E., Aakhavan rad, M. (2021). Optimization of Vehicle Routing Problem under Uncertainty with emphasis on Green - Lean Practices and Customer Satisfaction. Journal of Transportation Research, 18(1), 113-134. doi: 10.22034/tri.2021.82820

 

  • Guo, Yurong, Quan Shi, and Chiming Guo. 2022. "A Fuzzy Robust Programming Model for Sustainable Closed-Loop Supply Chain Network Design with Efficiency-Oriented Multi-Objective Optimization" Processes 10, no. 10: 1963.

 

  • Kaoud, Essam, Mohammad A. M. Abdel-Aal, Tatsuhiko Sakaguchi, and Naoki Uchiyama. 2022. "Robust Optimization for a Bi-Objective Green Closed-Loop Supply Chain with Heterogeneous Transportation System and Presorting Consideration" Sustainability 14, no. 16: 10281. https://doi.org/10.3390/su14161028Soleimani

 

 

  • Modak, N. M., Modak, N., Panda, S., & Sana, S. S. (2018). "Analyzing structure of two-echelon closed-loop supply chain for pricing, quality and recycling management". Journal of Cleaner Production, 171, 512–528.

 

  • Taleizadeh, A. A., Moshtagh, M. S., & Moon, I. (2018). "Pricing, product quality, and collection optimization in a decentralized closedloop supply chain with different channel structures: Game theoretical approach". Journal of Cleaner Production, 189, 406–431. https://doi.org/10.1016/J.JCLEPRO.2018.02.209.

 

  • Ghomi-avili, M., Gholamreza, S., Naeini, J., Tavakkoli-moghaddam, R., & Jabbarzadeh, A. (2018)." A fuzzy pricing model for a green competitive closed-loop supply chain network design in the presence of disruptions". Journal of Cleaner Production, 188, 425–442.

 

  • 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(October 2017), 118–131. https://doi.org/10.1016/j.ijpe.2017.09.009.

 

  • Fathollahi-fard, A. M., & Hajiaghaei-keshteli, M. (2018). "A stochastic multi-objective model for a closed-loop supply chain with environmental considerations". Applied Soft Computing Journal, 69, 232–249.

 

  • Mohtashami, Z., Aghsami, A., & Jolai, F. (2019). A green closed loop supply chain design using queuing system for reducing environmental impact and energy consumption. Journal of Cleaner Production, 118452.

 

  • Hasani, A., Mokhtari, H., & Fattahi, M. (2021). A multi-objective optimization approach for green and resilient supply chain network design: A real-life Case Study. Journal of Cleaner Production, 123199. doi:10.1016/j.jclepro.2020.123199

 

  • Alam Tabriz, A. (2006), Meta-heuristic algorithms in Combination Optimization, Saffar publishing.

 

 

  • Tarokh, M., EsmaeiliGookeh, M., Torabi, S. (2012). A Model to Optimize the Design of a Reverse Logistic Network under Uncertainty. Advances in Industrial Engineering, 46(2), 159-173. doi: 10.22059/jieng.2012.30559

 

  • Altiparmak,F., Gen,M., Lin,L., Karaoglan,I.( 2009), “A steady-state genetic algorithm for multi-product supply chain network design”, Computers & Industrial Engineering, 56, 521–537. https://doi.org/10.1016/j.cie.2007.05.012

 

  • Mousavi S. M & Niaki S, T. A, (2013), Capacitated location allocation problem with stochastic location and fuzzy demand: A hybrid algorithm, Applied Mathematical Modelling, 37(7), 5109–5119 https://doi.org/10.1016/j.apm.2012.10.038

 

  • Durand D, and white S.R, (2000), Trading accuracy for speed in parallel simulated annealing with simultaneous moves, Elsevier parallel somputing, vol26, pp 135-150.

  • تاریخ دریافت 27 اردیبهشت 1401
  • تاریخ بازنگری 09 بهمن 1401
  • تاریخ پذیرش 10 فروردین 1402