طراحی شبکه زنجیره تامین حلقه بسته ماهیان پرورشی سردآبی با در نظر گرفتن شرایط عدم قطعیت: مطالعه موردی شبکه زنجیره تامین قزل آلا در مازندران

فصیحی, مائده; نجفی, سید اسماعیل; توکلی مقدم, رضا; حاجی آقائی کشتلی, مصطفی

doi:10.22054/jims.2021.60904.2657

نوع مقاله : مقاله پژوهشی

نویسندگان

¹ دانشجوی دکتری، گروه مهندسی صنایع، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

² استادیار، گروه مهندسی صنایع، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

³ استاد دانشکده مهندسی صنایع، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران

⁴ استادیار گروه مهندسی صنایع، دانشگاه علم و فناوری مازندران، بهشهر، ایران

https://doi.org/10.22054/jims.2021.60904.2657

چکیده

در اقتصاد رقابتی کنونی، مدیریت زنجیره تامین امری مهم تلقی می‌گردد. در سال‌های اخیر به دلیل کمبود منابع جهت پاسخگویی به تقاضای فزاینده غذا، توجه محققان به زنجیره تأمین مواد غذایی افزایش یافته است. از آنجاییکه ماهی یکی از اقلام مطلوب در سبد غذایی خانوار است، توسعه آبزی‌پروری و بازیافت محصولات برگشتی در لجستیک معکوس به حفظ منابع آبی و توسعه پایدار کمک قابل ملاحظه‌ای می‌نماید. از این‌رو نهادهای دولتی و همچنین ذینفعان صنعت آبزی‌پروری علاقه‌مند به لجستیک معکوس هستند. مطالعه حاضر بر روی بهینه‌سازی یک زنجیره تامین حلقه بسته ماهی متمرکز شده است. بدین منظور، در ابتدا یک مدل ریاضی دو هدفه ارائه شده است که علاوه بر به حداقل رساندن هزینه‌ها، به حداکثر رساندن پاسخگویی به تقاضای مشتریان در شرایط عدم قطعیت را نیز درنظر می‌گیرد. چندین الگوریتم فراابتکاری چند‌هدفه شناخته شده و یک الگوریتم فراابتکاری تلفیقی پیشنهادی برای یافتن راه‌حل های پارتو بکار گرفته شده‌اند و راه حل‌ها از نظر معیارهای عملکرد مقایسه گردیده‌اند. همچنین روش اپسیلون-محدودیت و تجزیه و تحلیل حساسیت جهت اعتبارسنجی الگوریتم‌ها و ارزیابی کارایی مدل بکارگرفته شده‌اند. سرانجام، از الگوریتم ویکور برای انتخاب روش حل برتر استفاده شده است. به منظور نشان دادن قابلیت مدل پیشنهادی، یک مطالعه موردی شامل زنجیره تامین حلقه بسته ماهی قزل آلا در شمال ایران بررسی گردیده است. نتایج حاصل از بررسی مسائل نشان می‌دهند که مدل توسعه یافته می‌تواند جهت صرفه جویی در هزینه‌ها و بالابردن سطح رضایت مشتریان موثر باشد.

کلیدواژه‌ها

موضوعات

مدیریت زنجیره تامین

عنوان مقاله [English]

Cold-water farmed fish chain supply chain network design considering uncertainty conditions: A case study of trout supply chain network in Mazandaran

نویسندگان [English]

Maedeh Fasihi ¹
Seyed Esmaeil Najafi ²
Reza Tavakkoli-Moghaddam ³
Mostafa Hahiaghaei-Keshteli ⁴

¹ Ph.D. Student, Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

² Assistant Professor, Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

³ Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

⁴ Assistant Professor, Department of Industrial Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran

چکیده [English]

The supply chain management is an important factor in current competitive market. In recent years, the shortage of resources for answering an increasing food demand has increased researchers’ attention to the food supply chain. Given the importance of fish in the Household Food Basket, the development of aquaculture and recycling of returned goods in reverse logistics would significantly help with preserving water resources, as well as sustainable development. Therefore, government agencies and aquaculture industry beneficiaries are interested in reverse logistics. This study is focused on the optimization of a closed-loop supply chain of fish. To this end, a new bi-objective mathematical model is proposed that both minimizes total costs and maximizes fulfilling customers demand in uncertainty situation. Several well-known multi-objective meta-heuristic algorithms and a proposed hybrid meta-heuristic algorithm are applied to identify Pareto solutions. The solutions are then compared in terms of performance metrics. Also, the epsilon-constraint method and sensitivity analysis are used to validate the algorithms and evaluate the performance of the model. Lastly, the VIKOR method is used to select the superior method. To demonstrate the capability of the proposed model, a closed-loop supply chain of trout in northern Iran is investigated as a case study. The results show that the developed model could be effective in reducing the costs and increasing customer satisfaction.

کلیدواژه‌ها [English]

Closed-loop supply chain
Fish reverse logistics
Bi-objective mathematical model
uncertainty
Meta-heuristic algorithms

مراجع

پارسائیان، سمیرا. امیری، مقصود. عظیمی، پرهام. تقوی فرد، محمدتقی. (1398). »طراحی مدل شبیه سازی زنجیره تامین حلقه بسته سبز و قیمت گذاری محصول در حضور رقیب«. مجله مطالعات مدیریت صنعتی، دوره 17، شماره 52، 202-153.

References

Abdi, A., Abdi, A., Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2021). A set of calibrated metaheuristics to address a closed-loop supply chain network design problem under uncertainty. International Journal of Systems Science: Operations & Logistics, 8(1), 23-40.

Abedi, A., & Zhu, W. (2017). An optimisation model for purchase, production and distribution in fish supply chain–a case study. International Journal of Production Research, 55(12), 3451-3464.

Altiparmak, F., Gen, M., Lin, L., & Paksoy, T. (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers & industrial engineering, 51(1), 196-215.

Amini, M., Bienstock, C. C., & Golias, M. (2020). Management of supply chains with attribute-sensitive products: a comprehensive literature review and future research agenda. The International Journal of Logistics Management.

Bensalem, A., & Kin, V. (2019, January). A bibliometric analysis of reverse logistics from 1992 to 2017. In Supply Chain Forum: An International Journal (Vol. 20, No. 1, pp. 15-28). Taylor & Francis.

Chan, F. T., Wang, Z. X., Goswami, A., Singhania, A., & Tiwari, M. K. (2020). Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations. International Journal of Production Research, 1-20.

Chaudhuri, A., Dukovska-Popovska, I., Subramanian, N., Chan, H. K., &Bai, R. (2018). Decision-making in cold chain logistics using data analytics: a literature review. The International Journal of Logistics Management.

Cheraghalipour, A., Paydar, M. M., & Hajiaghaei-Keshteli, M. (2018). A bi-objective optimization for citrus closed-loop supply chain using Pareto-based algorithms. Applied Soft Computing, 69, 33-59.

Dania, W. A. P., Xing, K., & Amer, Y. (2018). Collaboration behavioural factors for sustainable agri-food supply chains: A systematic review. Journal of Cleaner Production, 186, 851-864.

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.

Deng, W., Zhao, H., Zou, L., Li, G., Yang, X., & Wu, D. (2017). A novel collaborative optimization algorithm in solving complex optimization problems. Soft Computing, 21(15), 4387-4398.

Eskandarpour, M., Dejax, P., Miemczyk, J., & Péton, O. (2015). Sustainable supply chain network design: An optimization-oriented review. Omega, 54, 11-32.

Farahani, R. Z., Rezapour, S., Drezner, T., & Fallah, S. (2014). Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega, 45, 92-118.

Fathollahi-Fard, A. M., Ahmadi, A., & Al-e-Hashem, S. M. (2020a). Sustainable closed-loop supply chain network for an integrated water supply and wastewater collection system under uncertainty. Journal of Environmental Management, 275, 111277.

Fathollahi-Fard, A. M., Ahmadi, A., Goodarzian, F., & Cheikhrouhou, N. (2020b). A bi-objective home healthcare routing and scheduling problem considering patients’ satisfaction in a fuzzy environment. Applied soft computing, 93, 106385.

Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., & Mirjalili, S. (2020c). A set of efficient heuristics for a home healthcare problem. Neural Computing and Applications, 32(10), 6185-6205.

Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., & Tavakkoli-Moghaddam, R. (2018). The social engineering optimizer (SEO). Engineering Applications of Artificial Intelligence, 72, 267-293.

Ferguson, M. E., &Ketzenberg, M. E. (2005). Information Sharing to Improve Retail Product Freshness of Perishables (ed. 3).

Ghare, P. M. (1963). A model for an exponentially decaying inventory. J. ind. Engng, 14, 238-243.

Gholami-Zanjani, S. M., Jabalameli, M. S., Klibi, W., & Pishvaee, M. S. (2021). A robust location-inventory model for food supply chains operating under disruptions with ripple effects. International Journal of Production Research, 59(1), 301-324.

Govindan, K. (2018). Sustainable consumption and production in the food supply chain: A conceptual framework. International Journal of Production Economics, 195, 419-431.

Govindan, K., & Soleimani, H. (2017). A review of reverse logistics and closed-loop supply chains: a Journal of Cleaner Production focus. Journal of Cleaner Production, 142, 371-384.

Govindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9-28.

Hajiaghaei-Keshteli, M., & Fard, A. M. F. (2019). Sustainable closed-loop supply chain network design with discount supposition. Neural Computing and Applications, 31(9), 5343-5377.

Hajiaghaei-Keshteli, M., Sajadifar, S. M., & Haji, R. (2011). Determination of the economical policy of a three-echelon inventory system with (R, Q) ordering policy and information sharing. The International Journal of Advanced Manufacturing Technology, 55(5-8), 831-841.

Hemmati, M., & Pasandideh, S. H. R. (2020). A bi-objective supplier location, supplier selection and order allocation problem with green constraints: scenario-based approach. Journal of Ambient Intelligence and Humanized Computing, 1-24.

Joshi, A. D., & Gupta, S. M. (2019). Evaluation of design alternatives of End-Of-Life products using internet of things. International Journal of Production Economics, 208, 281-293.

Jouzdani, J., & Govindan, K. (2021). On the sustainable perishable food supply chain network design: A dairy products case to achieve sustainable development goals. Journal of Cleaner Production, 278, 123060.

Karimi, N., Zandieh, M., & Karamooz, H. R. (2010). Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach. Expert Systems with Applications, 37(6), 4024-4032.

Kazemi, N., Modak, N. M., & Govindan, K. (2019). A review of reverse logistics and closed loop supply chain management studies published in IJPR: a bibliometric and content analysis. International Journal of Production Research, 57(15-16), 4937-4960.

Li, Y., Lim, A., & Rodrigues, B. (2009). Note—Pricing and Inventory Control for a Perishable Product. Manufacturing & Service Operations Management, 11(3), 538-542.

Liao, Y., Kaviyani-Charati, M., Hajiaghaei-Keshteli, M., & Diabat, A. (2020). Designing a closed-loop supply chain network for citrus fruits crates considering environmental and economic issues. Journal of Manufacturing Systems, 55, 199-220.

Lin, D. Y., & Wu, M. H. (2016). Pricing and inventory problem in shrimp supply chain: A case study of Taiwan's white shrimp industry. Aquaculture, 456, 24-35.

Lusiantoro, L., Yates, N., Mena, C., &Varga, L. (2018). A refined framework of information sharing in perishable product supply chains. International Journal of Physical Distribution & Logistics Management, 48(3), 254-283.

Masruroh, N. A., Fauziah, H. A., & Sulistyo, S. R. (2020). Integrated production scheduling and distribution allocation for multi-products considering sequence-dependent setups: a practical application. Production Engineering, 14(2), 191-206.

Mo, W. Y., Man, Y. B., & Wong, M. H. (2018). Use of food waste, fish waste and food processing waste for China's aquaculture industry: Needs and challenge. Science of the Total Environment, 613, 635-643.

Nezhadroshan, A. M., Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2020). A scenario-based possibilistic-stochastic programming approach to address resilient humanitarian logistics considering travel time and resilience levels of facilities. International Journal of Systems Science: Operations & Logistics, 1-27.

Onggo, B. S., Panadero, J., Corlu, C. G., & Juan, A. A. (2019). Agri-food supply chains with stochastic demands: A multi-period inventory routing problem with perishable products. Simulation Modelling Practice and Theory, 97, 101970.

Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European journal of operational research, 156(2), 445-455.

Paduloh, P., Djatna, T., Sukardi, S., & Muslich, M. (2020). Uncertainty Models in Reverse Supply Chain: A Review. Int. J. Supply Chain Manag, 9, 139-149.

Peidro, D., Mula, J., Poler, R., & Lario, F. C. (2009). Quantitative models for supply chain planning under uncertainty: a review. The International Journal of Advanced Manufacturing Technology, 43(3-4), 400-420.

Peng, H., Shen, N., Liao, H., Xue, H., & Wang, Q. (2020). Uncertainty factors, methods, and solutions of closed-loop supply chain—A review for current situation and future prospects. Journal of Cleaner Production, 254, 120032.

Pishvaee, M. S., Farahani, R. Z., & Dullaert, W. (2010). A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computers & operations research, 37(6), 1100-1112.

Pourmehdi, M., Paydar, M. M., & Asadi-Gangraj, E. (2020). Scenario-based design of a steel sustainable closed-loop supply chain network considering production technology. Journal of Cleaner Production, 277, 123298.

Prajapati, H., Kant, R., & Shankar, R. (2019). Bequeath life to death: State-of-art review on reverse logistics. Journal of Cleaner Production, 211, 503-520.

Rahbari, M., Hajiagha, S. H. R., Dehaghi, M. R., Moallem, M., & Dorcheh, F. R. (2020). Modeling and solving a five-echelon location–inventory–routing problem for red meat supply chain. Kybernetes.

Rahemi, H., Torabi, S. A., Avami, A., & Jolai, F. (2020). Bioethanol supply chain network design considering land characteristics. Renewable and Sustainable Energy Reviews, 119, 109517.

Raza, S. A. (2020). A systematic literature review of closed-loop supply chains. Benchmarking: An International Journal.

Rocco, C. D., & Morabito, R. (2016). Production and logistics planning in the tomato processing industry: A conceptual scheme and mathematical model. Computers and Electronics in Agriculture, 127, 763-774.

Salehi-Amiri, A., Zahedi, A., Akbapour, N., & Hajiaghaei-Keshteli, M. (2021). Designing a sustainable closed-loop supply chain network for walnut industry. Renewable and Sustainable Energy Reviews, 141, 110821.

Sharma, J., Tyagi, M., & Bhardwaj, A. (2020). Parametric review of food supply chain performance implications under different aspects. Journal of Advances in Management Research.

Shekarian, E. (2020). A review of factors affecting closed-loop supply chain models. Journal of Cleaner Production, 253, 119823.

Siddh, M. M., Soni, G., Jain, R., & Sharma, M. K. (2018). Structural model of perishable food supply chain quality (PFSCQ) to improve sustainable organizational performance. Benchmarking: An International Journal.

Suwal, S., Ketnawa, S., Liceaga, A. M., & Huang, J. Y. (2018). Electro-membrane fractionation of antioxidant peptides from protein hydrolysates of rainbow trout (Oncorhynchus mykiss) byproducts. Innovative Food Science & Emerging Technologies, 45, 122-131.

Tabrizi, S., Ghodsypour, S. H., & Ahmadi, A. (2018). Modelling three-echelon warm-water fish supply chain: A bi-level optimization approach under Nash–Cournot equilibrium. Applied Soft Computing, 71, 1035-1053.

Taguchi, G. (1986). Introduction to quality engineering: designing quality into products and processes (No. 658.562 T3).

Taylor, D. H. (1994). Problems of food supply logistics in Russia and the CIS. International Journal of Physical Distribution & Logistics Management, 24(2), 15-22.

Tordecilla, R. D., Juan, A. A., Montoya-Torres, J. R., Quintero-Araujo, C. L., & Panadero, J. (2021). Simulation-optimization methods for designing and assessing resilient supply chain networks under uncertainty scenarios: A review. Simulation modelling practice and theory, 106, 102166.

Utomo, D. S., Onggo, B. S., & Eldridge, S. (2018). Applications of agent-based modelling and simulation in the agri-food supply chains. European Journal of Operational Research, 269(3), 794-805.

Van Engeland, J., Beliën, J., De Boeck, L., & De Jaeger, S. (2020). Literature review: Strategic network optimization models in waste reverse supply chains. Omega, 91, 102012.

Zhang, Y., Che, A., & Chu, F. (2020). Improved model and efficient method for bi-objective closed-loop food supply chain problem with returnable transport items. International Journal of Production Research, 1-18.

In Persian

Parsaiyan, S., Amiri, M., Azimi, P., & Taghavifard, M. T. (2019). Designing a green closed-loop supply chain simulation model and product pricing in the presence of a competitor. Industrial Management Studies, 17(52), 153-202.

طراحی شبکه زنجیره تامین حلقه بسته ماهیان پرورشی سردآبی با در نظر گرفتن شرایط عدم قطعیت: مطالعه موردی شبکه زنجیره تامین قزل آلا در مازندران

مراجع

مراجع

دوره 19، شماره 63دی 1400صفحه 1-50

دوره 19، شماره 63
دی 1400
صفحه 1-50