Economic Research and Perspectives

Economic Research and Perspectives

Optimization of Multi-Level Green Supply Chain Network - Multi-Product Aquatic Processing

Authors
zahra avazpur 1
ahmad ghorbanpur 2
reza Jalali 2
hojja parsa 3
1 Master of Industrial Management, Faculty of Business and Economics, Persian Gulf University
2 Assistant Professor, Department of Industrial Management, Faculty of Business and Economics, Persian Gulf University
3 Associate Professor, Department of Economics, Faculty of Business and Economics, Persian Gulf University
Abstract
Aim and Introduction

In the last decade, the design of the green supply chain network has become very important due to the increase in competition in global markets to achieve success, which requires the simultaneous commitment and cooperation of suppliers, manufacturers and distributors in the form of a network. One of the generalizable fields of the green supply chain network is the aquatic processing industry as one of the most important food industries that has paid less attention to optimize its supply chain in order to provide environmental benefits. In recent decades, the demand for food has increased more than the capacity to provide resources for it. Hence, the traditional food supply chain can no longer effectively manage demand. The increase in world population, globalization, global warming, drought and groundwater crisis in recent years have led to the loss of natural resources needed for animal husbandry and the reduction of marine reserves in Iran as well as in other Middle Eastern countries. The expansion of aquaculture farms has not only contributed to the development of sustainable food in the country, but has also been very effective in preserving endangered species. The aquaculture industry has grown significantly in recent decades. In this industry, green supply chain network design seems to be critical for aquatic processing.

Methodology

This study is a descriptive research and applied one in terms of purpose. In this research, after examining the theoretical foundations and experimental background, the network design was proposed. The aquatic supply network was designed in three levels, for four farms, three factories, and two customers. The first level includes aquaculture farms. The second level consists of models of aquatic processing factories. The third level includes customer networks. Of course, other levels of the aquaculture supply chain, i.e. retailers, can also be considered in network design. Then the appropriate mathematical model was formulated and validated with a case study. LP-metric method was used to solve this model. The GAMS software was used in this study.

Findings

In this research, the network of green aquatic management has been designed to achieve the goals of minimizing costs and carbon dioxide emissions and maximizing the capacity of units at three levels for four farms, three factories, and two customers. In this research, after drawing the network of the problem and explaining the mathematical model, it was solved using the comprehensive criterion method with p equal to 1, 2, and 3.

The value of the objective function was calculated with p equal to 0.09. By increasing the value of p, it became clear that there is no distance between the values of the objective functions and the ideal values. Then, the value of decision and binary variables was calculated. The first factory has the highest aquatic receiving capacity from the second, third and fourth farms.

Discussion and Conclusion

In order to achieve the goals with an emphasis on reducing the carbon footprint, the first factory should process the third product, the second factory should process all three types of products, and the third factory should process the second product. In this research, certainty is considered for the parameters. If the surrounding environment is very dynamic, this turbulence can increase the uncertainty of model parameters. Therefore, it is suggested to solve the research model assuming uncertainty in the parameters in other researches. In this research, the same weight has been considered for three purposes.

In this study, it is assumed that the transportation cost between nodes depends only on the distance. It is important to note that the cost of transportation between two points can be affected by various factors. Therefore, it is suggested to pay attention to these factors in other researches. Using the research model can minimize the total costs in the aquaculture network and maximize the capacity of using the components of the entire chain, bringing the environmental destruction to the lowest level. Therefore, it is suggested to use this model. One of the factors affecting the production and emission of carbon is the number of car trips. In other words, the lower the number of trips (carrying times), the lower the emissions. It is suggested managers to use a means of transportation with a higher capacity. Timely servicing of goods transport vehicles can also be effective in carbon emissions. It is recommended to periodically repair the car. In the chain under study, most of the cars were worn out and without fuel consumption reduction technology. It is suggested to replace the old vehicles carrying goods with modern and high-tech vehicles. It is recommended to use gas-fueled vehicles for transporting goods.
Keywords
network
Optimization
Green supply chain
Aquatic Processing

Subjects

فتحی، م.؛ نصراللهی، م.؛ زمانیان، ع. (1398). مدل‌سازی ریاضی شبکه زنجیره تأمین پایدار در وضعیت عدم قطعیت و حل آن با استفاده از الگوریتم‌های فراابتکاری، نشریه علمی پژوهشی مدیریت صنعتی،11(4)، 621-652.
فرهانی، ح. و عریضی، ح. (1395). روش‌های پیشرفته پژوهش در علوم انسانی. اصفهان: انتشارات جهاد دانشگاهی
قربان‌پور، ا.؛ پویا، ع.؛ ناظمی، ش. و ناجی‌عظیمی، ز. (1395). مدل تعاملی اقدامات مدیریت زنجیره تأمین سبز صنایع نفتی در ایران و کاربست آن در گروه‌بندی جهت تحلیل عملکرد سبزشان. رساله دکتری، دانشگاه فردوسی مشهد. ایران.
محمدی، ا.؛ عالم تبریز، ا.؛ پیشوایی، م. (1397). طراحی شبکه زنجیره ‌تأمین سبز حلقه ‌بسته همراه با تصمیم‌های مالی در شرایط عدم قطعیت. نشریه علمی پژوهشی مدیریت صنعتی، 10(1)، 61-84.
مهاجری ،ا. فلاح، م.(1394). مدل کمینه‌سازی هزینه برای کاهش کربن ناشی از روش های مختلف حمل و نقل، مجله تحقیق در عملیات در کاربردهای آن، 3(46)،18-3 .
Akhimien, N.G.; Latif, E., & Hou, S.S. (2020). Application of circular economy principles in buildings: A systematic review. Journal of Building Engineering, 38: 41-54.
Arponen, J.; Granskog, A.; Pantsar-Kallio, M.; Stuchtey, M.; Tormanen, A., & Vanthournout, H. (2015). The Opportunities of a Circular Economy for Finland. Sitra, 1796-7104.
Barros, M. V.; Salvador, R.; Francisco, G.; Carlos, A., & Piekarski, C. (2021). Circular economy as a driver to sustainable businesses. Cleaner Environmental Systems, 2:1-11.
Bjørnbet, M.M.; Skaar, C.; Fet, A.M., & Schulte, K. (2021). Circular economy in manufacturing companies: A review of case study literature. Journal of Cleaner Production, 294:268-280.
Bocken, N. M. P.; Tunn, V. S. C.; Van Den Hende, E. A., & Schoormans, J. P. L. (2019). Business models for sustainable consumption in the circular economy: An expert study. Journal of Cleaner Production, 212: 324-333.
Booker, J. M., & Ross, T. J. (2011). An evolution of uncertainty assessment and quantification. Scientia Iranica, 18(3), 669-676.
Boulding, K.E. (1966). The economics of the coming spaceship Earth. Environmental Quality in a Growing Economy,1 :3-14.
Demartini, M.; Pinna, C.; Aliakbarian, B.; Tonelli, F., & Terzi, S. (2015). Soft Drink Supply Chain Sustainability: A Case Based Approach to Identify and Explain Best Practices and Key Performance Indicators. Sustainability, 10(10), 35-40.
Ellen M. F. (2013). Towards the circular economy. Economic and business rationale for an accelerated transition. Retrieved from:
Enes Ünal, Jing Shao. (2018). A taxonomy of circular economy implementation strategies for manufacturing firms: Analysis of 391 cradle-to-cradle products. Journal of Cleaner Production, 212: 754-765.
Erkman, S. (1997). Industrial ecology: An historical view. Journal of Cleaner Production, 5: 1-10.
Feng, J., & Gong, Z. (2020). Integrated linguistic entropy weight method and multi objective programming model for supplier selection and order allocation in a circular economy: A case study. Journal of Cleaner Production, 277: 597-609.
Fresner, J. (1998). Cleaner production as a means for effective environmental management. Journal of Cleaner Production. 6: 171-179.
Geissdoerfer, M.; Savaget, P.; Bocken, N. M. P., & Hultink, E. J. (2017). The circular economy-A new sustainability paradigm?. Journal of Cleaner Production, 143: 757-768.
Grafstrom, J., & Aasma, S. (2021). Breaking circular economy barriers. Journal of Cleaner Production, 292: 2-18.
Guillén-Gosálbez, G., Grossmann, I., (2010). A global optimization strategy for the environmentally conscious design of chemical supply chains under uncertainty in the damage assessment model. Computers & Chemical Engineering. 34. 42-58.
Handfield, R. B., Nichols Jr., E. L.,(1999), Introduction to Supply Chain Management. Englewood Cliffs, NJ: Prentice Hall.
Handfield, R.B., & Nichols Jr., E.L. (1999). Introduction to Supply Chain Management. Prentice Hall, Upper Saddler River, New Jersey.
Hugos, M., (2011), Essentials of Supply Chain Management, Third Edition. Publisher: John Wiley &Sons, 2011.P 332.
Kazancoglu, Y.; Ipek, K., & Muhittin, S. (2018). A new holistic conceptual framework for green supply chain management performance assessment based on circular economy. Journal of Cleaner Production,195: 1282-1299.
Keulen, M., & Kirchherr, J. (2021). The Implementation of the circular economy: Barriers and enablers in the coffee value chain. Journal of Cleaner Production,38: 33-46.
Kirchherr, J., Reike, D., Hekkert, M. (2017). Conceptualizing the circular economy: An analysis of 114 definitions. Resources, Conservation and Recycling, 127: 221-232.
La Londe, Bernard J. , (1997), Supply Chain Management: Myth or Reality? Supply Chain Management. Review. Spring. 1, 6-7.
Lieder, M.; Asif, F. M. A.; Rashid, A.; Mihelič, A., & Kotnik, S. (2017). Towards circular economy implementation in manufacturing systems using a multi-method simulation approach to link design and business strategy. The International Journal of Advanced Manufacturing Technology, 93: 1953–1970.
Liu, Y., Ma, L, Liu, Y., (2021), A novel robust fuzzy mean-UPM model for green closed-loop supply chain network design under distribution ambiguity, Applied Mathematical Modelling, 92, 99-135.
Liu, Y., Ma, L, Liu, Y., (2021), A novel robust fuzzy mean-UPM model for green closed-loop supply chain network design under distribution ambiguity, Applied Mathematical Modelling, 92, 99-135.
McDonough, W., & Braungart, M. )2002(. Cradle to cradle: Remaking the way we make things. Chem. Eng. News 193.
McDonough, W., & Braungart, M., )2013(.The Upcycle: Beyond Sustainability-Designing for Abundance, North Point Press, 227.
Melo, M.T.., Nickel, S., Saldanha da Gama, F., (2006). Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning, Computers & Operations Research, 33 (1), 181-208.
Merli, R.; Preziosi, M., & Acampora, A. )2017(. How do scholars approach the circular economy? A systematic literature review. Journal of Cleaner Production, 178:703-722.
Mohammadi, A., & Alemtabriz, A., & Pishvaee, M., & Zandeieh, M. (2020). A multi-stage stochastic programming model for sustainable closed-loop supply chain network design with financial decisions: A case study of plastic production and recycling supply chain. Science iranac, 27(1 (Transactions E: Industrial Engineering)), 377-395.
Moreno, J.; Ormaz, M.; Alvarez, M.J., & Jac, C. (2021). Advancing circular economy performance indicators and their application in Spanish companies. Journal of Cleaner Production 279: 605-617.
Murray, A.; Skene, K., & Haynes, K. )2015(. The circular economy: An interdisciplinary exploration of the concept and application in a global context. Journal of Business Ethics,140 : 369-380.
Oliver, R. K., Webber, M. D. ,‌(1982), Supply-chain management: logistics catches up with strategy. In: M. Christopher, ed. ,(1992), Logistics: The strategic issues. London: Chapman & Hall, 63-75.
Pearce, D.W., & Turner, R.K. (1990). Economics of Natural Resources and the Environment. JHU Press, Baltimore.
Rivera-Torres, P.; Garcés-Ayerbe, C.; Suárez-Perales, I., & Leyva-de la Hiz, D. I. (2020). Is it possible to change from a linear to a circular economy? An overview of opportunities and barriers for European small and medium-sized enterprise companies. International Journal of Environmental Research and Public Health, 16(5): 851.
Salmenperä, H.; Pitkänen, K.; Kautto, P., & Saikku, L. (2020). Critical factors for enhancing the circular economy in waste management. Journal of Cleaner Production,280:339-353.
Sarkis, J. Zhu, Q. & Lai, K. H (2011), "An organizational theoretic review of green supply chain management literature", International Journal of Production Economics, 130(1). PP: 1-15.
Seuring, S., Müller, M. ,‌(2008), From a literature review to a conceptual framework for sustainable supply chain management. J. Clean. Prod. 16 (15), 1699–1710.
Singh, P., & Giacosa, E. (2020). Cognitive biases of consumers as barriers in transition towards circular economy. Management Decision. 57(4):921-936.
Smol, M.; Kulczycka, J.; Henclik, A.; Gorazda, K., & Wzorek, Z. (2015). The possible use of sewage sludge ash (SSA) in the construction industry as a way towards a circular economy. Journal of Cleaner Production,95: 45-54.
Srivastava, S.K. ,‌(2007), Green supply‐chain management: a state‐of‐the‐art literature review. Int. J. Manag. Rev. 9 (1), 53–80.
Tseng, M.L., Chiu, A., (2013). Evaluating firm's green supply chain management in linguistic preferences. Journal of Cleaner Production. 40.
Yavari, M. & Zaker, H. (2020). Designing a resilient-green closed loop supply chain network for perishable products by considering disruption in both supply chain and power networks. Computers & Chemical Engineering, 134,106680 .
 
Economic Research and Perspectives
Volume 23, Issue 1
Spring 2023
Pages 197-219