طراحی مدل زنجیره‌تامین حلقه بسته در شرایط عدم‌اطمینان با در نظر گرفتن انبارهای واسطه ای (بررسی موردی: شرکت خودرنگ)
محورهای موضوعی : مدیریت صنعتی
Laila Arab
1
(Master of Science in Management, Amin Instituted Of Higher Education,Isfahan,Iran.)
sayyed mohammad reza davoodi
2
(Assistant Professor, Department of Management, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran)
کلید واژه: شبکه عصبی, لجستیک, عدم قطعیت, زنجیره‌تامین حلقه بسته,
چکیده مقاله :
مدیریت‌زنجیره‌تامین، فرآیند برنامه‌ریزی، اجرا و کنترل کارآمد جریان مواد‌اولیه، موجودی‌های در جریان ساخت، محصولات‌نهایی و همچنین جریان اطلاعات مرتبط با آن از تامین مواد اولیه تا تحویل به مصرف کننده نهایی می‌باشد. هدف این تحقیق طراحی یک مدل زنجیره‌تامین حلقه‌بسته در شرایط عدم‌اطمینان با در نظر گرفتن انبارهای واسطه ای در شرکت خودرنگ می باشد تا با تاثیرآن بر روندتولید و توزیع، برلزوم شناخت هرچه بیشتر این مفهوم و جایگاهی که می‌تواند در توسعه شرکت خودرنگ داشته باشد تاکیدکند. در این تحقیق پس ازجمع‌آوری اطلاعات ومشاوره با کارشناسان شرکت خودرنگ، تا حد امکان بدون لطمه‌زدن به اصل داده‌ها مدل ساده‌سازی گردید و با استفاده ازتکنیک‌های برنامه‌ریزی غیرخطی ، شبکه‌های‌عصبی و با نرم‌افزار‌های متلب و گمز کدگذاری گردید. نتایج این پژوهش در یک محیط بسته و بدون دخالت متغیرهای خارج از مدل، در شرکت خودرنگ نشان می‌دهد مدیران این شرکت توانسته‌اند با پیاده‌سازی معیارهای مربوط به زنجیره‌تامین حلقه‌بسته وپیش‌بینی میزان تقاضا و برگشت محصول، رضایت مشتریان و تامین‌کنندگان عمده خود را فراهم سازند.
The management of the supply chain is the process of planning, implementing and controlling the flow of raw material, inventory in the course of construction, final products, as well as the flow of related information from the supply of raw materials up to delivery to the final consumer. The purpose of this study is to design a closed-loop supply chain in uncertainty conditions taking into account the intermediary warehouses in the khodrang company. so that its impact on the process of production and distribution, Berlzom recognizes as much of this concept and position as can be found in Enhance the development of khodrang company. In this research after collecting information and consulting with experts of the company, the model was simplified as much as possible without damaging the data principle. Using the nonlinear programming and neural networks and with the software Metalb and Gram coding. The results of this research in a closed environment without the involvement of external variables in the company itself show that the managers of this company have been able to implement the criteria and indicators related to the ring chain and the Nasal demand and product return levels provide the satisfaction of their major customers and suppliers.
1- Abdallah, T., Farhat, A., Diabat, A., & Kennedy, S. (2012). Green supply chains with carbon trading and environmental sourcing: Formulation and life cycle assessment. Applied Mathematical Modelling, 36(9), 4271-4285.
2- Azar, A., Amini, M., Rajabzadeh Ghatari, M. (2016).Design of integrated mathematical model for closed-loop supply chain. Journal of Management Research in Iran 20(1).1-28.
3- Ballou, R. H. (2004). Business logistics: Supply chain management. Ed.
4- Choi, J., Bai, S. X., Geunes, J., & Romeijn, H. E. (2007). Manufacturing delivery performance for supply chain management. Mathematical and Computer Modelling, 45(1-2), 11-20.
5- Fallah, A., Zagardi, H., Chaharsoghi, K. (2017). Introducing a two-tier model of closed-loop supply chain design in terms of uncertainty and inter-chain competition. Modeling in Engineering. Iran.15(49), 201-215.
6- Giri, B. C., & Sharma, S. (2015). Optimizing a closed-loop supply chain with manufacturing defects and quality dependent return rate. Journal of Manufacturing Systems, 35, 92-111.
7- Ghomi-Avili, M., Tavakkoli-Moghaddam, R., Jalali, G., & Jabbarzadeh, A. (2017). A network design model for a resilient closed loop supply chain with lateral transshipment. International Journal of Engineering-Transactions C: Aspects, 30(3), 374.
8- Guide Jr, V. D. R., & Van Wassenhove, L. N. (2009). OR FORUM—The evolution of closed-loop supply chain research. Operations research, 57(1), 10-18.
9- Hasanzadeh, A., Jaarian, A. (2010). The Effect of Analogy on Supply Chains, First Edition, Tehran, Managers Today.
10- Hasanzadeh, SH., Paryab, H. (2013). Proposal Selection and Evaluation of Suppliers by Fuzzy Combination Method and Bee Algorithm. , 7th National Conference and First International Conference on E-Commerce and Economics, Tehran, Iranian Association of E-Commerce.
11- Hasani, A. (2017). Two-Step Stochastic Programming Based on Sample Mean Approximation and Accelerated Bandwidth Algorithm for Designing Closed-loop Supply Chain Network under Uncertainty. Modeling in Engineering 15(49), 217-234.
12- Hasani, A., Zegordi, S. H., & Nikbakhsh, E. (2012). Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty. International Journal of Production Research, 50(16), 4649-4669.
13- Mahmoudzadeh, M., Sadjadi, S. J., & Mansour, S. (2013). Robust optimal dynamic production/pricing policies in a closed-loop system. Applied Mathematical Modelling, 37(16-17), 8141-8161.
14- Melo, M. T., Nickel, S., & Saldanha-Da-Gama, F. (2009). Facility location and supply chain management–A review. European journal of operational research, 196(2), 401-412.
15- 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.
16- Simchi-Levi, D., Simchi-Levi, E., & Kaminsky, P. (1999). Designing and managing the supply chain: Concepts, strategies, and cases. New York: McGraw-Hill
17- Simon, B., Amor, M. B., & Földényi, R. (2016). Life cycle impact assessment of beverage packaging systems: focus on the collection of post-consumer bottles. Journal of Cleaner Production, 112, 238-248.
18- Saberi Rabar, M., Farghani, M., Kazemi, M.(2014). Evaluation and Selection of Suppliers in Supply Chain Using Combined Model of Fuzzy Hierarchical Fuzzy Analysis. International Management Conference, Tehran, Mobin Cultural Ambassadors Institute.
19- Taherkhani, M., Tavakoli Moghadam.(2017). Development of a Two-Level Solution Four-Level Supply Chain Model Using STEM Method. Production and Operations Management. 8(1).
_||_1- Abdallah, T., Farhat, A., Diabat, A., & Kennedy, S. (2012). Green supply chains with carbon trading and environmental sourcing: Formulation and life cycle assessment. Applied Mathematical Modelling, 36(9), 4271-4285.
2- Azar, A., Amini, M., Rajabzadeh Ghatari, M. (2016).Design of integrated mathematical model for closed-loop supply chain. Journal of Management Research in Iran 20(1).1-28.
3- Ballou, R. H. (2004). Business logistics: Supply chain management. Ed.
4- Choi, J., Bai, S. X., Geunes, J., & Romeijn, H. E. (2007). Manufacturing delivery performance for supply chain management. Mathematical and Computer Modelling, 45(1-2), 11-20.
5- Fallah, A., Zagardi, H., Chaharsoghi, K. (2017). Introducing a two-tier model of closed-loop supply chain design in terms of uncertainty and inter-chain competition. Modeling in Engineering. Iran.15(49), 201-215.
6- Giri, B. C., & Sharma, S. (2015). Optimizing a closed-loop supply chain with manufacturing defects and quality dependent return rate. Journal of Manufacturing Systems, 35, 92-111.
7- Ghomi-Avili, M., Tavakkoli-Moghaddam, R., Jalali, G., & Jabbarzadeh, A. (2017). A network design model for a resilient closed loop supply chain with lateral transshipment. International Journal of Engineering-Transactions C: Aspects, 30(3), 374.
8- Guide Jr, V. D. R., & Van Wassenhove, L. N. (2009). OR FORUM—The evolution of closed-loop supply chain research. Operations research, 57(1), 10-18.
9- Hasanzadeh, A., Jaarian, A. (2010). The Effect of Analogy on Supply Chains, First Edition, Tehran, Managers Today.
10- Hasanzadeh, SH., Paryab, H. (2013). Proposal Selection and Evaluation of Suppliers by Fuzzy Combination Method and Bee Algorithm. , 7th National Conference and First International Conference on E-Commerce and Economics, Tehran, Iranian Association of E-Commerce.
11- Hasani, A. (2017). Two-Step Stochastic Programming Based on Sample Mean Approximation and Accelerated Bandwidth Algorithm for Designing Closed-loop Supply Chain Network under Uncertainty. Modeling in Engineering 15(49), 217-234.
12- Hasani, A., Zegordi, S. H., & Nikbakhsh, E. (2012). Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty. International Journal of Production Research, 50(16), 4649-4669.
13- Mahmoudzadeh, M., Sadjadi, S. J., & Mansour, S. (2013). Robust optimal dynamic production/pricing policies in a closed-loop system. Applied Mathematical Modelling, 37(16-17), 8141-8161.
14- Melo, M. T., Nickel, S., & Saldanha-Da-Gama, F. (2009). Facility location and supply chain management–A review. European journal of operational research, 196(2), 401-412.
15- 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.
16- Simchi-Levi, D., Simchi-Levi, E., & Kaminsky, P. (1999). Designing and managing the supply chain: Concepts, strategies, and cases. New York: McGraw-Hill
17- Simon, B., Amor, M. B., & Földényi, R. (2016). Life cycle impact assessment of beverage packaging systems: focus on the collection of post-consumer bottles. Journal of Cleaner Production, 112, 238-248.
18- Saberi Rabar, M., Farghani, M., Kazemi, M.(2014). Evaluation and Selection of Suppliers in Supply Chain Using Combined Model of Fuzzy Hierarchical Fuzzy Analysis. International Management Conference, Tehran, Mobin Cultural Ambassadors Institute.
19- Taherkhani, M., Tavakoli Moghadam.(2017). Development of a Two-Level Solution Four-Level Supply Chain Model Using STEM Method. Production and Operations Management. 8(1).
