ارائه مدل پویایی سیستم برای بهینه سازی چندهدفه تولید– موجودی– مسیریابی در زنجیره تامین سبز تحت شرایط عدم قطعیت
محورهای موضوعی :
مدیریت صنعتی
Katayoun Naderi
1
,
Roya M.ahari
2
,
Javid Jouzdani
3
,
Atefeh Amindoust
4
1 - Department of Industrial Engineering, Najafabad Branch, ,Islamic Azad University, Najafabad, Iran.
2 - Department of Industrial Engineering,
Najafabad Branch, Islamic Azad University,
Najafabad Iran
3 - Department of Industrial Engineering, Golpayegan University of
Technology: Golpayegan, Isfahan,
4 - Department of Industrial Engineering, Najafabad Branch, Islamic Azad university, Najafabad, Iran
تاریخ دریافت : 1400/01/28
تاریخ پذیرش : 1400/07/24
تاریخ انتشار : 1400/11/10
کلید واژه:
تولید-موجودی-مسیریابی,
مدل پویایی سیستم,
شرایط عدم قطعیت,
بهینه سازی,
چکیده مقاله :
در این تحقیق برای طراحی یک مدل بهینه سازی چند هدفه؛ مولفه های هزینه، رضایت مشتری و حفاظت از محیط زیست در نظر گرفته شده است. برای بهینه سازی چندهدفه تولید-موجودی-مسیریابی در زنجیره تامین سبز تحت شرایط عدم قطعیت مدل پویایی سیستم ارائه شده است. با تقاضای مشتری برای چند دوره، مدل می تواند با تمرکز بر تصمیم گیری در مورد مواردی مانند انتخاب تامین کننده و خرده فروش با توجه به فاصله میان آن‌ها، مدل‌های تولید و سطح نوپایی تکنولوژی حمل و نقل، به تصمیم‌گیری بپردازد. برای این منظور برای گردآوری اطلاعات ابتدا با استفاده از بررسی مطالعات پیشین، به تعیین متغیرهای تاثیرگذار بر مدل پرداخته شد، سپس با توجه به نظر خبرگان این موضوع، این متغیرها آنالیز شده، سپس روابط بین متغیرهای انتخاب شده با بکارگیری مدل علت و معلولی مشخص شد و پس از آن با طراحی مدل پویایی سیستم و ارزیابی و بررسی آن از طریق آزمون های تعریف شده توسط اجرا در نرم افزار Vensim مدلسازی تحقیق به اتمام رسید. سرانجام، سه سناریو برای تعیین استراتژی‌های تاثیرگذار بر مدل توسعه داده شد. نتایج حاکی از تأثیرگذارترین استراتژی‌ها در دستیابی به وضعیت مطلوب، حداکثر رضایت مشتری، حداقل هزینه و موجودی انبار و حداکثر میزان تولید با اجرای مناسب پروژه های در حال اجرای سازمان در راستای تولید سبز با استفاده از دانش فنی مناسب است.
چکیده انگلیسی:
This study examines the cost, customer satisfaction and environmental protection to design a multi-objective optimization model using system dynamics and to provide a system dynamics model for multi-objective production-inventory-routing in the green supply chain under uncertainty. Considers.By customer demand for several periods, the model can make decisions by focusing on decisions such as the choice of supplier and retailer according to the distance between them, production models and the nascent level of transportation technology. For this purpose, to collect information, first using previous studies, the variables affecting the model (20 variables) were determined, then according to the experts, these variables were analyzed, then the relationships between the selected variables using the model. Cause and effect were identified and then the research modeling was completed by designing a system dynamics model and evaluating it through tests defined by execution in vensim software. Finally, three scenarios were developed to determine the strategies affecting the model. The results indicate the most effective strategies in achieving the desired situation, maximum customer satisfaction, minimum cost and inventory and maximum production with proper implementation of ongoing projects in the direction of green production using appropriate technical knowledge.
منابع و مأخذ:
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Badhotiya, G. K., Soni, G., & Mittal, M. L. (2019). Fuzzy multi-objective optimization for multi-site integrated production and distribution planning in two echelon supply chain. The International Journal of Advanced Manufacturing Technology, 102(1-4), 635-645.
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Banasik, A., Kanellopoulos, A., Bloemhof-Ruwaard, J. M., & Claassen, G. D. H. (2019). Accounting for uncertainty in eco-efficient agri-food supply chains: A case study for mushroom production planning. Journal of cleaner production, 216, 249-256.
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Cao, Y., Zhao, Y., Wen, L., Li, Y., Wang, S., Liu, Y., ... & Weng, J. (2019). System dynamics simulation for CO2 emission mitigation in green electric-coal supply chain. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2019.06.029
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Gharaei, A., & Jolai, F. (2018). A multi-agent approach to the integrated production scheduling and distribution problem in multi-factory supply chain. Applied Soft Computing, 65, 577-589.
Hendalianpour, A. (2018). Mathematical Modeling for Integrating Production-Routing-Inventory Perishable Goods: A Case Study of Blood Products in Iranian Hospitals. In International Conference on Dynamics in Logistics(pp. 125-136). Springer, Cham.
Liu, Y., Dehghani, E., Jabalameli, M. S., Diabat, A., & Lu, C. C. (2020). A coordinated location-inventory problem with supply disruptions: A two-phase queuing theory–optimization model approach. Computers & Industrial Engineering, 142, 106326.
Manerba, D., & Perboli, G. (2019). New solution approaches for the capacitated supplier selection problem with total quantity discount and activation costs under demand uncertainty. Computers & Operations Research, 101, 29-42.
Manupati, V. K., Jedidah, S. J., Gupta, S., Bhandari, A., & Ramkumar, M. (2019). Optimization of a multi-echelon sustainable production-distribution supply chain system with lead time consideration under carbon emission policies. Computers & Industrial Engineering, 135, 1312-1323.
Mohammed, A., & Duffuaa, S. (2019, January). A Meta-Heuristic Algorithm Based on Simulated Annealing for Designing Multi-Objective Supply Chain Systems. In 2019 Industrial & Systems Engineering Conference (ISEC)(pp. 1-6). IEEE.
Niu, B., Tan, L., Liu, J., Liu, J., Yi, W., & Wang, H. (2019). Cooperative bacterial foraging optimization method for multi-objective multi-echelon supply chain optimization problem. Swarm and Evolutionary Computation. https://doi.org/10.1016/j.swevo.2019.05.003
Nurjanni, K. P., Carvalho, M. S., & Costa, L. (2017). Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model. International Journal of Production Economics, 183, 421-432.
Qureshi, N., Harry‐O'kuru, R., Liu, S., & Saha, B. (2019). Yellow top (Physaria fendleri) presscake: A novel substrate for butanol production and reduction in environmental pollution. Biotechnology progress, 35(3), e2767.
Rad, R. S., & Nahavandi, N. (2018). A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount. Journal of cleaner production, 196, 1549-1565.
Rebs, T., Thiel, D., Brandenburg, M., & Seuring, S. (2019). Impacts of stakeholder influences and dynamic capabilities on the sustainability performance of supply chains: A system dynamics model. Journal of Business Economics, 1-34.
Sarkar, B., Omair, M., & Choi, S. B. (2018). A multi-objective optimization of energy, economic, and carbon emission in a production model under sustainable supply chain management. Applied Sciences, 8(10), 1744.
Schwaninger, M., & Groesser, S. (2020). System Dynamics Modeling: Validation for Quality Assurance. System Dynamics: Theory and Applications, 119-138.
Song, M., Cui, X., & Wang, S. (2018). Simulation of land green supply chain based on system dynamics and policy optimization. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2018.08.021
Tajik, N., Tavakkoli-Moghaddam, R., Vahdani, B., & Mousavi, S. M. (2014). A robust optimization approach for pollution routing problem with pickup and delivery under uncertainty. Journal of Manufacturing Systems, 33(2), 277-286.
Vafaeenezhad, T., Tavakkoli-Moghaddam, R., & Cheikhrouhou, N. (2019). Multi-objective mathematical modeling for sustainable supply chain management in the paper industry. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2019.05.027
Yan, M. R., Chien, K. M., & Yang, T. N. (2016). Green component procurement collaboration for improving supply chain management in the high technology industries: A case study from the systems perspective. Sustainability, 8(2), 105.
Yang, G., Zhang, W., & Zha, D. (2019). Industrial production: Pursuing scale expansion or pollution reduction? Judgment based on the Copeland-Toylor model. Journal of cleaner production, 216, 14-24.
Zhang, H., & Yang, K. (2020). Multi-objective optimization for green dual-channel supply chain network design considering transportation mode selection. In Supply Chain and Logistics Management: Concepts, Methodologies, Tools, and Applications(pp. 382-404). IGI Global.
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Alinaghian, M., & Zamani, M. (2019). A bi-objective fleet size and mix green inventory routing problem, model and solution method. Soft Computing, 23(4), 1375-1391.
Badhotiya, G. K., Soni, G., & Mittal, M. L. (2019). Fuzzy multi-objective optimization for multi-site integrated production and distribution planning in two echelon supply chain. The International Journal of Advanced Manufacturing Technology, 102(1-4), 635-645.
Bala, B. K., Arshad, F. M., & Noh, K. M. (2017). System dynamics. Springer Texts in Business and Economics.
Banasik, A., Kanellopoulos, A., Bloemhof-Ruwaard, J. M., & Claassen, G. D. H. (2019). Accounting for uncertainty in eco-efficient agri-food supply chains: A case study for mushroom production planning. Journal of cleaner production, 216, 249-256.
Beamon, B.M. (1998). Supply chain design and analysis: Models and methods. International Journal of Production Economics, 55(3), 281-294.
Cao, Y., Zhao, Y., Wen, L., Li, Y., Wang, S., Liu, Y., ... & Weng, J. (2019). System dynamics simulation for CO2 emission mitigation in green electric-coal supply chain. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2019.06.029
Forrester, J. W. (2007). System dynamics—the next fifty years. System Dynamics. Review: The Journal of the System Dynamics Society, 23(2‐3), 359-370.
Garai, A., & Roy, T. K. (2020). Multi-objective optimization of cost-effective and customer-centric closed-loop supply chain management model in T-environment. Soft Computing, 24(1), 155-178.
Gharaei, A., & Jolai, F. (2018). A multi-agent approach to the integrated production scheduling and distribution problem in multi-factory supply chain. Applied Soft Computing, 65, 577-589.
Hendalianpour, A. (2018). Mathematical Modeling for Integrating Production-Routing-Inventory Perishable Goods: A Case Study of Blood Products in Iranian Hospitals. In International Conference on Dynamics in Logistics(pp. 125-136). Springer, Cham.
Liu, Y., Dehghani, E., Jabalameli, M. S., Diabat, A., & Lu, C. C. (2020). A coordinated location-inventory problem with supply disruptions: A two-phase queuing theory–optimization model approach. Computers & Industrial Engineering, 142, 106326.
Manerba, D., & Perboli, G. (2019). New solution approaches for the capacitated supplier selection problem with total quantity discount and activation costs under demand uncertainty. Computers & Operations Research, 101, 29-42.
Manupati, V. K., Jedidah, S. J., Gupta, S., Bhandari, A., & Ramkumar, M. (2019). Optimization of a multi-echelon sustainable production-distribution supply chain system with lead time consideration under carbon emission policies. Computers & Industrial Engineering, 135, 1312-1323.
Mohammed, A., & Duffuaa, S. (2019, January). A Meta-Heuristic Algorithm Based on Simulated Annealing for Designing Multi-Objective Supply Chain Systems. In 2019 Industrial & Systems Engineering Conference (ISEC)(pp. 1-6). IEEE.
Niu, B., Tan, L., Liu, J., Liu, J., Yi, W., & Wang, H. (2019). Cooperative bacterial foraging optimization method for multi-objective multi-echelon supply chain optimization problem. Swarm and Evolutionary Computation. https://doi.org/10.1016/j.swevo.2019.05.003
Nurjanni, K. P., Carvalho, M. S., & Costa, L. (2017). Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model. International Journal of Production Economics, 183, 421-432.
Qureshi, N., Harry‐O'kuru, R., Liu, S., & Saha, B. (2019). Yellow top (Physaria fendleri) presscake: A novel substrate for butanol production and reduction in environmental pollution. Biotechnology progress, 35(3), e2767.
Rad, R. S., & Nahavandi, N. (2018). A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount. Journal of cleaner production, 196, 1549-1565.
Rebs, T., Thiel, D., Brandenburg, M., & Seuring, S. (2019). Impacts of stakeholder influences and dynamic capabilities on the sustainability performance of supply chains: A system dynamics model. Journal of Business Economics, 1-34.
Sarkar, B., Omair, M., & Choi, S. B. (2018). A multi-objective optimization of energy, economic, and carbon emission in a production model under sustainable supply chain management. Applied Sciences, 8(10), 1744.
Schwaninger, M., & Groesser, S. (2020). System Dynamics Modeling: Validation for Quality Assurance. System Dynamics: Theory and Applications, 119-138.
Song, M., Cui, X., & Wang, S. (2018). Simulation of land green supply chain based on system dynamics and policy optimization. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2018.08.021
Tajik, N., Tavakkoli-Moghaddam, R., Vahdani, B., & Mousavi, S. M. (2014). A robust optimization approach for pollution routing problem with pickup and delivery under uncertainty. Journal of Manufacturing Systems, 33(2), 277-286.
Vafaeenezhad, T., Tavakkoli-Moghaddam, R., & Cheikhrouhou, N. (2019). Multi-objective mathematical modeling for sustainable supply chain management in the paper industry. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2019.05.027
Yan, M. R., Chien, K. M., & Yang, T. N. (2016). Green component procurement collaboration for improving supply chain management in the high technology industries: A case study from the systems perspective. Sustainability, 8(2), 105.
Yang, G., Zhang, W., & Zha, D. (2019). Industrial production: Pursuing scale expansion or pollution reduction? Judgment based on the Copeland-Toylor model. Journal of cleaner production, 216, 14-24.
Zhang, H., & Yang, K. (2020). Multi-objective optimization for green dual-channel supply chain network design considering transportation mode selection. In Supply Chain and Logistics Management: Concepts, Methodologies, Tools, and Applications(pp. 382-404). IGI Global.
