شبیه سازی هزینه حمل و نقل طراحی شبکه زنجیره تأمین با در نظر گرفتن تقاضای وابسته به قیمت و کیفیت

نوع مقاله : مقاله پژوهشی

نویسندگان

مهندسی صنایع، فنی و مهندسی، دانشگاه آزاد اسلامی واحد دماوند، تهران، ایران

چکیده

طراحی مناسب شبکه، اثرات بسیاری بر عملکرد، بهره وری و اثربخشی زنجیره های تأمین در رسیدن به اهداف انتظاری و برآورده نمودن نیازهای مشتریان دارد. در این پژوهش، یک مدل چندهدفه ی چندسطحی برای طراحی شبکه ی زنجیره تأمین با در نظر گرفتن قیمت- گذاری، سطح کیفی محصولات و اختلال ارائه شده است. هزینه ی حملونقل هر وسیلهی نقلیه یک تابع تصادفی پویا و نه پارامتر فرض شده است. بنابراین از شبیه سازی گسسته-پیشامد برای ارزیابی هزینه های حمل ونقل استفاده شده است. با توجه به نقش مهم مفاهیم ریسک و اختلال در طراحی شبکه ی زنجیره تأمین، کمینه سازی ریسک به همراه بیشینه سازی سود با توجه به مفاهیم قیمت گذاری و کیفیت، به عنوان توابع هدف تعریف شده اند. تقاضای زنجیره تأمین تابعی خطی از قیمت و سطح کیفی محصولات در نظر گرفته شده است. در نهایت، مسئله ی طراحی شبکه زنجیره تأمین با شبیه سازی، ریسک و تقاضای وابسته به قیمت و کیفیت با الگوریتم NSGA-IIحل و نتایج با الگوریتم MOSA اعتبارسنجی شده اند.

کلیدواژه‌ها


عنوان مقاله [English]

Simulation of transportation costs Supply chain network design taking into account price and quality dependent demand

نویسندگان [English]

  • Sayed Mohammad mahdi Kazemi
  • Payman Taki
Industrial Engineering, Technical and Engineering, Islamic Azad University, Damavand Branch, Tehran, Iran
چکیده [English]

Proper network design has many effects on the performance, efficiency and effectiveness of supply chains in achieving the expected goals and meeting the needs of customers. In this research, a multi-level multi-objective model for supply chain network design considering pricing, product quality level and disruption is presented. The cost of transporting each vehicle is assumed to be a dynamic random function rather than a parameter. Therefore, discrete-event simulation has been used to estimate transportation costs. Due to the important role of risk and disruption concepts in supply chain network design, risk minimization along with profit maximization according to pricing and quality concepts have been defined as objective functions. Supply chain demand is considered a linear function of price and quality level of products. Finally, the supply chain network design problem is solved by simulation, risk and price-dependent demand with NSGA-II algorithm and the results are validated by MOSA algorithm.

کلیدواژه‌ها [English]

  • Supply chain network design
  • Discrete-event simulation
  • Disruption
  • pricing
  • Quality
[1] Kazemi, S. M. M., & Taki, P. (2012). Discrete event simulation of Packed groceries logistics supply system. 2012 4th International Conference on Computer Modeling and Simulation (ICCMS 2012). HongKong, IPCSIT, 22.
[2] 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(0), 92–118. http://doi.org/http://dx.doi.org/10.1016/j.omega.2013. 08.006
[3] Shapiro, J. (2006). Modeling the supply chain. Cengage Learning.
[4] Karnon, J., & Afzali, H. H. A. (2014). When to use discrete event simulation (DES) for the economic evaluation of health technologies? A review and critique of the costs and benefits of DES. Pharmacoeconomics, 32(6), 547–558.
[5] Taki, P., & Kazemi, S. M. M. (2013). A Three Echelon Supply System: A Discrete Event Simulation. In Communication Systems and Network Technologies (CSNT), 2013 International Conference on (pp. 850–852). IEEE.
[6] Pidd, M. (1998). Computer simulation in management science.
[7] Li, X., & Wang, Q. (2007). Coordination mechanisms of supply chain systems. European Journal of Operational Research, 179(1), 1–16.
[8] Fugate, B., Sahin, F., & Mentzer, J. T. (2006). Supply chain management coordination mechanisms. Journal of Business Logistics, 27(2), 129–161.
[9] Xu, L., & Beamon, B. M. (2006). Supply chain coordination and cooperation mechanisms: an attribute‐based approach. Journal of Supply Chain Management, 42(1), 4–12.
[10] Thomas, D. J., & Griffin, P. M. (1996). Coordinated supply chain management. European Journal of Operational Research, 94(1), 1–15.
[11] Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation. In Das Summa Summarum des Management (pp. 265–275). Springer.
[12] Xu, N., & Nozick, L. (2009). Modeling supplier selection and the use of option contracts for global supply chain design. Computers & Operations Research, 36(10), 2786–2800. http://doi.org/http://dx.doi.org/10.1016/j.cor.2008.12. 013
[13] Azad, N., Saharidis, G. K. D., Davoudpour, H., Malekly, H., & Yektamaram, S. A. (2013). Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach. Annals of Operations Research, 210(1), 125–163.
[14] Azad, N., Davoudpour, H., Saharidis, G. K. D., & Shiripour, M. (2014). A new model to mitigating random disruption risks of facility and transportation in supply chain network design. The International Journal of Advanced Manufacturing Technology, 70(9–12), 1757–1774.
[15] Singh, A. R., Mishra, P. K., Jain, R., & Khurana, M. K. (2012). Design of global supply chain network with operational risks. The International Journal of Advanced Manufacturing Technology, 60(1–4), 273– 290.
[16] Giri, B. C., & Bardhan, S. (2014). Coordinating a supply chain with backup supplier through buyback contract under supply disruption and uncertain demand. International Journal of Systems Science: Operations & Logistics, 1(4), 193–204.
[17] Xu, M., Wang, X., & Zhao, L. (2014). Predicted supply chain resilience based on structural evolution against random supply disruptions. International Journal of Systems Science: Operations & Logistics, 1(2), 105–117.
[18] Fang, H., & Xiao, R. (2014). Cycle quality chain early warning network with e-channel lead time disruption. International Journal of Systems Science: Operations & Logistics, 1(1), 47–67.
[19] Yu, H., Sun, C., & Chen, J. (2007). Simulating the supply disruption for the coordinated supply chain. Journal of Systems Science and Systems Engineering, 16(3), 323–335.