طراحی سیستم پشتیبان تصمیم به منظور پیش بینی تقاضا جهت طراحی شبکه پویا استوار در شرایط عدم قطعیت و تأثیر آن بر توجیه پذیری اقتصادی

محورهای موضوعی : مهندسی مالی

محمد مختاری ¹ , ابوتراب علیرضایی ² , حسن جوانشیر ³ , محمود مدیری ⁴

1 - گروه مدیریت صنعتی،دانشکده مدیریت،واحد تهران جنوب،دانشگاه آزاد اسلامی، تهران،ایران.
2 - گروه مدیریت صنعتی،دانشکده مدیریت، واحد تهران جنوب،دانشگاه آزاد اسلامی تهران،ایران.
3 - گروه صنایع،دانشکده صنایع، واحد تهران جنوب،دانشگاه آزاد اسلامی، تهران،ایران.
4 - گروه مدیریت صنعتی،دانشکده مدیریت،واحد تهران جنوب، دانشگاه آزاد اسلامی ،تهران،ایران.

تاریخ دریافت : 1398/11/13 تاریخ پذیرش : 1398/12/18 تاریخ انتشار : 1399/01/01

کلید واژه: پیش بینی, عدم قطعیت, سیستم پشتیبان تصمیم, طراحی شبکه, استوار,

چکیده مقاله :

به حداقل رسیدن هزینه های زنجیره تأمین به عنوان یکی از مسائل ضروری در فعالیت های مرتبط با پشتیبانی از جمله سیستم های برنامه ریزی مالی،فعالیت های مربوط به بازاریابی و فروش،قیمت تمام شده محصولات به چگونگی مدیریت زنجیره تأمین مجموعه‌ای از روش‌هایی که برای یکپارچه‌سازی مؤثر تأمین‌کنندگان، تولیدکنندگان، انبارها و فروشگاه‌ها به کار می‌رود،بستگی دارد تا هزینه‌های کل زنجیره تأمین به حداقل برسد و همچنین نیاز مشتریان با سطح خدمت‌رسانی بالایی برآورده شود.در این مقاله به منظور برنامه ریزی و طراحی شبکه زنجیره تامین پویا در شرایط عدم قطعیت از ابزار پیش بینی با استفاده از شبکه عصبی مصنوعی پرداخته شده است.هدف پژوهش یکی از مهمترین موارد رعایت شده در این تحقیق استفاده از متدولوژی های هوش مصنوعی مانند Grid Clustering, Subtractive Partitioning, FCM به منظور کشف الگوها و روابط بنیادی و تکنیکال موجود در داده های تاریخی استفاده شده است. برای این کار به ارائه یک شبکه عصبی مصنوعی چند لایه فازی استنتاجی مبتنی برزنتیک جهت جلوگیری از غیرتوجیه پذیری فنی و اقتصادی حل و اجرا پرداخته شده است. مدل پایه این مقاله که توسط محقق ارائه شده است به برنامه ریزی استوار و چند دوره ای برای حالت چند محصولی در شرایط عدم قطعیت می باشد.

چکیده انگلیسی:

Minimize supply chain costs as one of the essential issues in support activities such as financial planning systems,How to manage supply chain A set of ways to integrate Effective suppliers, manufacturers, warehouses and stores used to minimize total supply chain costs and meet customer service needs with a high level of service. In this study, the design of a robust cement supply chain dynamic network model was designed to reduce supply chain management costs after a crisis. Principal and efficient design of cement grid infrastructures, given the strong demand fluctuations at different times of the year, can significantly reduce financial costs on the one hand and reduce the potential for high-speed, high-cost corruption by correct prediction. Other leads.From the following tool The nose has been analyzed using artificial neural networks. The purpose of this study is to use artificial intelligence methodologies such as Grid Clustering, Subtractive Partitioning, FCM to explore fundamental and technical patterns and relationships in historical data. Used. To this end, a genetically-based inference fuzzy multilayer fuzzy neural network is introduced to prevent technical and economic unpredictability. The basic model of this paper presented by the researcher is a robust and multi-periodic planning for multi-product state under uncertainty.

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• Hasan M, Optimizing working capital and payment system through integrating financial and physical supply chain and the role of banks, (2019), Cataloged from PDF version of internship report,Includes bibliographical references (pages 23-26).


• Jahromi AT, Er MJ, Li X, Lim BS, Sequential fuzzy clustering based dynamic fuzzy neural network for fault diagnosis and prognosis, Neurocomputing,Volume 196, 5 July 2016, Pages 31-41.


• Kozma B, Barrat A, Consensus formation on adaptive networks, Physical Review E, Vol. 77, Iss. 1 ,January 2008.


• Mohammed Ali Wahdan & Mohamed Ashraf Emam (2017). The Impact of Supply Chain Management on Financial Performance and Responsibility Accounting: Agribusiness Case from Egypt. Accounting and Finance Research, Vol. 6, No. 2; 2017


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• Usher JS, Kamal AH, Kim SW, (2001), A decision support system for economic justification of material handling investments, Computers & Industrial Engineering, Volume 39, Issues 1–2, February 2001, Pages 35-47.


• Wagner, S.M , Kemmerling,R .(2014). Supply chain management executives in corporate upper echelons. Journal of Purchasing & Supply Management 20 (2014) 156–166.


• Wisner, P. (2011). Linking Supply Chain Performance to a Firm’s Financial Performance. Distinguished Lecturer The University of Tennessee. 25 may 2011.


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• Zadeh LA, Fuzzy sets, Information and Control,Volume 8, Issue 3, June 1965, Pages 338-353.

1) Soltani Tehrani, Mehdi, Mosaddekkhah, Mehdi, Hasanpour, Hossein Ali (2016), optimization of the multi-level, multi-product and multi-period closed loop supply chain with the aim of reducing supply chain costs (case study: Amad Behinesaz Company), Scientific Journal of Management Supply Chain, Volume 18, Number 53, Fall 2015, Pages 27-36.
2) Seddiqi, Ruhollah, Riahi, Mahboboh, (2016), evaluation of the effect of supply chain financial flow management on the performance of companies listed on the Tehran Stock Exchange, Journal of Experimental Studies of Financial Accounting, Volume 14, Number 56, Pages 133-154 .
3) Mohammadi, Fatemeh and Najafi, Amir (2014) Investigating the performance of supply chain financial management and its role in improving working capital and liquidity, the first international conference on accounting, management auditing and economics, Isfahan, conference secretariat.
4) Bezdek JC, Ehrlich R, Full W, FCM: The fuzzy c-means clustering algorithm, Computers & Geosciences, Volume 10, Issues 2-3, 1984, Pages 191-203.
5) Cattivelli FS, Sayed AH, Distributed Detection Over Adaptive Networks Using Diffusion Adaptation, IEEE Transactions on Signal Processing (Volume: 59, Issue: 5, May 2011), Page(s): 1917-1932.
6) Dotoli, M., Fanti, M.P., Mangini, A.M., (2007). Fuzzy multi-objective optimization for network design of integrated e-supply chains. International Journal of Computer Integrated Manufacturing 20, 588 – 601.
7) Ellinger AE, Natarajarathinam M, Adams FG, Gray JB, Hofman D, O'Marah K,(2011), Supply Chain Management Competency and Firm Financial Success, Journal of Business LogisticsVolume 32, Issue 3, Wiley Online Library.
8) Esfahanipour A, Mardani P, An ANFIS model for stock price prediction: The case of Tehran stock exchange, International Symposium on Innovations in Intelligent Systems and Applications, 2011.
9) Genovese A, Acquaye AA, Figueroa A, Koh SCL, (2017), Sustainable supply chain management and the transition towards a circular economy: Evidence and some applications, Omega, Volume 66, Part B, Pages 344-357
10) Hasan M, Optimizing working capital and payment system through integrating financial and physical supply chain and the role of banks, (2019), Cataloged from PDF version of internship report, Includes bibliographical references (pages 23-26).
11) Jahromi AT, Er MJ, Li X, Lim BS, Sequential fuzzy clustering based dynamic fuzzy neural network for fault diagnosis and prognosis, Neurocomputing, Volume 196, 5 July 2016, Pages 31-41.
12) Kozma B, Barrat A, Consensus formation on adaptive networks, Physical Review E, Vol. 77, Iss. January 1, 2008.
13) Mohammed Ali Wahdan & Mohamed Ashraf Emam (2017). The Impact of Supply Chain Management on Financial Performance and Responsibility Accounting: Agribusiness Case from Egypt. Accounting and Finance Research, Vol. 6, No. 2; 2017
14) Peng, P., Snyder, L.V., Lim, A., Liu, Z., (2011). Reliable logistics networks design with facility disruptions, Transportation Research Part B, doi:10.1016/j.trb.2011.05.022.
15) Pishvaee, M.S., Torabi, S.A., (2010). A possibilistic programming approach for closed loop supply chain network design under uncertainty. Fuzzy Sets and Systems, 161, 2668–2683.
16) Qi, L., Shen, Z-J.M., Snyder, L.V., (2010). The Effect of Supply Disruptions on Supply Chain Design Decisions. Transportation Science 44(2): 274–289.
17) Sha Y, Huang J, The multi-period location-allocation problem of engineering emergency blood supply systems, Systems Engineering Procedia, Volume 5, 2012, Pages 21-28.
18) Usher JS, Kamal AH, Kim SW, (2001), A decision support system for economic justification of material handling investments, Computers & Industrial Engineering, Volume 39, Issues 1-2, February 2001, Pages 35-47.
19) Wagner, S.M., Kemmerling, R. (2014). Supply chain management executives in corporate upper echelons. Journal of Purchasing & Supply Management 20 (2014) 156–166.
20) Wisner, P. (2011). Linking Supply Chain Performance to a Firm's Financial Performance. Distinguished Lecturer The University of Tennessee. May 25, 2011.
21) Woei, Loh Shyong. (2008), "Supply chain performance and financial success of selected companies on Bursa Malaysia", www.dspace.fsktm.um.edu.my.com
22) Zadeh LA, Fuzzy sets, Information and Control, Volume 8, Issue 3, June 1965, Pages 338-353.