بهره‌گیری از سیستم استنتاج فازی در رویکرد پویایی‌شناسی سیستم به‌منظور الگوسازیِ کسب‌وکارِ شرکت‌های پخش در ایران

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 استاد، گروه حسابداری، دانشکده اقتصاد و مدیریت و علوم اجتماعی، دانشگاه شیراز، شیراز، ایران.

2 دکتری، گروه مدیریت سیستم‌ها، دانشکده اقتصاد و مدیریت و علوم اجتماعی، دانشگاه شیراز، شیراز، ایران.

3 استادیار، گروه حسابداری، دانشکده کسب‌وکار و اقتصاد، دانشگاه خلیج فارس، بوشهر، ایران.

چکیده

هدف: پژوهش حاضر با هدف ارائه روشی برای بهره‌گیری از متغیرهای زبانی و سیستم استنتاج فازی، در رویکرد پویاشناسی سیستم‌ها، به‌منظور منعکس‌کردن شیوه تفکر ذهنی تصمیم‌گیرنده و بررسی روش ارائه‌شده در بخش خرید یک شرکت‌ پخش اجرا شده است.  
روش: با انجام مرور ادبیات در حوزه منطق فازی و پویاشناسی سیستم‌ها، نحوه اجرای کار تعیین شد. در ادامه با همکاری کارشناس مربوط به بخش خرید شرکت پخش منتخب و در دست مطالعه، متغیرهای زبانی فازی و قوانین مشخص شدند. در نهایت، الگوی سیستم استنتاج فازی در پویایی سیستم به‌دست آمد.
یافته‌ها: رویکرد ارائه‌شده می‌تواند پویایی‌های کسب‌وکار شرکت پخش مدنظر را در بخش خرید، مطابق با آنچه در عمل رخ می‌دهد منعکس کند. در انتها نیز نشان داده شد که با توجه به بازخوردهای مدل و اصلاح و تعدیل متغیرهای زبانی به‌کارگرفته شده، می‌توان به مقادیر مناسب برای تصمیم‌گیری دست یافت.
نتیجه‌گیری: رویکرد ترکیبی اجرا شده، این پتانسیل را دارد که در شبیه‌سازی موارد مشابه به‌کار گرفته شود: جایی که درک و تصمیم‌گیری عامل انسانی نقشی جدی دارد. همچنین، می‌تواند به‌سادگی از پیچیدگی‌های روابط مورد نیاز در رویکرد پویاشناسی سیستم‌ها بکاهد.

کلیدواژه‌ها

عنوان مقاله [English]

Utilization of Fuzzy Inference System in System Dynamics to Design a Business Model for Distribution Companies in Iran

نویسندگان [English]

  • Shokrollah Khajavi 1
  • Amir Sayed Alikhani 2
  • Ali Ghayouri Moghadam 3
1 Prof., Department of Accounting, Faculty of Economics and Management and Social Sciences, Shiraz University, Shiraz, Iran.
2 Ph.D., Department of Systems Management, Faculty of Economics and Management and Social Sciences, Shiraz University, Shiraz, Iran.
3 Assistant Prof., Department of Accounting, Faculty of Business and Economics of Persian Gulf University, Bushehr, Iran.
چکیده [English]

Objective: Applying the system dynamics approach in businesses requires specialized knowledge, in particular, of defining mathematical relationships among variables. This stud seeks to make the use of this approach easier by providing a method for using linguistic variables and the fuzzy inference system in the systems dynamics approach. To evaluate the ease of use and efficiency of the presented method, this method would be used to define the relationship among variables in the purchasing department of a distribution company.
Methods: To carry out this research, a literature review was first conducted in the field of fuzzy logic and system dynamics. Next, with the cooperation of an expert from the purchasing department of the distribution company under study, some fuzzy linguistic variables as well as their rules were determined. Finally, the SD model was obtained by using the fuzzy inference system.
Results: The proposed approach can reflect the business dynamics of the distribution company in accordance with what is happening in practice. According to the feedback model feedback and based on the modified linguistic variables, appropriate values were obtained for decision making. In order to evaluate the hybrid approach, a fuzzy inference system was used to calculate the purchase rate according to the two factors of inventory and base sales. These two factors were expressed through linguistic variables by the words "low", "medium", and "high", while the purchase price, as the output of the inference system, was expressed through the five words "very low", "low", "medium", "much", and "too much", according to the expert. After implementing the model, the presented approach (by modifying the fuzzy linguistic variables) was found capable of changing the output to achieve the desired results, as the expert confirmed.
Conclusion: The combined approach can be used in simulating similar cases (where human factor perception and decision-making play a significant role) and can easily reduce the complexity of the required formulas in the system dynamics approach. An important function of the hybrid approach used in this study was to model and simulate the real world in accordance with what is happening in practice.

کلیدواژه‌ها [English]

  • Fuzzy inference system
  • Fuzzy rule-based system
  • Linguistic variables
  • Systems dynamics

مراجع

عبادی ضیائی، علی؛ محقر، علی؛ آذر، عادل؛ صادقی مقدم، محمدرضا و صفری، حسین (1399). شناسایی حلقه‎های علّی مرتبط با رویکردهای متداول در مدل تعالی سازمانی مبتنی بر پویایی سیستم. مدیریت صنعتی، 12(2)، 249-270.
 
References
Ahmed, R., & Robinson, S. (2014). Modelling and simulation in business and industry: insights into the processes and practices of expert modellers. Journal of the Operational Research Society, 65(5), 660–672.
Ebadi Ziaei, A., Mohaghar, A., Azar, A., Sadeghi Moghadam, M., Safari, H. (2020). Identifying Causal Loops for Common Approaches of the EFQM Excellence Model. Industrial Management Journal, 12(2), 249-270. (in Persian)
Golroudbary, S. R., & Zahraee, S. M. (2015). System dynamics model for optimizing the recycling and collection of waste material in a closed-loop supply chain. Simulation modelling practice and theory, 53, 88-102.
Goodarzian, F., Shishebori, D., Nasseri, H., & Dadvar, F. (2021). A bi-objective production-distribution problem in a supply chain network under grey flexible conditions. RAIRO-Operations Research, 55(3), 1971-2000.
Kochan, C. G., Nowicki, D. R., Sauser, B., & Randall, W. S. (2018). Impact of cloud-based information sharing on hospital supply chain performance: A system dynamics framework. International Journal of Production Economics, 195, 168-185.
Kumar, S., Teruyuki, Y. (2007). System dynamics study of the Japanese automotive industry closed loop supply chain. Journal of management Technology Management, 18(2), 115- 138.
Mehmanpazir, F., Khalili-Damghani, K., & Hafezalkotob, A. (2022). Dynamic strategic planning: A hybrid approach based on logarithmic regression, system dynamics, Game Theory and Fuzzy Inference System (Case study Steel Industry). Resources Policy, 77, 102769.
Mendoza, G. A., & Prabhu, R. (2006). Participatory modeling and analysis for sustainable forest management: Overview of soft system dynamics models and applications. Forest Policy and Economics, 9(2), 179-196.
Metz, P. J. (1998). Demystifying supply chain management. Supply Chain Management Review, 1(4), 55-46.
Mousakhani, M., Saghafi, F., Hasanzadeh, M., & Sadeghi, M. E. (2022). Proposing dynamic model of functional interactions of iot technological innovation system by using system dynamics and fuzzy DEMATEL (No. 2206.11847).
Olivares-Aguila, J., & ElMaraghy, W. (2021). System dynamics modelling for supply chain disruptions. International Journal of Production Research, 59(6), 1757-1775.
Orji, I. J., & Wei, S. (2015). An innovative integration of fuzzy-logic and systems dynamics in sustainable supplier selection: A case on manufacturing industry. Computers & Industrial Engineering, 88, 1-12.
Rebs, T., Brandenburg, M., & Seuring, S. (2019). System dynamics modeling for sustainable supply chain management: A literature review and systems thinking approach. Journal of cleaner production, 208, 1265-1280.
Sabounchi, N. S., Triantis, K. P., Kianmehr, H., & Sarangi, S. (2019). Fuzzy rule‐based inference in system dynamics formulations. System Dynamics Review, 35(4), 310–336.
Sterman, J. D. (2000). Business dynamics: systems thinking and modeling for a complex world. Boston: Irwin/McGraw-Hill.
Sterman, J. (2018). System dynamics at sixty: the path forward. System Dynamics Review. Wiley Online Library.
Teimoury, E., Neadei, H., Ansari, S., Sabbath, M. (2013). A multi-objective analysis for import quota policy making in a perishable fruit and vegetable supply chain: A system dynamics approach. Computers and Electronics in Agriculture, (93), 45-37.
Towill, D. R. (1996). Time compression and supply chain management: a guided tour. Supply Chain Management, (1), 27-15.
Usenik, J., & Turnsek, T. (2013). Modeling conflict dynamics with fuzzy logic inference. Journal of US-China Public Administration, 10(5), 457–474.
Yasarcan, H. (2011). Information Sharing in Supply Chains: A Systemic Approach. 29th International Conference of the System Dynamics Society, Washington DC, USA, (1), 4042- 4060.
Youssefi, H., Nahaei, V., & Nematian, J. (2011). A new method for modeling system dynamics by fuzzy logic: Modeling of research and development in the national system of innovation. Journal of Mathematics and Computer Science, 2(1), 88–99.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3), 199–249.
مدیریت صنعتی
دوره 14، شماره 2
1401
صفحه 250-266

فایل ها

هم رسانی

ارجاع به این مقاله

آمار