مدل‌سازی عامل بنیان پایداری اقتصادی زنجیره‌تأمین محصولات کشاورزی با درنظر گرفتن کشاورزی قراردادی، تنوع اقلیمی و مکانیزم پیشنهاد قیمت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی صنایع، گروه مهندسی صنایع ، داتشکدۀ فنی مهندسی، دانشگاه یزد، یزد، ایران

2 دانشیار گروه مهندسی صنایع ، داتشکدۀ فنی مهندسی، دانشگاه یزد، یزد، ایران

3 استاد گروه مهندسی صنایع ، داتشکدۀ فنی مهندسی، دانشگاه یزد، یزد، ایران

چکیده

امروزه مدل‌سازی عامل بنیان باتوجه‌به ماهیت هوشمندی و استقلال عوامل تشکیل‌دهنده به یک ابزار مؤثر برای مدل‌سازی و ارزیابی سیستم‌های پیچیده تبدیل شده است. این سیستم‌های پیچیده رفتارهایی از خود بروز می‌دهد که از رفتار اجزاء به‌تنهایی قابل استنتاج نیست و هربار تجربه سیستم ممکن است به نتایج متفاوتی منجر شود. در این مطالعه زنجیره‌تأمین محصولات کشاورزی (ASC)، به‌عنوان نمونه‌ای از یک سیستم پیچیده متأثر از الگوهای رفتاری غیرقابل ‌پیش‌بینی فردی عامل‌ها در زنجیره بررسی می‌شود. هدف ما مدل‌سازی این سیستم پیچیده و ارزیابی نقش سیاست‌های هماهنگی کشاورزان (حق بیمه و قیمت کشاورزان قراردادی)، نقش اثرپذیری تصمیمات عامل‌ها از یکدیگر و عدم قطعیت اقلیمی و بر پایداری اقتصادی زنجیره است. عوامل این مطالعه شامل کشاورزان، عمده‌فروشان و فروشندگان هستند که این عوامل به‌طور مستقل به‌دنبال دستیابی به اهداف فردی خود در ارتباط با سایر عوامل هستند و برای تولید، توزیع و تجارت محصولات زراعی با یکدیگر ارتباط و رقابت دارند. قیمت محصولات در یک مکانیزم رقابتی تعیین می‌شود. کشاورزان برای کسب منافع خود می‌توانند برای انتخاب محصول و عمده‌فروشان و کشاورزان برای بهره‌گیری از هماهنگی با سایر کشاورزان تصمیم‌گیری می‌کنند. در نهایت فروشندگان به‌دنبال تأمین تقاضای خود با کمترین هزینه‌اند. نتایج تحلیل آماری نشان داد که با کاهش جذابیت سیاست‌های هماهنگی در زنجیره، کشاورزان به‌تدریج در نوسانات قیمتی ناشی از تأثیر عدم‌قطعیت‌های وجود در بازار، منابع مالی خود را از دست می‌دهند. همچنین این نتایج نشان داد که ایجاد قیمت‌های حمایتی و اثرات الگوهای رفتاری بر پایداری قیمت در ASC مؤثر است.

کلیدواژه‌ها


عنوان مقاله [English]

Assessing the Economic Sustainability of the Agricultural Supply Chain by Considering Contract Farming, Climatic Diversity, and Price Bidding Mechanism; An Agent-based Modeling

نویسندگان [English]

  • Amir Hajimirzajan 1
  • Mohammadali Vahdat 2
  • Ahmad Sadegheih 3
1 PhD student in Industrial Engineering, Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
2 Associate Professor, Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
3 Professor, Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
چکیده [English]

Today, due to the intelligent nature of each agent, agent-based simulation has become an effective tool for predicting many complex systems between independent agents. These complex systems exhibit behaviors that cannot be inferred from the behavior of the components alone, and each experience of the system may lead to different results.In this study, the Agricultural Supply Chain (ASC) is examined as one of these systems in which agents try to make the best decisions to maximize their benefits through learning from the environment. Agents of this study include farmers, wholesalers, and sellers who independently seek to achieve their individual goals in competing with other agents. The price of crops is determined in a competitive bidding price mechanism. Each farmer can allocate his resources to cultivate a particular crop based on his own and other neighboring farmers experience. They can also become a contract farmer with the nearest wholesaler. Wholesalers decide on a similar mechanism for their contract operation. Eventually, sellers try to meet their demand at the lowest cost. The statistical analysis results showed that as the attractiveness of conventional agriculture in the supply chain decreases, they gradually lose their financial resources and go bankrupt in price fluctuations due to the impact of uncertainties in the market and the environment. These results also showed that the creation of supportive prices and the effects of behavioral and social patterns of agents play an important role in price stability and control of fluctuations in ASC.

کلیدواژه‌ها [English]

  • Agri-Food Supply Chain
  • Contract Farming
  • Farmers coordination
  • Simulation
  • Agent-Based modeling
[1] Grösser, S. N. (2017). Complexity management and system dynamics thinking Dynamics of Long-Life Assets (pp. 69-92): Springer, Cham.
[2] Krejci, C. C. (2014). Complex Adaptive Food Supply Systems.
[3] Higgins, A., Miller, C., Archer, A., Ton, T., Fletcher, C., & McAllister, R. Challenges of operations research practice in agricultural value chains. Journal of the Operational Research Society, 61(6), 964-973. (2010)
[4] Chebolu-Subramanian, V., & Gaukler, G. M. Product contamination in a multi-stage food supply chain. European Journal of Operational Research, 244(1), 164-175. (2015)
[5] Busby, J. S., Onggo, B. S., & Liu, Y. Agent-based computational modelling of social risk responses. European Journal of Operational Research, 251(3), 1029-1042. (2016)
[6] Krejci, C., & Beamon, B. Impacts of farmer coordination decisions on food supply chain structure. Journal of Artificial Societies and Social Simulation, 18(2), 19. (2015)
[7] Macal, C. M., & North, M. J. (2005). Tutorial on agent-based modeling and simulation. Paper presented at the Proceedings of the Winter Simulation Conference, 2005.
[8] Crooks, A. T., & Heppenstall, A. J. (2012). Introduction to agent-based modelling Agent-based models of geographical systems (pp. 85-105): Springer.
[9] Luna, F., & Stefansson, B. (2000). Economic Simulations in Swarm: Agent-Based Modelling and Object Oriented Programming: Agent-Based Modelling and Object Oriented Programming (Vol. 14): Springer Science & Business Media.
[10] Gustafsson, L., & Sternad, M. Consistent micro, macro and state-based population modelling. Mathematical biosciences, 225(2), 94-107. (2010)
[11] Gómez-Cruz, N. A., Saa, I. L., & Hurtado, F. F. O. Agent-based simulation in management and organizational studies: a survey. European Journal of Management and Business Economics. (2017)
[12] Thunem, A.-J. (2009). A survey of organization studies relevant to safety and dependability Reliability, Risk, and Safety, Three Volume Set (pp. 789-794): CRC Press.
[13] Bichraoui, N., Guillaume, B., & Halog, A. Agent-based modelling simulation for the development of an industrial symbiosis-preliminary results. Procedia Environmental Sciences, 17, 195-204. (2013)
[14] Macal, C. M. Agent-based modeling and artificial life. Complex Social and Behavioral Systems: Game Theory and Agent-Based Models, 725-745. (2020)
[15] Crooks, A., Heppenstall, A., & Malleson, N. Agent-based modeling. (2018)
[16] Niazi, M., & Hussain, A. Agent-based computing from multi-agent systems to agent-based models: a visual survey. Scientometrics, 89(2), 479-499. (2011)
[17] Long, Q. Data-driven decision making for supply chain networks with agent-based computational experiment. Knowledge-Based Systems, 141, 55-66. (2018)
[18] Rubinstein, A., & Dalgaard, C.-j. (1998). Modeling bounded rationality: MIT press.
[19] Simon, H. A. Bounded rationality and organizational learning. Organization science, 2(1), 125-134. (1991)
[20] Simon, H. A. (1997). Models of bounded rationality: Empirically grounded economic reason (Vol. 3): MIT press.
[21] Gigerenzer, G., & Selten, R. (2002). Bounded rationality: The adaptive toolbox: MIT press.
[22] Todd, P. M., & Gigerenzer, G. Bounding rationality to the world. Journal of Economic Psychology, 24(2), 143-165. (2003)
[23] Hajimirzajan, A., Vahdat, M., Sadegheih, A., Shadkam, E., & El Bilali, H. An integrated strategic framework for large-scale crop planning: sustainable climate-smart crop planning and agri-food supply chain management. Sustainable Production and Consumption, 26, 709-732. (2021)
[24] Utomo, D. S., Onggo, B. S., & Eldridge, S. Applications of agent-based modelling and simulation in the agri-food supply chains. European Journal of Operational Research, 269(3), 794-805. (2018)
[25] Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo: Mit Press.
[26] Borodin, V., Bourtembourg, J., Hnaien, F., & Labadie, N. Handling uncertainty in agricultural supply chain management: A state of the art. European Journal of Operational Research, 254(2), 348-359. (2016)
[27] Jagustović, R., Zougmoré, R. B., Kessler, A., Ritsema, C. J., Keesstra, S., & Reynolds, M. Contribution of systems thinking and complex adaptive system attributes to sustainable food production: Example from a climate-smart village. Agricultural Systems, 171, 65-75. (2019)
[28] Key, N. How much do farmers value their independence? Agricultural Economics, 33(1), 117-126. (2005)
[29] Handayati, Y., Simatupang, T. M., & Perdana, T. Agri-food supply chain coordination: the state-of-the-art and recent developments. Logistics Research, 8(1), 1-15. (2015)
[30] Hajimirzajan, A., Pirayesh, M., & Dehghanian, F. Developing a Supply Chain Planning Model for Perishable Crops. Journal of Production and Operations Management, 6(1), 35-60. (2015)
[31] Haji-Mirzajan, A., Pirayesh-Neghab, M., & Faal, F. (2013). Introducing dynamic supply chain model for agricultural products with quality consideration. Paper presented at the Proceeding of the Nineth International Conference on Industrial Engineering, Khajeh Nasir al-Din Tusi University of Technology, Faculty of Industrial Engineering, Tehran, Iran.
[32] Kremmydas, D., Athanasiadis, I. N., & Rozakis, S. A review of agent based modeling for agricultural policy evaluation. Agricultural Systems, 164, 95-106. (2018)
[33] Huber, R., Bakker, M., Balmann, A., et al. Representation of decision-making in European agricultural agent-based models. Agricultural Systems, 167, 143-160. (2018)
[34] Krejci, C. C., & Beamon, B. M. (2012). Modeling food supply chains using multi-agent simulation. Paper presented at the Proceedings of the 2012 Winter Simulation Conference (WSC).
[35] Nguyen, H. K., Chiong, R., Chica, M., Middleton, R., & Pham, D. T. K. Contract Farming in the Mekong Delta's Rice Supply Chain: Insights from an Agent-Based Modeling Study. Journal of Artificial Societies and Social Simulation, 22(3). (2019)
[36] Ng, D. W. Structural change in a food supply chain. International Food and Agribusiness Management Review, 11(1030-2016-82711), 17-48. (2008)
[37] He, Z., Wang, S., & Cheng, T. Competition and evolution in multi-product supply chains: An agent-based retailer model. International Journal of Production Economics, 146(1), 325-336. (2013)
[38] Ross, R. B., & Westgren, R. E. An agent‐based model of entrepreneurial behavior in agri‐food markets. Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, 57(4), 459-480. (2009)
[39] Ross, R. B. Entrepreneurial behavior in agri-food supply chains: the role of supply chain partners. Journal on Chain and Network Science, 11(1), 19-30. (2011)
[40] Krejci, C. C., Stone, R. T., Dorneich, M. C., & Gilbert, S. B. Analysis of food hub commerce and participation using agent-based modeling: integrating financial and social drivers. Human factors, 58(1), 58-79. (2016)
[41] Jansson, J. O. (2013). The economics of services: Microfoundations, development and policy: Edward Elgar Publishing.
[42] Wilensky, U. (1999). NetLogo (Version 6.2). Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Retrieved from http://ccl.northwestern.edu/netlogo/
[43] Alfi, V., Cristelli, M., Pietronero, L., & Zaccaria, A. Minimal agent based model for financial markets II. The European Physical Journal B, 67(3), 399-417. (2009)
[44] HE, Y.-b., & CAI, W.-m. Linking a farmer crop selection model (FCS) with an agronomic model (EPIC) to simulate cropping pattern in Northeast China. Journal of integrative agriculture, 15(10), 2417-2425. (2016)
[45] Chang, X., Li, J., Rodriguez, D., & Su, Q. Agent-based simulation of pricing strategy for agri-products considering customer preference. International Journal of Production Research, 54(13), 3777-3795. (2016)
[46] Handayati, Y., Simatupang, T. M., Perdana, T., & Siallagan, M. A simulation of contract farming using agent based modeling. Journal of Operations and Supply Chain Management, 9(2), 28-48. (2016)
[47] Vattam, S. S., Goel, A. K., & Rugaber, S. (2011). Behavior patterns: Bridging conceptual models and agent-based simulations in interactive learning environments. Paper presented at the 2011 IEEE 11th International Conference on Advanced Learning Technologies.