Agent-based Simulation of National Oil Products Distribution Company’s Supply Network in the Framework of a Complex Adaptive System in Order to Achieve an Optimal Inventory Level

Document Type : Research Paper


1 Assistant Prof., Department of Industrial Management, Islamic Azad University, Rasht Branch, Rasht, Iran.

2 Assistant Prof., Department of Administrative Management, Islamic Azad University, Rasht Branch, Rasht, Ir

3 Assistant Prof., Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

4 M.A., Department of Executive Management, Faculty of Management and Accounting, Islamic Azad University, Rasht Branch, Rasht, Iran


Objective: One of the most important challenges of supply chains is the coordination of inventory policies among supply chain elements including suppliers, manufacturers, and distributors. Accordingly, the current study aims to investigate the achievement of the elements of the petrol distribution system to the optimal level of inventory.
Methods: At first, the supply network of National Guilan Oil Products Distribution Company is defined as a complex adaptive system and then, this network is simulated using agent-based modeling. The core component of this simulation consists of interactions between agents or members of the supply network in the context of inventory management based on the economic order quantity (EOQ).
Results: The results of simulation in Net Logo software showed that agent-based modeling of the network in the form of a complex adaptive system leads to better understanding of the behavior of supply chain agents in their achievement to the optimal inventory level and enables them to get the proper estimate of the economic order quantity, re-order point and total cost.
Conclusion: In summary, it can be seen that the agents in the supply chain have the ability to provide their customers’ needs and will not face lost sales as long as they do not pay extra inventory costs.


Azar, A. & Alimohammadlou, M. (2007). Supply Chain Inventory Management: A Mathematical Modeling. Quarterly Journal of MRI, 11 (3), 1-28. (in Persian)
Chatfield, D. C., Harrison, T. P. & Hayya, J. C. (2006). SISCO: an object-oriented supply chain simulation system. Decision Support Systems, 42(1), 422–434.
Chatfield, D. C., Hayya, J. C. & Cook, D. P. (2013). Stockout propagation and amplification in supply chain inventory systems. International Journal of Production Research, 51(5), 1491–1507.
Choi, T. Y., Dooley, K. J. & Rungtusanatham, M. (2001). Supply networks and complex adaptive systems: Control versus emergence. Journal of Operations Managemen, 19, 351-366.
Costas, J., Ponte, B., de la Fuente, D., Pino, R. & Puche, J. (2015). Applying Goldratt’s Theory of Constraints to reduce the Bullwhip Effect through agent-based modeling. Expert Systems with Applications, 42 (4), 2049–2060.
Dominguez, R., Cannella, S., Framinan, J. M. (2015). SCOPE: a multi-agent system tool for supply chain network analysis. Proceedings of the EUROCON 2015-international conference on computer as a tool, 1–15.
Eisenhardt, K. M. & Piezunka, H. (2011). Complexity theory and corporate strategy. In P. Allen, S. Maguire, & B. McKelvey (Eds.). The SAGE handbook of complexity andmanagement (pp. 506–523). Thousand Oaks, CA: SAGE Publications.
Fox, M. S., Chionglo, J. F. & Barbuceanu, M. (1993). The integrated supply chain management system. Department of Industrial Engineering (University of Toronto): Internal Report.
Fu-gui, D. (2012). Agent-based Simulation Model of Single Point Inventory System. Systems Engineering, 4, 298-304.
Gerber, A., Russ, C. & Klusch, M. (2003). Supply web co-ordination by an agent-based trading network with integrated logistics services. Electronic Commerce Research and Applications, 2 (2), 133-146.
Giannoccarra, L. (2002). Inventory management in supply chains: reinforcementlearning approach. International Journal of Production Economics, 78, 153-161.
Gilbert, N. (2008). Agent-based models. London, Sage Publications.
Haghnevis, M., Askin, R. G. & Armbruster, D. (2016). An agent-based modeling optimization approach for understanding behavior of engineered complex adaptive systems. Socio-Economic Planning Sciences, 56, 67-87.
Haj Shir Mohammadi, A. (2014). The principals of inventory and production planning and control. Arken-e Danesh Publication, Isfahan. (in Persian)
Haji, R. Moarefdoost, M. M. & Ebrahimi, S. B. (2010). Finding the Cost of Inventory in Make to Order Supply Chain under Vendor Managed Inventory Program. Journal of Industrial Management, 1 (3), 21-36. (in Persian)
Jafarnejad, A. & Amouzad Mahdirji, H. (2013). Designing and controlling the supply chain: A quantitative approach. Mehraban Publication, Tehran. (in Persian)
Lau, H. C. Agussurja, L. & Thangarajoo, R. (2008). Real-time supply chain control via multiagent adjustable autonomy. Computers & Operations Research, 35 (11), 3452–3464.
Li, G., Yang, H., Sun, L., Ji, P. & Feng, L. (2010). The evolutionary complexity of complex adaptive supply networks: A simulation and case study. International Journal of Production Economics, 124 (2), 310-330.
Liang, W. Y. & Huang, C. C. (2006). Agent-based demand forecast in multi-echelon supply chain. Decision Support Systems, 42 (1), 390-407.
McCarthy, I. P. (2004). Manufacturing strategy: Understanding the fitness landscape. International Journal of Operations & Production Management, 24 (2), 124-150.
Nilsson, F. & Darely, V. (2006). On complex adaptive systems and agent-based modelling for improving decision-making in manufacturing and logistics settings Experiences from a packaging company. International Journal of Operations & Production Management, 26 (12), 1351-1373.
North, M. Macal, C. & Campbell, P. (2005). Oh behave! Agent-based behavioral representations in problem solving environments. Future Generation Computer Systems, 21 (7), 1192-1198.
Onik, M. F. A. Fielt, E. G. & Gable, G. G. (2016). Complex adaptive systems theory in information systems research: A systematic literature review. Twenty-Fourth European Conference on Information Systems (ECIS), İstanbul, Turkey.
Pathak, S. D., Day, J. M., Nair, A., Sawaya, W. J. & Kristal, M. M. (2007). Complexity and Adaptivity in Supply Networks: Building Supply Network Theory Using a Complex Adaptive Systems Perspective. Decision Sciences, 38 (4), 547-580.
Ponte, B., Sierra, E., de la Fuente, D. & Lozano, J. (2017). Exploring the interaction of inventory policies across the supply chain: An agent-based approach. Computers & Operations Research, 78, 335–348.
Roundy, P. T., Bradshaw, M. & Brockman, B. K. (2018). The emergence of entrepreneurial ecosystems: A complex adaptive systems Approach. Journal of Business Research, 86, 1–10.
Saebi, A. R. & Hashemi Golpayegani, S. A. R. (2010). Agent based supply chain management (A review on intelligent agents and supply chain management). Naghoos Press, Tehran. (in Persian)
Sargent, R. G. (2007). Verification and validation of simulation models. Simulation conference, Washington, DC, USA.
Smith, E. R. & Semin, G. R. (2004). Socially situated cognition: Cognition in its social context. Advances in Experimental Social Psychology, 36, 53-117.
Strader, T. J., Lin, F. R. & Shaw, M. J. (1998). Simulation of order fulfillment in divergent assembly supply chains. Journal of Artificial Societies and Social Simulation, 1(2), 1-5.
Teimouri, A. & Ahmadi, M. (2009). Supply chain management. Publication of Iran University of science & Technology, Tehran. (in Persian)
Vakili, M. R., Nori, S., Yaghobi, S. (2016). An Inventory– Scheduling Model for Supply Chain of Construction Project. Journal of Industrial Management, 8 (1), 113-140. (in Persian)
Wacker, J. G. (1998). A definition of theory: research guidelines for different theory-building research methods in operations management. Journal of Operations Management, 16, 361–385.
Wooldridge, M. (2009). An introduction to multi agent systems. 2nd edition. John Wiley and Sons Ltd, London.
Wycisk, C. (2008). “Smart parts” supply networks as complex adaptive systems: analysis and implications. International Journal of Physical, Distribution & Logistics Management, 38, 108-125.
Ye, W. & You, F. (2016). A computationally efficient simulation-based optimization methodwith region-wise surrogate modeling for stochastic inventorymanagement of supply chains with general networkstructures. Computers and Chemical Engineering,87 (2016) 164–179.
Zhou, Y. Guo, S. Xu, C. Y. Liu, D. Chen, L. & Ye, Y. (2015). Integrated optimal allocation model for complex adaptive system of water resources management (I): Methodologies. Journal of Hydrology, 531, 964–976.
Zhu, F. Yao, Y. Tang, W. & Tang, J. (2017). A hierarchical composite framework of parallel discrete event simulation for modelling complex adaptive systems. Simulation Modelling Practice and Theory, 77, 141–156.