Steel Supply Chain Complexity and Resilience Assessment: Interactive Network Mapping, Graph Theory, and Agent-Based Simulation
الموضوعات :
Mohammad Hossein Sadat Hosseini Khajouei
1
,
Nazanin Pilevari
2
,
Reza Radfar
3
,
Ali Mohtashami
4
1 - Department of Management and Economics Islamic Azad University Science and Research Branch Tehran Iran.
2 - Department of industrial management college of management and accounting Islamic Azad university West Tehran Branch Tehran Iran
3 - Islamic azad university. science and research branch
4 - Department of Industrial Engineering & Management Islamic Azad University Qazvin Branch Qazvin Iran.
الکلمات المفتاحية: Agent-Based Modeling, Complex Adaptive System, Network Analysis, Resilience, Steel Supply Chain,
ملخص المقالة :
Complexity is seen as a major challenge in supply chain research because of interactions, interdependencies, and uncertainties. The main purpose of this study is to develop agent-based models and simulations that focus on Steel Supply Chain Resilience (SSCR) based on complex adaptive network analysis and graph theory. Before and after the simulation, and by mapping the Iranian SSC to the network in the Gephi environment (0.9.2), we used graph theory to analyze node-level and network-level indices. This hypothesis was tested that the corresponding network of the target SSC contained a Complex Adaptive System (CAS). Agent Based Modeling (ABM) has been proposed as a way to track Iran's steel industry supply chain behavior during the crisis using NetLogo (6.2.0). BehaviorSpace, as NetLogo's integrated software tool, was selected for the proposed parameter sweep, design, and experiment execution of agent-based modeling. For sensitivity analysis, the output files were taken from two types of spreadsheets and six scenarios in the table for XLSTAT statistical analysis.
[1] Callender, A.K., (2007). “The Mechanistic/Vitalistic Dualism of Chiropractic and General Systems Theory: Daniel D. Palmer and Ludwig von Bertalanffy”. Journal of Chiropractic Humanities, 14, 1-21. https://doi.org/10.1016/S1556-3499(13)60158-8.
[2] Mittelstrass, J., (2014). “Complexity, Reductionism, and Holism in Science and Philosophy of Science”, Complexity and Analogy in Science. Pontifical Academy of Sciences, Acta22, Vatican City 2014. https://doi.org/10.1007/978-3-319-04382-1_14.
[3] Chaudhry, Q.A. (2016). “An Introduction to Agent-Based Modeling: Modeling Natural,social, and engineered complex systems with netlogo”. Complex Adapt Syst Model 4, 11. https://doi.org/10.1186/s40294-016-0027-6.
[4] Harland, C.M., Lamming, R.C., Cousins, P.D., (1999). “Developing the concept of supply strategy”. International Journal of Operations & Production Management, 19 (7), 650–673. https://doi.org/10.1108/01443579910278910.
[5] Choi, T., Dooley, K.J., Rungtusanatham, M. (2001), “Supply networks and complex adaptive systems: control versus emergence”, Journal of Operations Management, 19 (3), 351-366. https://doi.org/10.1016/S0272-6963(00)00068-1.
[6] Surana, A., Kumara, S., Greaves, M. and U.N., Raghavan (2005). “Supply-chain networks: a complex adaptive systems perspective”, International Journal of Production Research, 43(20), 4235–4265. http://dx.doi.org/10.1080/00207540500142274.
[7] Borgatti, Stephen & Li, Xun. (2009). “On Social Network Analysis in a Supply Chain Context”. Journal of Supply Chain Management. 45, 5 - 22. https://doi.org/10.1111/j.1745-493X.2009.03166.x.
[8] Cook KS, Emerson RM, Gillmore MR, Yamagishi T (1983). “The distribution of power in exchange networks: theory and experimental results”. Am J Soc, 89, 275–305. https://doi.org/10.1086/227866.
[9] Ponnambalam, L., Long, D.H., Sarawgi, D., Fu, X., and R.S.M., Goh, (2014). “Multi-Agent Models to Study the Robustness and Resilience of Complex Supply Chain Networks”, 2014 International Conference on Intelligent Autonomous Agents, Networks and Systems Bandung, Indonesia. https://doi.org/10.1109/inagentsys.2014.7005717.
[10] Perera, S., Perera, H.N., and D., Kasthurirathna, (2017). “Structural characteristics of complex Supply Perspective.” Journal of Operations Management, 33-34, 43–59. https://doi.org/10.1109/mercon.2017.7980470.
[11] Modrak, V., and S., Bednar, (2016). “Topological Complexity Measures of Supply Chain Networks”, Procedia CIRP, 40, 295 – 300. https://doi.org/10.1016/j.procir.2016.01.041.
[12] Wang, H., Gu, T., Jin, M., Zhao, R. and G., Wang (2018). “The complexity measurement and evolution analysis of supply chain network under disruption risks”, Chaos, Solitons and Fractals, 116, 72–78. http://dx.doi.org/10.1016/j.chaos.2018.09.018.
[13] Macal, C.M., (2018). “Tutorial on agent-based modeling and simulation: ABM design for the Zombie Apocalypse”, Proceedings of the 2018 Winter Simulation Conference. https://doi.org/ 10.1109/WSC.2018.8632240.
[14] Macal, C.M., and M.J., North, (2005a). “Tutorial on agent-based modeling and simulation”, Proceedings of the 2006 Winter Simulation Conference, IEEE, https://doi.org/10.1109/wsc.2005.1574234.
[15] Macal, C.M., and M.J., North, (2006b). “Tutorial on agent-based modeling and simulation part 2: How to model with agents”, Proceedings of the 2006 Winter Simulation Conference, 1(5), 73-83. https://doi.org/10.1109/wsc.2006.323040.
[16] Macal, C.M., and M.J., North, (2007c). “Agent-based modeling and simulation: Desktop ABMS”, Proceedings of the 2007 Winter Simulation Conference, 95-106, IEEE. https://doi.org/10.1109/wsc.2007.4419592.
[17] Macal, C.M., and M.J., North, (2010d). “Tutorial on agent-based modelling and simulation”, Journal of Simulation, 4(3), 151-162. https://doi.org/10.1057/jos.2010.3.
[18] Macal, C.M., and M.J., North, (2011e). “Introductory Tutorial: Agent-Based Modeling and Simulation”, Proceedings of the 2011 Winter Simulation Conference (Wsc), 1451-1464. https://doi.org/10.1109/wsc.2011.6147864.
[19] Macal, C.M., and M.J., North, (2013f). “Introductory Tutorial: Agent-Based Modeling and Simulation”, 2013 Winter Simulation Conference (Wsc), 362-376. https://doi.org/10.1109/wsc.2013.6721434.
[20] Macal, C.M., and M.J., North, (2014g). “Introductory Tutorial: Agent-Based Modeling and Simulation”, Proceedings of the 2014 Winter Simulation Conference (Wsc), 6-20. https://doi.org/10.1109/wsc.2014.7019874.
[21] Pettit, T. J., K. L. Croxton, and J. Fiksel. 2013. “Ensuring Supply Chain Resilience: Development and Implementation of an Assessment Tool.” Journal of Business Logistics 34 (1): 46–76. http://dx.doi.org/10.1111/jbl.12009.
[22] Ponomarov, S., and M. Holcomb. 2009. “Understanding the Concept of Supply Chain Resilience.” The International Journal of Logistics Management 20 (1), 124–143. http://dx.doi.org/10.1108/09574090910954873.
[23] Kim, Y., Y.-S. Chen, and K. Linderlan. 2015. “Supply Network Disruption and Resilience: A Network Structural”, Journal of operations management, 33(34), 43-59. https://doi.org/10.1016/j.jom.2014.10.006.
[24] Yuan Yao & Nathalie Fabbe-Costes (2018). “Can you measure resilience if you are unable to define it? The analysis of Supply Network Resilience (SNRES)”, Supply Chain Forum: An International Journal, 19 (4), 255-265, http://dx.doi.org/10.1080/16258312.2018.1540248
[25] Sauser, B., Baldwin, C., Pourreza, S., Randall, W., and D., Nowicki, (2018). “Resilience of small- and medium-sized enterprises as a correlation to community impact: an agent-based modeling approach”, Natural Hazards, 90(1), https://link.springer.com/article/10.1007/s11069-017-3034-9.
[26] Suk Na, H. and A., Banerjee (2019). “Agent-based discrete-event simulation model for no-notice natural disaster evacuation planning”, Computers & Industrial Engineering, 129, 44-55. http://dx.doi.org/10.1016/j.cie.2019.01.022.
[27] Delcea, C., and L.A., Cotfas, (2019). “Increasing awareness in classroom evacuation situations using agent-based modeling”, Physica A, 523, 1400–1418. https://doi.org/10.1016/j.ssci.2024.106520.
[28] Rozo, K.R. Arellana, J., Santander-Mercado, A., and M. Jubiz-Diaz (2019), “Modelling building emergency evacuation plans considering the dynamic behaviour of pedestrians using agent-based simulation”, Safety Science, 113, 276-284, https://doi.org/10.1016/j.ssci.2018.11.028.
[29] Fan, C., Gong, L., and H., Li, (2019). “An agent-based model approach for assessing tourist recovery strategies after an earthquake: A case study of Jiuzhai Valley”, Tourism Management, 75, 307–317. https://doi.org/10.1016/j.tourman.2019.05.013.
[30] Yang, Q., Gruenbacher, D., Heier Stamm, J.L., Brase, G.L., DeLoach, S.A., Amrine, D.A. and C., Scoglio (2019). “Developing an agent-based model to simulate the beef cattle production and transportation in southwest Kansas”, Physica A, 526, 120856. http://dx.doi.org/10.1016/j.physa.2019.04.092.
[31] Wang, Z. and J., Zhang (2019). “Agent-based evaluation of humanitarian relief goods supply capability”, International Journal of Disaster Risk Reduction, 36, 101-105. http://dx.doi.org/10.1016/j.ijdrr.2019.101105.
[32] Sadat Hosseini Khajouei, M.H., Pilevari, N., Radfar, R., Mohtashami, A., (2021). "Complex adaptive systems, agent-based modeling and supply chain network management: A systematic literature review", Journal of Industrial Engineering and Management Studies, 8(2), 54-92. https://doi.org/10.22116/jiems.2021.251146.1388.
[33] Liu, Z., Rexachs, D., Epelde, F., and E., Luque, (2017). “A simulation and optimization based method for calibrating agent-based emergency department models under data scarcity”, Computers & Industrial Engineering,103, 300–309. https://doi.org/10.1016/j.cie.2016.11.036.
[34] Sandhu, M.A., Helo, P., and Y., Kristianto (2019). “Steel supply chain management by simulation modelling”, Benchmarking: An International Journal, 20, 45-61. http://dx.doi.org/10.1108/14635771311299489.
[35] Sadat Hosseini Khajouei, M. H., Pilevari, N., Radfar, R., Mohtashami, A. (2023). “Iran’s Steel Chain Interactive Network Mapping”. International Journal of Iron & Steel Society of Iran, 20(1): 35-43. Https://doi.org/10.22034/ijissi.2024.2010492.1271.
[36] Scott J, Carrington P. (2014). “The SAGE Handbook of Social Network Analysis [Internet]”. SAGE Publications Ltd; http://dx.doi.org/10.4135/9781446294413.
[37] Perez C, Germon R. (2016). “Graph Creation and Analysis for Linking Actors: Application to Social Data”. Automating Open Source Intelligence, ISBN 9780128029169, 103-129. https://doi.org/10.1016/B978-0-12-802916-9.00007-5.
[38] Chen Y, Wang J, Jin F (2020). “Robustness of China’s air transport network from 1975 to 2017”, Physica A: Statistical Mechanics and its Applications, 539: 122876. https://doi.org/10.1016/j.physa.2019.122876.
[39] Cheung T.Y.K., Wong C.W.H., Zhang A, (2020). “The evolution of aviation network: Global airport connectivity index 2006–2016”, 133:101826. https://doi.org/10.1016/j.tre.2019.101826.
[40] Bombelli A, Santos B.F., Tavasszy L. (2020). “Analysis of the air cargo transport network using a complex network theory perspective”, Transportation Research Part E: Logistics and Transportation Review, 138: 101959, https://doi.org/10.1016/j.tre.2020.101959.
[41] Magaia N, Francisco A.P., Pereira P, Correia M, (2015). “Betweenness centrality in Delay Tolerant Networks: A survey”, Ad Hoc Networks, 33: 284-305, https://doi.org/10.1016/j.adhoc.2015.05.002.
[42] Broido A.D., Clauset, A., (2019). “ Scale-free networks are rare”, Nature Communication. 10, 1017. https://doi.org/10.1038/s41467-019-08746-5.
[43] Limpert E, Stahel W, (2017). “ The log-normal distribution”. Significance, 14: 8–9. https://doi.org/10.1111/j.1740-9713.2017.00993.x.
