Abstract
Based on the modelling steps discussed in Part I, this chapter aims to present ways in which agent-based simulation models of supply chains can be developed and used to improve the performance of these systems in both normal and abnormal situations. An industrial supply chain with a network of several independent companies is a good example of a socio-technical system. The physical and social networks of the actors involved in their operation collectively form an interconnected, complex system in which the actors determine the development and operation of the physical network and, likewise, the physical network affects the behaviour of the actors. In this type of system, the many interactions taking place in the social and physical subsystems can result in the complex, dynamic behaviour of the supply chain as a whole. Accordingly, any attempt to improve the functioning of the supply chain requires a comprehensive understanding of this behaviour under different supply network configurations. Most of the current approaches to the modelling and simulation of supply chains do not capture the rich socio-technical dynamics present. The agent-based modelling approach, however, seems to be very promising as a means to address this complex behaviour. To demonstrate its applicability, we will present agent-based simulation models for two different industrial supply chains: an oil refinery and a multi-plant chemical enterprise. Using the models described in this chapter, the outcomes of the system under a broad range of possible agent behavioural rules and environmental events can be explored, and improved levels of system functioning can be identified.
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Notes
- 1.
RMs stands for Raw Materials.
- 2.
For simplicity, it is assumed that the magnitude of the disturbance is known as soon as the disturbance occurs. In reality, this may involve uncertainty. Furthermore, currently delays are in the order of magnitude of days, but the granularity could be adjusted so that a delay could be expressed in parts of a day (e.g. hours) instead of full days of 24Â hours.
- 3.
kbbl stands for 1000 standard oil barrels.
References
Behdani, B., Lukszo, Z., Adhitya, A., & Srinivasan, R. (2010a). Agent-based modelling to support operations management in a multi-plant enterprise. International Journal of Innovative Computing, Information and Control, 6(7), 1–12.
Behdani, B., Lukszo, Z., Adhitya, A., & Srinivasan, R. (2010b). Performance analysis of a multi-plant speciality chemical manufacturing enterprise using an agent-based model. Computers & Chemical Engineering, 34(5), 793–801.
Bhatnagar, R., Chandra, P., & Goyal, S. K. (1993). Models for multi-plant coordination. European Journal of Operational Research, 67(2), 141–160.
Biegler, L., & Grossmann, I. (2004). Retrospective on optimization. Journal of Computers and Chemical Engineering, 28(8), 1169–1192.
Forrester, J. (1958). Industrial dynamics: a major breakthrough for decision makers. Harvard Business Review, 36(4), 37–66.
Gjerdrum, J., Shah, N., & Papageorgiou, L. G. (2000). A combined optimisation and agent-based approach for supply chain modelling and performance assessment. Production Planning & Control, 12, 81–88.
Holweg, M., & Bicheno, J. (2002). Supply chain simulation—a tool for education, enhancement and endeavour. International Journal of Production Economics, 78(2), 163–175.
Julka, N., Srinivasan, R., & Karimi, I. (2002a). Agent-based supply chain management—1: framework. Computers & Chemical Engineering, 26(12), 1755–1769.
Julka, N., Karimi, I., & Srinivasan, R. (2002b). Agent-based supply chain management—2: a refinery application. Computers & Chemical Engineering, 26(12), 1771–1781.
Kaihara, T. (2003). Multi-agent based supply chain modelling with dynamic environment. International Journal of Production Economics, 85(2), 263–269.
Lee-Post, A., & Chung, C. H. (2008). Systems for supporting operations management decisions. Berlin: Springer.
Mele, F. D., Guillén, G., Espuña, A., & Puigjaner, L. (2007). An agent-based approach for supply chain retrofitting under uncertainty. Computers & Chemical Engineering, 31(5–6), 722–735.
Merkuryev, Y., Merkuryeva, G., Piera, M., & Guasch, A. (2009). Simulation-based case studies in logistics. Berlin: Springer. ISBN: 978-1-84882-186-6.
Min, H., & Zhou, G. (2010). Supply chain modeling: past, present, and future. Computers and Industrial Engineering, 43(1–2), 231–249.
Moyaux, T., Chaib-draa, B., & D’Amours, S. (2006). Supply chain management and multiagent systems: an overview. Berlin: Springer.
Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7(4), 308–313.
Pitty, S. S., Li, W., Adhitya, A., Srinivasan, R., & Karimi, I. (2008). Decision support for integrated refinery supply chains—1. Dynamic simulation. Computers & Chemical Engineering, 32(11), 2767–2786.
Siirola, J. D., Hauan, S., & Westerberg, A. W. (2003). Toward agent-based process systems engineering: proposed framework and application to non-convex optimization. Computers & Chemical Engineering, 27(12), 1801–1811.
Srinivasan, R., Bansal, M., & Karimi, I. (2006). AÂ multi-agent approach to supply chain management in the chemical industry. Berlin: Springer.
Swaminathan, J. M., Smith, S. F., & Sadeh, N. M. (1998). Modeling supply chain dynamics: a multiagent approach. Decision Sciences, 29(3), 607–632.
Tayur, S., Ganeshan, R., & Magazine, M. (1999). Quantitative models for supply chain management. Norwell: Kluwer Academic.
van Dam, K. H. (2009). Capturing socio-technical systems with agent-based modelling. PhD thesis, Delft University of Technology, Delft, the Netherlands. ISBN 978-90-79787-12-8.
van Dam, K. H., Adhitya, A., Srinivasan, R., & Lukszo, Z. (2008). Benchmarking numerical and agent-based models of an oil refinery supply chain. In B. Braunschweig & X. Joulia (Eds.), Computer-aided chemical engineering: Vol. 25. Proceedings of the European symposium on computer aided process engineering ESCAPE 18 (pp. 623–628). Lyon: Elsevier.
van Dam, K. H., Adhitya, A., Srinivasan, R., & Lukszo, Z. (2009a). Critical evaluation of paradigms for modelling integrated supply chains. Journal of Computers and Chemical Engineering, 33(10), 1711–1726. 2009.01.023. doi:10.1016/j.compchemeng.
van Dam, K. H., Lukszo, Z., & Srinivasan, R. (2009b). Abnormal situation management in a refinery supply chain supported by an agent-based simulation model. In R. M. de Brito Alves, C. A. O. do Nascimento, & E. C. Biscaia Jr. (Eds.), Proceedings of the 10th international symposium on process systems engineering—PSE2009, Salvador, Bahia, Brazil.
Zhang, H., Wong, W. K., Adhitya, A., & Srinivasan, R. (2008). Agent-based simulation of a speciality chemicals supply chain. In Proceedings of the first international conference on infrastructure systems (INFRA 2008): building networks for a brighter future, Rotterdam, The Netherlands.
Acknowledgements
The authors would like to express their thanks to Rajagopalan Srinivasan and Arief Adhitya for their support in the development of the models used in both case studies.
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Behdani, B., van Dam, K.H., Lukszo, Z. (2013). Agent-Based Models of Supply Chains. In: van Dam, K., Nikolic, I., Lukszo, Z. (eds) Agent-Based Modelling of Socio-Technical Systems. Agent-Based Social Systems, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4933-7_5
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