A message-driving formalism for modeling and simulation of multi-agent supply chain systems

Abstract

To acquire a competitive advantage in the expanding market, manufacturing enterprises should be able to manage their supply chains as effectively as possible. It is now becoming popular to model supply chains as multi-agent systems and use discrete event simulation to learn more about their behaviors or investigate the implications of alternative configurations. In order to enhance the computational efficiency and keep the simulation credibility, this paper proposes a message-driving formalism for the simulation of multi-agent supply chain systems. Through the message-driving formalism, the problem of shared variables is addressed and the parallel operation of agents is implemented. Simulation experiments with a prototype implementation show that the message-driving formalism is able to provide credible results in significantly less simulation time.

Keywords

Supply chain / multi-agent system / modeling and simulation / parallel discrete event simulation / message-driving formalism

References

[1] Al-Mutawah K., Lee V., Cheung Y.. A new multi-agent system framework for tacit knowledge management in manufacturing supply chains. Journal of Intelligent Manufacturing, 2009, 20(5): 593-610.

[2] Banks, J. & Nelson, B.L. (2010). Discrete-event System Simulation. Prentice Hall

[3] Chandy K.M., Misra J.. Asynchronous distributed simulation via a sequence of parallel computations. Communications of the ACM, 1981, 24(4): 198-206.

[4] Chatfield D.C., Hayya J.C., Harrison T.P.. A multi-formalism architecture for agent-based, order-centric supply chain simulation. Simulation Modelling Practice and Theory, 2007, 15(2): 153-174.

[5] Chopra, S. & Meindl, P. (2001). Supply Chain Management: Strategy, Planning and Operation. Prentice Hall, Inc.

[6] Dam K.H., Lukszo Z.. Negenborn R.R., Lukszo Z., Hellendoorn H.. Model factory for socio-technical infrastructure systems. Intelligent Infrastructures, Intelligent Systems, Control and Automation: Science and Engineering, vol 42, 2010, Netherlands: Springer 25-52.

[7] Dam K.H., Adhitya A., Srinivasan R., Lukszo Z.. Critical evaluation of paradigms for modelling integrated supply chains. Computers and Chemical Engineering, 2009, 33(10): 1711-1726.

[8] Freeman, E., Robson, E., Bates, B. & Sierra, K. (2004). Head First Design Patterns. O’ Reilly & Associates, Inc.

[9] Giannakis M., Louis M.. A multi-agent based framework for supply chain risk management. Journal of Purchasing and Supply Management, 2011, 17(1): 23-31.

[10] Govindu R., Chinnam R.B.. MASCF: a generic process-centered methodological framework for analysis and design of multi-agent supply chain systems. Computers & Industrial Engineering, 2007, 53(4): 584-609.

[11] Han, Y. (2009). Research, design and implementaion of flexible supply chain simulation & analysis system. Master’s thesis, Tsinghua University

[12] Hybinette M., Kraemer E., Xiong Y., Matthews G., Ahmed J.. SASSY: a design for a scalable agent-based simulation system using a distributed discrete event infrastructure. 2006 Winter Simulation Conference, 2006, 1: 926-933.

[13] Jefferson D.R.. Virtual time. ACM Transactions on Programming Languages and Systems, 1985, 7: 404-425.

[14] Jefferson, D.R. (1990) Virtual time ii: storage management in conservative and optimistic systems. In: Ninth Annual ACM Symposium on Principles of Distributed Computing, 75–89, New York, NY, USA

[15] Jennings N.R., Sycara K., Wooldridge M.. A roadmap of agent research and development. Journal of Autonomous Agents and Multi — Agent Systems, 1998, 1(1): 7-38.

[16] Labarthe O., Espinasse B., Ferrarini A., Montreuil B.. Toward a methodological framework for agent-based modelling and simulation of supply chains in a mass customization context. Simulation Modelling Practice and Theory, 2007, 15(2): 113-136.

[17] Lim C., Low Y., Turner S.. Relaxing safetime computation of a conservative simulation algorithm. International Conference on Parallel and Distributed Processing Techniques and Applications, 1998, Nevada, USA: Las Vegas 1538-1545.

[18] Logan B., Theodoropoulos G.. The distributed simulation of multiagent systems. Proceedings of the IEEE, 2001, 89(2Sp.Iss.SI): 174-185.

[19] Luh P., Feng W.. From manufacturing scheduling to supply chain coordination: the control of complexity and uncertainty. Journal of Systems Science and Systems Engineering, 2003, 12(3): 279-297.

[20] Niewiadomska-Szynkiewicz, E. & Sikora, A. (2002). Algorithms for distributed simulation — comparative study. In: International Conference on Parallel Computing in Electrical Engineering, 261–266, Los Alamitos

[21] Pathak S.D., Dilts D.M., Biswas G.. A multi-paradigm simulator for simulating complex adaptive supply chain networks. 36th Winter Simulation Conference, 2003, 1: 808-816.

[22] Sadeh N.M., Hildum D.W., Kjenstad D., Tseng A.. MASCOT: an agent-based architecture for dynamic supply chain creation and coordination in the internet economy. Production Planning & Control, 2001, 12(3): 212-223.

[23] Swaminathan J.M., Smith S.F., Sadeh N.M.. Modeling supply chain dynamics: a multiagent approach. Decision Sciences, 1998, 29(3): 607-632.

[24] Tan, L., Xu, S., Meyer, B. & Erwin, B. (2009). An agent-based formal framework for modeling and simulating supply chains. In: IEEE International Conference on Information Reuse & Integration, 224–229

[25] Tan, W., Chai, Y. & Liu, Y. (2011). Design of a flexible, extensible and reusable supply chain simulation system. In: The Fourth International Joint Conference on Computational Sciences and Optimization, 61–65

[26] Tropper C.. Parallel discrete-event simulation applications. Journal of Parallel and Distributed Computing, 2002, 62(3): 327-335.

[27] Xie M., Chen J.. Studies on horizontal competition among homogenous retailers through agent-based simulation. Journal of Systems Science and Systems Engineering, 2004, 13(4): 490-505.

[28] Yoo T., Cho H., Yucesan E.. Web services-based parallel replicated discrete event simulation for large-scale simulation optimization. Simulation: Transactions of the Society for Modeling and Simulation International, 2009, 85(7): 461-475.