Agent-Based Simulation for Supply Chain Transport Corridors

Authors: Kamalendu Pal

Abstract:

Supply chains are the backbone of trade and commerce. Their logistics use different transport corridors on regular basis for operational purpose. The international supply chain transport corridors include different infrastructure elements (e.g. weighbridge, package handling equipments, border clearance authorities, and so on). This paper presents the use of multi-agent systems (MAS) to model and simulate some aspects of transportation corridors, and in particular the area of weighbridge resource optimization for operational profit. An underlying multi-agent model provides a means of modeling the relationships among stakeholders in order to enable coordination in a transport corridor environment. Simulations of the costs of container unloading, reloading, and waiting time for queuing up tracks have been carried out using data sets. Results of the simulation provide the potential guidance in making decisions about optimal service resource allocation in a trade corridor.

Keywords: Multi-agent systems, simulation, supply chain, transport corridor, weighbridge.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1107369

References:

[1] J. L. Adler, G. Satapathy, V. Manikonda, B. Bowles, and V. J. Blue, A multi-agent approach to cooperative traffic management and route guidance, Transportation Research, 39B, pp.297-318, 2005.
[2] A. L. Azevedo, C. Toscano, and J. P. Sousa, Cooperative planning in dynamic supply chains, International Journal of Computer Integrated Manufacturing, vol. 18, no. 5, pp. 350-356, 2005.
[3] R. H. Bordini, J. F. Hubner, and M. Woodridge, Programming Multiagent Systems in Agent Speak Using JASON: A Practical Introduction with JASON, Wiley Blackwell, 2007.
[4] A. Cuppari, P. Guida, M. Martelli, V. Mascardi, and F. Zini, “Prototyping freight trains traffic management using multi-agent systems,” in Proc. Int. Conf. Inf. Intell. Syst., Los Alamitos, CA, 1999, pp. 646-653.
[5] Y. S. Chang, and J. K. Lee; Case-based modification for optimization agents: AGENTOPT; Decision Support Systems; 36; pp. 355- 370, 2004.
[6] S. Chen, Y. Chen, and C. Hsu, A New Approach to Integrate Internet-of- Things and Software-as-a-Service Model for Logistic Systems – A Case Study, Sensors, vol. 14, no. 4, pp. 6144-6164. 2014.
[7] O. Chidyiwa, and M. Thinyane, An investigation of the Adequacy of Agent Platforms for Rural e-Service Provisioning, in SATNAC, Ezulwini, 2009.
[8] M. Fisher, R. H. Bordini, B. Hirsch, and T. Torroni, Computational logics and agents: a road map of current technologies and future trends, Computational Intelligence, vol. 23, no. 1, pp.61-91, 2007.
[9] D. Frey, and W. Peer-Oliver, Integrated multi-agent-based supply chain management. In Proceedings of the 12th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE-2003), pp. 24-29, 2003.
[10] L. Gambardella, A. Rizzoli, and P. Funk, Agent-based Planning and Simulation of Combined Rail/Road Transport, Simulation, vol. 78, no. 5, pp. 293-303, May 2002.
[11] S. Halle, and B. Chaib-draa, A Collaborative driving system based on multiagent modeling and simulations, Transportation Research Part C, vol. 13, no. 4, pp.320-345, 2005.
[12] L. Henesey, and J. A. Persson, Analyzing Transactions Costs in Transport Corridors Using Multi Agent-Based Simulation. In Multi- Agent Systems for Traffic and Transportation Engineering. Ana Bazzan and Franziska Klüg IGI Global, April 2009.
[13] J. E. Hernández, M. M. Alemany, F. C. Lario, R. Poler, SCAMM-CP: A Supply Chain Agent-Based Modelling Methodology: The Supports a Collaborative Planning Process, Innovar, vo. 19, no. 34, 2009.
[14] K. Kaim, and M. Lenar, Modelling Agent Behaviours in Simulating Transport Corridors Using Prometheus and Jason. Proceedings of the 2008 conference on New Trends in Multimedia and Network Information Systems, pp.182-192, 2008.
[15] A. Karageorgos, M. N. Mehandjiev, A. Haemmerle, and G. Weichhart, "Agent-based optimisation of logistics and production planning." Engineering Applications of Artificial Intelligence 16(4), 2003.
[16] B. Karakostas, A DNS Architecture for the Internet of Thing: A Case Study in Transport Logistics, in the 4th International Conference on Ambient Systems, Halifax. 2013.
[17] M. Luck, P. McBurney, and C. Preist, Agent technology Enabling next generation computing (a roadmap for agent based computing), The AgentLink Community, 2003.
[18] A. Mehra, and N. Mark, Case Study: Intelligent Software Supply Chain Agents using ADE. Proceedings from the AAAI Workshop on Software Tools for Developing Agents, 1998.
[19] F. Mele, D. Fernando, G. Guillén-Gosálbez, E. Antonio, and Puigjaner, L. Agent-based systems for supply chain management EWO Seminar, 11 December, 2007.
[20] D. Pawlaszczyk, Scalable Multi Agent Based Simulation – Considering Effective Simulation of Transport Logistics Networks, 12th ASIM Conference – Simulation in Production and Logistics, 2006.
[21] K. Pal, and B. Karakostas, A Multi Agent-based Service Framework for Supply Chain Management, The 5th International Conference on Ambient Systems, Networks and Technologies (ANT-2014), in Procedia Computer Science, vol. 32, p. 53-60. 2014.
[22] M. P. Papazoglou, P. Traverso, S. Dustdar, and F. Leymann, Service- Oriented Computing: State-of-the-Art Research Challenges, IEEE Computer, 11, pp. 38-45, 2007.
[23] L. G. Peck, and R. N. Hazelwood, Finite Queuing Tables, John Wiley & Sons Inc, 1958.
[24] D. Perugini, S. Wark, A. Zschorn, D. Lambert, L. Sterling, and A. Pearce, Agents in Logistics Planning – Experiences with the Coalition Agents Experiment Project, In Proceedings of workshop at the Second International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2003), Melbourne, Australia, 2003.
[25] L. Padgham, and M. Winikoff, Prometheus: A methodology for developing intelligent agents, in third international workshop on agent- Oriented Software Engineering, July 2002.
[26] R. J. Rabelo, Interoperating standards in multiagent agile manufacturing scheduling systems, International Journal of Computer Applications in Technology archive. vol. 18 no. 1-4, July, 2003.
[27] N. M. Sadeh, T. Chan, L. Van, O. Kwon, and K. Takizawa, Creating an Open Agent Environment foe Context-aware M-Commerce, in Agentcities: Challenges in Open Agent Environments, Ed by Burg, Dale, Finin, Nakashima, Padgham, Sierra, and Willmott, LNAI, Springer, pp. 152-158, 2003.
[28] H. Sundmaeker, P. Guillemin, P. Friess, and S. Woelffle, (ed.) (2010) Vision and Challenges for Realising the Internet of Things, (CERP-IoT) Cluster of European Research Projects on the Internet of Things.
[29] F. Wang, Agent-Based Control for Networked Traffic Management Systems, IEEE Intelligent Systems, 20(5), pp. 92-96, 2005.
[30] M. Wooldridge, and N. R. Jennings, Intelligent agents: theory and practice, The Knowledge Engineering Review, vol. 10, no. 2, pp. 115- 152, 1999.
[31] G. Weiss, Adaptation and learning in Multi-Agent Systems: Some Remarks and a Bibliography, In Proceedings IJCAI’95 Workshop on Adaptation and Learning in Multi-Agent Systems, LNAI 1042, pp.1-22, Springer, 1995.
[32] K. Zhu, and A. Bos, Agent-based design of international freight transportation systems, NECTAR Conference, Delft. 1999.