Agent-Based Simulation for Supply Chain Transport Corridors
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:70308", author = "Kamalendu Pal", title = "Agent-Based Simulation for Supply Chain Transport Corridors", 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.", volume = "9", number = "7", pages = "1677-5", }