Using a Multi-Agent System for Supply Chain Management

Using a Multi-Agent System for Supply Chain Management

L. C. M. Perera A. S. Karunananda 

Department of Computational Mathematics, University of Moratuwa, Sri Lanka

Page: 
107-115
|
DOI: 
https://doi.org/10.2495/DNE-V11-N2-107-115
Received: 
N/A
|
Accepted: 
N/A
|
Published: 
30 April 2016
| Citation

OPEN ACCESS

Abstract: 

Supply chain management (SCM) is a well-known example of a complex system. Classical computing technologies have shown little success in modeling complex systems. However, a large body of research in multi-agent system (MAS) technology has demonstrated how complex systems can be modeled to generate smart solutions, which could not be done otherwise. We have researched on the design and development of MAS for SCM. In this solution, each phase in the supply chain has been developed as an agent enabling communication, coordination and negotiation among the agents to achieve intended business goals. The study investigated decentralized collaborative planning architecture and agents are attached to different containers of the system. The containers have been implemented using a Java Agent Development Framework (JADE) and consist of diverse methods to support collaboration in the supply chain environment. Agents have different behaviors and their decisions are based on defined ontology. The identified key roles in the supply chain are raw material suppliers, manufacturers, distributors and retailers. They perform autonomous tasks with collaboration to accomplish final customer satisfaction.

Keywords: 

 multi-agent system, supply chain management.

  References

[1] Moyaux, T., Chaib-draa, B. & D’Amours, S., supply chain management and multiagent systems: an overview, Springer Abstract, 28, pp. 1–27, 2006. doi: http://dx.doi.org/10.1007/978-3-54033876-5_1

[2] Hernández, J.E., Poler, R., Mula, J., & de La Fuente, D., A multiagent based-model for the collaborative planning process in decentralized supply chain networks, 3rd International Conference on Industrial Engineering and Industrial Management  , Barcelona-Terrassa, September 2nd-4th 2009, pp. 1012–1020, 2009.

[3] Swaminathan, J.M., Smith, S.F. & Sadeh, N.M., Modeling supply chain dynamics: a multiagent approach, Decision Sciences, 29(3), pp. 607–632, 1998. doi: http://dx.doi.org/ 10.1111/j.1540-5915.1998.tb01356.x

[4] Kumar, V., & Srinivasan, S., A review of supply chain management using multi-agent system, International Journal of Computer Science Issues, 7(5), pp. 198–205, 2010.

[5] Um, W., A study of multi-agent based supply chain modeling and management, iBusiness, 2(4), pp. 333–341, 2010.

[6] Min, J.U., Bjornsson, H. Agent based supply chain management automation. Proceedings of the Eighth International Conference on Computing in Civil and Building Engineering (ICCCBE-VIII), pp. 1001–1006, 2000.

[7] Ojha, M., Optimizing supply chain management using gravitational search algorithm and multi agent system. Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20–22, 2011, pp. 481–491, 2012. doi: http://dx.doi. org/10.1007/978-81-322-0487-9_47

[8] Lau, S.K., & Li, X., A multi-agent approach towards collaborative supply chain managementIn B. Liu (Eds.), International Conference on Electronic Business. Hong Kong: The Chinese  University of Hong Kong, pp. 929-935, 2005.

[9] Fransoo, J.C., & Wouters, M.J., Measuring the bullwhip effect in the supply chain, Supply Chain Management: An International Journal, 5(2), pp. 78–89, 2000.

[10] Julka, N., Srinivasan, R., & Karimi, I., Agent-based supply chain management – 1: framework, Computers & Chemical Engineering, 26(12), pp. 1755–1769, 2002. doi: http://dx.doi. org/10.1016/S0098-1354(02)00150-3

[11] d’Inverno, M., Kinny, D., Luck, M., & Wooldridge, M., A formal specification of dMARS. Intelligent Agents IV Agent Theories, Architectures, and Languages, Springer, pp. 155–176, 1998.

[12] Pereira, D., Oliveira, E., Moreira, N., and Sarmento, L., Towards an architecture for emotional BDI agents, in EPIA, vol. 5, pp. 40–47, 2005.

[13] Zhu, X., Agent based modeling for supply chain management: examining the impact of information sharing, Kent State University, 2008.

[14] Carvalho, R. & Custódio, L., A multiagent systems approach for managing supply-chain problems: new tools and results, Inteligencia Artificial, 9(25), pp. 79–88, 2005.

[15] Popirlan, C.I., Stefănescu, A. & Stefănescu, L., Multi-agent approach for data analysis in a knowledge-based system for contact centers, World Academy of Science Engineering and Technology, 59, pp. 1126–1131, 2011.

[16] Chopra, A.K., & Singh, M.P., Elements of a business-level architecture for multiagent systems. 

Programming Multi-Agent Systems, Springer, pp. 15–30, 2010.

[17] Srinivasan, S., Singh, S.K., and Kumar, V., Multi-agent system based service oriented architecture for supply chain management system, International Journal of Computer Applications, 27(5), pp. 12–16, 2011.

[18] Haitham, A., Applying Electronic Supply Chain Management Using Multi-Agent System A 

Managerial Perspective, International Arab Journal of e-Technology, 1(3), pp. 106–113, 2010

[19] Fu, Y., Piplani, R., De Souza, R., & Wu, J., Multi-agent enabled modeling and simulation towards collaborative inventory management in supply chains. Simulation Conference, 2000. Proceedings. Winter, vol. 2, pp. 1763–1771, 2000.

[20] Rady, H.A., Multi-Agent System for Negotiation in a Collaborative Supply Chain Management, International Journal of Video & Image Processing and Network Security, 11(5), pp. 25–35, 2011.

[21] Chen, Y., Peng, Y., Finin, T., Labrou, Y., Cost, S., Chu, B., Sun, R., & Willhelm, R., A negotiation-based multi-agent system for supply chain management, Work. Notes Agents, vol. 99, 1999.

[22] Shehory, O.M., Architectural properties of multi-agent systems, Carnegie Mellon University, The Robotics Institute, 1998.

[23] Chen, B., Cheng, H.H., & Palen, J., Integrating mobile agent technology with multi-agent systems for distributed traffic detection and management systems, Transportation Research Part C: Emerging Technologies, 17(1), pp. 1–10, February 2009.

[24] Wooldridge, M., & Jennings, N.R., Intelligent Agents: Theory and Practice, 1995. [http:// www.ent.mrt.ac.lk/~ekulasek/it6401/ker.pdf]

[25] Labrou, Y., Finin, T., & Peng, Y., The current landscape of agent communication languages IEEE Intell. Syst., vol. 14, no. 2, pp. 45–52, 1999. doi: http://dx.doi.org/10.1109/5254.757631

[26] Chopra, A.K., & Singh, M.P., Agent Communication, MIT Press, 2011, www.csc.ncsu.edu/ faculty/mpsingh/papers/mas/Agent-Communication-chapter.pdf

[27] Foundation for Intelligent Physical Agents, FIPA 97 Specification, Part 2, Agent Communication Language, 1997. [http://www.fipa.org/specs/fipa00018/OC00018.pdf]

[28] Kumar, S., & Kumar, U., Java agent development framework, International Journal Research, 1(9), pp. 1022–1025, 2014.

[29] Anandampilai, B., Content-based multicasting using JADE, International Journal of Soft Computing, 2(3), pp. 422–425, 2007.

[30] Chen, B., Cheng, H.H., and Palen, J., Integrating mobile agent technology with multi-agent systems for distributed traffic detection and management systems, Transportation Research Part C: Emerging Technologies, 17(1), pp. 1–10, February 2009.

[31] Jennings, N.R., Wooldridge, M., & Sycara, K., A Roadmap of Agent Research and Development, 

1998. [http://users.ecs.soton.ac.uk/nrj/download-files/roadmap.pdf]