Commenced in January 2007
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Multi-Agent Based Modeling Using Multi-Criteria Decision Analysis and OLAP System for Decision Support Problems
Authors: Omar Boutkhoum, Mohamed Hanine, Tarik Agouti, Abdessadek Tikniouine
Abstract:
This paper discusses the intake of combining multi-criteria decision analysis (MCDA) with OLAP systems, to generate an integrated analysis process dealing with complex multi-criteria decision-making situations. In this context, a multi-agent modeling is presented for decision support systems by combining multi-criteria decision analysis (MCDA) with OLAP systems. The proposed modeling which consists in performing the multi-agent system (MAS) architecture, procedure and protocol of the negotiation model is elaborated as a decision support tool for complex decision-making environments. Our objective is to take advantage from the multi-agent system which distributes resources and computational capabilities across interconnected agents, and provide a problem modeling in terms of autonomous interacting component-agents. Thus, the identification and evaluation of criteria as well as the evaluation and ranking of alternatives in a decision support situation will be performed by organizing tasks and user preferences between different agents in order to reach the right decision. At the end, an illustrative example is conducted to demonstrate the function and effectiveness of our MAS modeling.Keywords: Multidimensional Analysis, OLAP Analysis, Multi-criteria Decision Analysis, Multi-Agent System, Decision Support System.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1338802
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[1] Wooldridgey, M., & Ciancarini, P. (2001, January). Agent-oriented software engineering: The state of the art. In Agent-oriented software engineering (pp. 1-28). Springer Berlin Heidelberg.
[2] Nourani, C. F. (2002). Agent-Based Software Engineering and Agent Mediations. Hybrid Information Systems, 469–484. doi:10.1007/978-3- 7908-1782-9_34
[3] O’Hare, G. M., & Jennings, N. (1996). Foundations of distributed artificial intelligence (Vol. 9). John Wiley & Sons.
[4] S. D. J. McArthur, S. M. Strachan, and G. Jahn, “The design of a multiagent transformer condition monitoring system,” IEEE Transactions on Power Systems, vol. 19, no. 4, pp. 1845–1852, 2004.
[5] D. P. Buse and Q. H. Wu, “Mobile agents for remote control of distributed systems,” IEEE Transactions on Industrial Electronics, vol. 51, no. 6, pp. 1142–1149, 2004.
[6] E. M. Davidson, S. D. J. McArthur, J. R. McDonald, T. Cumming, and I. Watt, “Applying multi-agent system technology in practice: automated management and analysis of SCADA and digital fault recorder data,” IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 559–567, 2006.
[7] K. Fregene, D. C. Kennedy, and D. W. L. Wang, “Toward a systemsand control-oriented agent framework,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 35, no. 5, pp. 999–1012, 2005.
[8] García Ansola, P., de las Morenas, J., García, A., & Otamendi, J. (2012). Distributed decision support system for airport ground handling management using WSN and MAS. Engineering Applications of Artificial Intelligence, 25(3), 544–553.
[9] Ibri, S., Nourelfath, M., & Drias, H. (2012). A multi-agent approach for integrated emergency vehicle dispatching and covering problem. Engineering Applications of Artificial Intelligence, 25(3), 554–565.
[10] Dou, C., Wang, W., Hao, D.-W., & Li, X. (2015). MAS-based solution to energy management strategy of distributed generation system. International Journal of Electrical Power & Energy Systems, 69, 354– 366.
[11] Narayanaswami, S., & Rangaraj, N. (2015). A MAS architecture for dynamic, realtime rescheduling and learning applied to railway transportation. Expert Systems with Applications, 42(5), 2638–2656.
[12] Kaya, M., & Alhajj, R. (2014). Development of multidimensional academic information networks with a novel data cube based modeling method. Information Sciences, 265, 211–224.
[13] Lotfy, H. M. S., Khamis, S. M. S., & Aboghazalah, M. M. (2015). Multi-Agents and Learning: Implications for WebUsage Mining. Journal of Advanced Research. doi:10.1016/j.jare.2015.06.005
[14] Cao, M., Luo, X., Luo, X. (Robert), & Dai, X. (2015). Automated negotiation for e-commerce decision making: A goal deliberated agent architecture for multi-strategy selection. Decision Support Systems, 73, 1–14.
[15] Shen, Y., Colloc, J., Jacquet-Andrieu, A., & Lei, K. (2015). Emerging medical informatics with case-based reasoning for aiding clinical decision in multi-agent system. Journal of Biomedical Informatics, 56, 307–317
[16] Sun, B., & Ma, W. (2015). Rough approximation of a preference relation by multi-decision dominance for a multi-agent conflict analysis problem. Information Sciences, 315, 39–53
[17] Neville, B., Fasli, M., & Pitt, J. (2015). Utilising social recommendation for decision-making in distributed multi-agent systems. Expert Systems with Applications, 42(6), 2884–2906
[18] Santos, G., Pinto, T., Morais, H., Sousa, T. M., Pereira, I. F., Fernandes, R., … Vale, Z. (2015). Multi-agent simulation of competitive electricity markets: Autonomous systems cooperation for European market modeling. Energy Conversion and Management, 99, 387–399
[19] Kaya M, Alhajj R (2005) Fuzzy OLAP Association Rules Mining-Based Modular Reinforcement Learning Approach for Multiagent Systems. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics 35(2):326–338
[20] Rupnik R, Kukar M (2007) Data Mining Based Decision Support System to Support Association Rules. Elektrotehniski vestnik 74(4):195–200
[21] Lavbi D, Rupnik R (2009) Multi-Agent System for Decision Support in Enterprises. Journal of Information and Organizational Sciences 33(2):269–284
[22] Srinivasan S, Singh J, Kumar V (2011) Multi-agent based decision Support System using Data Mining and Case Based Reasoning. International Journal of Computer Science 8(4):340–349
[23] Markic I, Stula M, Maras J (2014) Intelligent Multi Agent Systems for decision support in insurance industry. 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO’2014) 1118–1123
[24] Jennings N. R., and Wooldridge M. (2001). Agent-Oriented Software Engineering, Handbook of agent technology, ed. J. Bradshaw, AAAI/MIT Press.
[25] Buckley J.J (1985) Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17 (3): 233–247
[26] Tsao C.-T, Chu C.-T (2001) Personnel selection using an improved fuzzy MCDM algorithm. Journal of Information and Optimization Sciences 22(3): 521–536
[27] Kannan G, Pokharel S, Sasi Kumar P (2009) A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources, Conservation and Recycling 54(1): 28–36
[28] Gumus, A.T., 2009. Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology. Expert Systems with Applications. 36,4067-4074.
[29] Pentaho community, Mondrian: Http://community.pentaho.com/projects/mondrian/. Accessed 3 August 2015