SRX Engineering

SRX Engineering / 2010 / Article

Research Article | Open Access

Volume 2010 |Article ID 503736 | 6 pages | https://doi.org/10.3814/2010/503736

Moving Coalition Analysis for Collaboration Evaluation in Overall Equipment Effectiveness

Received21 Nov 2009
Revised07 Jan 2010
Accepted21 Jan 2010
Published18 Mar 2010

Abstract

This paper proposed a method to evaluate collaboration of criteria such as Performance Rate, Quality Rate, and Availability of Overall Equipment Effectiveness (OEE) to players in time sequences. We proposed a method called “Moving Coalition Analysis” (MCA) to observe performance trends of a coalition over time by treating each year or time as a player. In this method, we divide a coalition into several subcoalitions and calculate the characteristic function of all subcoalitions. Then, we examine the collaboration of Performance Rate, Quality Rate, and Availability for each subcoalition using the Shapley value. This method shows the relationship between each player and its contributors and can show trends of an activity in a time sequence even though the value of OEE is constant. This concept is applicable to various sectors of maintenance management and can play a role in considering the balance of combinations of criteria when performing maintenance.

References

  1. R. K. Mobley, An Introduction to Predictive Maintenance, Van Nostrand Reinhold, New York, NY, USA, 1990.
  2. K. Komonen, “A cost model of industrial maintenance for profitability analysis and benchmarking,” International Journal of Production Economics, vol. 79, no. 1, pp. 15–31, 2002. View at: Publisher Site | Google Scholar
  3. E. A. Roth, The Shapley Value, Cambridge University Press, New York, NY, USA, 1988.
  4. P. M. Reyes, “Logistics networks: a game theory application for solving the transshipment problem,” Applied Mathematics and Computation, vol. 168, no. 2, pp. 1419–1431, 2005. View at: Publisher Site | Google Scholar | MathSciNet
  5. L. Petrosjan and G. Zaccour, “Time-consistent Shapley value allocation of pollution cost reduction,” Journal of Economic Dynamics and Control, vol. 27, no. 3, pp. 381–398, 2003. View at: Publisher Site | Google Scholar | MathSciNet
  6. K. Nakabayashi and K. Tone, “Egoist's dilemma: aDEA game,” Omega, vol. 34, no. 2, pp. 135–148, 2006. View at: Publisher Site | Google Scholar
  7. W. W. Cooper, L. M. Seiford, and K. Tone, Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Springer, London, UK, 2nd edition, 2006.
  8. M. Conklin, K. Powaga, and S. Lipovetsky, “Customer satisfaction analysis: identification of key drivers,” European Journal of Operational Research, vol. 154, no. 3, pp. 819–827, 2004. View at: Publisher Site | Google Scholar
  9. P. Tsarouhas, “Implementation of total productive maintenance in food industry: a case study,” Journal of Quality in Maintenance Engineering, vol. 13, no. 1, pp. 5–18, 2007. View at: Publisher Site | Google Scholar
  10. J. Wu, L. Liang, and F. Yang, “Determination of the weights for the ultimate cross efficiency using shapley value in cooperative game,” Expert Systems with Applications, vol. 36, no. 1, pp. 872–876, 2009. View at: Publisher Site | Google Scholar

Copyright © 2010 M. A. Mansor and A. Ohsato. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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