Measurement of States of Cellular Manufacturing Systems Based on Entropy Models

Abstract

Abstract:

Abstract： The states of manufacturing resources can be monitored and assessed through the amount of information needed to describe their technological structure and operational state from an information-theoretic perspective. The amount of information needed to describe cellular manufacturing systems was investigated by two measures, the structural entropy and the operational entropy. The structural entropic model and the operational entropic model were developed based on the Shannon entropy, respectively. Scheduling was introduced to measure the entropy models of cellular manufacturing systems and the feasible concepts of maximum schedule horizon and schedule adherence were advanced to quantitatively evaluate the effectiveness of schedules. The conclusion shows that the states of cellular manufacturing systems can be measured and monitored and effective ways can also be provided for improving operational efficiency of the systems by the entropy models built.

Key words: cellular manufacturing systems, information entropy, scheduling

CLC Number:

TH 17

ZHANG Zhifeng1,2，DAVID Janet2. Measurement of States of Cellular Manufacturing Systems Based on Entropy Models[J]. Journal of Shanghai Jiaotong University, 2015, 49(01): 109-115.

References

［1］Ateekh R, Subash A. Reconfigurations of manufacturing systems—An empirical study on concepts, research, and applications ［J］. The International Journal of Advanced Manufacturing Technology, 2013, 66(4): 107124.

［2］Wang H, Wang H, Hu S J. Utilizing variant differentiation to mitigate manufacturing complexity in mixedmodel assembly systems ［J］. Journal of Manufacturing Systems, 2013, 32(4):731740.

［3］Smart J, Calinescu A, Huatuco H L. Extending the informationtheoretic measures of the dynamic complexity of manufacturing systems ［J］. International Journal of Production Research, 2013, 51(2): 362379.

［4］ElMaraghy H, AlGeddawy T. Coevolution of products and manufacturing capabilities and application in autoparts assembly ［J］. Flexible Services and Manufacturing Journal, 2012, 24(2): 142170.

［5］Fujimoto H, Ahmed A. Entropic evaluation of assemblability in concurrent approach to assembly planning ［C］∥ Proceedings of the IEEE International Symposium on Assembly and Task Planning. Fukuoka: IEEE, 2007: 306311.

［6］Suh N. Complexity: theory and applications ［M］. England: Oxford University Press, 2011.

［7］Ashkan N, Jeffrey S. Simulation for manufacturing system design and operation: Literature review and analysis ［J］. Journal of Manufacturing Systems, 2014, 33(2): 241261.

［8］Smart J, Yue R, Huatuco H L. A method for analyzing operational complexity in supply chains ［J］. Journal of the Operational Research Society, 2013, 64: 654667.

［9］Mourtzis D, Doukas M. Decentralized manufacturing systems review: challenges and outlook ［J］. Logistics Research, 2012, 5(4): 113121.

［10］Jin G Q, Li W D, Tsai C F. Adaptive toolpath generation of rapid prototyping for complex product models ［J］. Journal of Manufacturing Systems, 2011, 30(3): 154164.

［11］Gunter L, Philip S, Juergen W. Innovation in production: The adoption and impacts of new manufacturing concepts in German industry ［M］. Germany: PhysicaVerlag, Heidelberg, 2010.

［12］张志峰, JINDAL V. 基于尖点突变的制造系统耗散结构模型及其度量［J］. 机械工程学报, 2011, 47(14):192197.

ZHANG Zhifeng, JINDAL V. Dissipative structure models of manufacturing systems and its measurement based on catastrophic theory ［J］. Chinese Journal of Mechanical Engineering, 2011, 47(14):192197.

［13］Kechadi T, Low S, Goncalves G. Recurrent neural network approach for cyclic job shop scheduling problem ［J］. Journal of Manufacturing Systems, 2013, 32(4): 689699.

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