Reliability analysis for an apparel manufacturing system applying fuzzy multistate network

Reliability analysis for a practical apparel manufacturing system by using fuzzy mathematics.The apparel manufacturing system is constructed as a fuzzy multistate network.A simple solution procedure to evaluate both the workstation-reliability and the system reliability.Both pessimistic and optimistic evaluations are considered. This paper addresses the reliability analysis for a real-world apparel manufacturing system by using fuzzy mathematics. The studied apparel manufacturing system is a precise handicraft profession which involves a great amount of labor-intensive processes. To consider human performance, the apparel manufacturing system is constructed as a fuzzy multistate network, termed apparel manufacturing network (AMN). The workload state of a workstation in the AMN is defined by three fuzzy membership functions: "under-normal-workload", "normal-workload", and "over-normal-workload". Hence, the workload of a workstation is fuzzy multistate and the workstation-reliability is measured by three fuzzy membership functions. Subsequently, the system reliability is evaluated in terms of all workstation-reliabilities, and is derived by fuzzy intersection. The reliability analysis can help the production manager to understand the demand satisfaction of the AMN.

[1]  F. J. Montero Fuzzy Coherent Systems , 1988 .

[2]  Zhaojun Li,et al.  Some Perspectives to Define and Model Reliability Using Fuzzy Sets , 2013 .

[3]  William J. Stevenson,et al.  Operations Management , 2011 .

[4]  Latife Görkemli,et al.  Fuzzy Bayesian reliability and availability analysis of production systems , 2010, Comput. Ind. Eng..

[5]  J. Dombi A general class of fuzzy operators, the demorgan class of fuzzy operators and fuzziness measures induced by fuzzy operators , 1982 .

[6]  W. Pedrycz Why triangular membership functions , 1994 .

[7]  Tien-Hui Chen,et al.  Applying cost-reliability analysis to improve system reliability , 2013 .

[8]  Didier Dubois,et al.  A review of fuzzy set aggregation connectives , 1985, Inf. Sci..

[9]  Yi-Kuei Lin,et al.  Performance evaluation for a footwear manufacturing system with multiple production lines and different station failure rates , 2013 .

[10]  R. Yager On a general class of fuzzy connectives , 1980 .

[11]  J. Pan,et al.  Integrated inventory models with fuzzy annual demand and fuzzy production rate in a supply chain , 2008 .

[12]  D. Pandey,et al.  Profust reliability of a gracefully degradable system , 2007, Fuzzy Sets Syst..

[13]  Kouroush Jenab,et al.  Fuzzy quality feature monitoring model , 2010 .

[14]  Shin-Guang Chen,et al.  Fuzzy-scorecard based logistics management in robust SCM , 2012, Comput. Ind. Eng..

[15]  Bernard J. Schroer,et al.  Simulation of an apparel assembly cell with walking workers and decouplers , 1993 .

[16]  Bart Kosko,et al.  Fuzzy Engineering , 1996 .

[17]  John N. Mordeson Fuzzy intersection equations and primary representations , 1996, Fuzzy Sets Syst..

[18]  Cheng Lichun,et al.  The fuzzy relation equation with union or intersection preserving operator , 1988 .

[19]  Xingwei Wang,et al.  Decentralized capacity allocation of a single-facility with fuzzy demand , 2014 .

[20]  Yi-Kuei Lin,et al.  Stochastic computer network under accuracy rate constraint from QoS viewpoint , 2013, Inf. Sci..

[21]  Yi-Kuei Lin,et al.  Reliability evaluation for a manufacturing network with multiple production lines , 2012, Comput. Ind. Eng..

[22]  Zhaojun Li,et al.  Continuous-state reliability measures based on fuzzy sets , 2012 .

[23]  Yi-Kuei Lin,et al.  On performance evaluation of ERP systems with fuzzy mathematics , 2009, Expert Syst. Appl..

[24]  Kai-Yuan Cai,et al.  Introduction to Fuzzy Reliability , 1996 .

[25]  Yi-Kuei Lin,et al.  Reliability-based performance indicator for a manufacturing network with multiple production lines in parallel , 2013 .

[26]  Ming-Kuen Chen,et al.  Establishing an order allocation decision support system via learning curve model for apparel logistics , 2014 .

[27]  M. Sheikhalishahi,et al.  An integrated fuzzy simulation–fuzzy data envelopment analysis approach for optimum maintenance planning , 2014, Int. J. Comput. Integr. Manuf..

[28]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[29]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[30]  İhsan Erozan A hybrid methodology for restructuring decision of a manufacturing system: A case study , 2011 .

[31]  M. Gupta,et al.  Theory of T -norms and fuzzy inference methods , 1991 .

[32]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[33]  Yi-Kuei Lin,et al.  Reliability evaluation of a stochastic-flow distribution network with delivery spoilage , 2013, Comput. Ind. Eng..

[34]  Kai-Yuan Cai,et al.  Fuzzy states as a basis for a theory of fuzzy reliability , 1993 .

[35]  Yi Ding,et al.  Fuzzy Multi-State Systems: General Definitions, and Performance Assessment , 2008, IEEE Transactions on Reliability.

[36]  R. Yager Connectives and quantifiers in fuzzy sets , 1991 .

[37]  John N. Mordeson,et al.  Fuzzy intersection equations , 1993 .