An integrated framework for reconfiguration of cellular manufacturing systems using virtual cells

Reconfiguration of manufacturing systems is discussed and an integrated framework which is mainly based on multiple objective simulation optimization is proposed for reconfiguration of manufacturing cells. The possibility of using virtual (logical) cells as a reconfiguration strategy in order to achieve performance targets of manufacturing cells facing changing production requirements is investigated. Creation of virtual cells is decided based on the overall performance of the system that is determined by the proposed integrated framework. In the framework generic capability units which are known as Resource Elements (RE) are used to define processing capabilities of virtual cells and processing requirements

[1]  Zubair M. Mohamed A flexible approach to (re)configure Flexible Manufacturing Cells , 1996 .

[2]  Meir J. Rosenblatt,et al.  The dynamics of plant layout , 1986 .

[3]  Asoo J. Vakharia,et al.  Redesigning a Cellular Manufacturing System to Handle Long-Term Demand Changes: A Methodology and Investigation* , 1993 .

[4]  Nancy Lea Hyer,et al.  Cellular manufacturing in the U.S. industry: a survey of users , 1989 .

[5]  James C. Schaff,et al.  The Virtual Cell , 1998, Pacific Symposium on Biocomputing.

[6]  Marius M. Solomon,et al.  Dynamic layout strategies for flexible manufacturing systems , 1990 .

[7]  N. N. Z. Gindy,et al.  Feature-based component model for computer-aided process planning systems , 1993 .

[8]  H. Seifoddini,et al.  Determination of a Flexibility Range for Cellular Manufacturing Systems under Product Mix Variations , 1997 .

[9]  A Baykasoglu,et al.  Solution of goal programming models using a basic taboo search algorithm , 1999, J. Oper. Res. Soc..

[10]  Adil Baykasoğlu,et al.  MOCACEF 1.0: Multiple objective capability based approach to form part-machine groups for cellular manufacturing applications , 2000 .

[11]  C. L. Ang,et al.  A comparative study of the performance of pure and hybrid group technology manufacturing systems using computer simulation techniques , 1984 .

[12]  Randall P. Sadowski,et al.  Introduction to Simulation Using Siman , 1990 .

[13]  Benoit Montreuil,et al.  Scheduling factories of the future , 1989 .

[14]  A. Baykasoğlu,et al.  A TABOO SEARCH BASED APPROACH TO FIND THE PARETO OPTIMAL SET IN MULTIPLE OBJECTIVE OPTIMIZATION , 1999 .

[15]  T. T. Narendran,et al.  Logical Cell Formation in FMS, Using Flexibility-Based Criteria , 1998 .

[16]  Sameh M. Saad,et al.  Handling internal and external disturbances in responsive manufacturing environments , 1998 .

[17]  Adil Baykasoglu,et al.  A new integrated system for loading and scheduling in cellular manufacturing , 2002, Int. J. Comput. Integr. Manuf..

[18]  Urban Wemmerlöv,et al.  CELLULAR MANUFACTURING AT 46 USER PLANTS : IMPLEMENTATION EXPERIENCES AND PERFORMANCE IMPROVEMENTS , 1997 .

[19]  F. Sassani,et al.  A simulation study on performance improvement of group technology cells , 1990 .

[20]  Tarun Gupta,et al.  Production data based similarity coefficient for machine-component grouping decisions in the design of a cellular manufacturing system , 1990 .

[21]  Evon M. O. Abu-Taieh,et al.  Comparative Study , 2020, Definitions.

[22]  T. Lacksonen,et al.  Quadratic assignment algorithms for the dynamic layout problem , 1993 .

[23]  Hamid Seifoddini,et al.  Sensitivity analysis in cellular manufacturing system in the case of product mix variation , 1996 .

[24]  E. S. Meieran,et al.  Intelligent manufacturing systems , 1993, Proceedings of 15th IEEE/CHMT International Electronic Manufacturing Technology Symposium.

[25]  Joseph A. C. Delaney Sensitivity analysis , 2018, The African Continental Free Trade Area: Economic and Distributional Effects.

[26]  Nancy Lea Hyer,et al.  Research issues in cellular manufacturing , 1987 .

[27]  J. Driscoll,et al.  A computer model for investigating the relayout of batch production areas , 1985 .