A holistic approach to manufacturing cell formation: Incorporation of machine flexibility and machine aggregation

Abstract One of the most practical approaches of improving productivity in a factory is to adopt the superior concept and technique of cellular manufacturing (CM) based on group technology (GT). Particularly, cell formation is an important, critical and difficult step in CM. In general, there have been a number of methodologies proposed for solving a machine-part grouping problem (MPGP). Besides considering the simple cell formation problem, some researchers have focused on machine flexibility, in which parts are having alternative routeings/process plans. However, it is very rare to consider the area of aggregation and disaggregation of machines in cell formation under uncertain constraints and uncertainty. In the light of this, the main aim of this present work is to address the MPGP holistically with the considerations of machine flexibility as well as machine aggregation and disaggregation simultaneously. In addition, based on the availability of alternative routeings, a method is proposed to generate an alternative solution for machine breakdown situations. Thus, the problem nature of this work will be more realistic and practical for today's global manufacturing era. The problem scope has been identified and the model is formulated in mathematical programming form. The objective function of this model is to minimize the total intercellular and intracellular part movement. Since MPGP has been proved to be non-polynomial (NP) complete, a genetic algorithm (GA), which is an excellent optimization technique, is employed to solve this problem.

[1]  Felix T.S. Chan,et al.  Machine-component grouping using genetic algorithms , 1998 .

[2]  Vito Albino,et al.  Limited flexibility in cellular manufacturing systems: A simulation study , 1999 .

[3]  Tzung-Pei Hong,et al.  Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process , 2001, Applied Intelligence.

[4]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[5]  Richard Shell,et al.  Manufacturing system cell formation and evaluation using a new inter-cell flow reduction heuristic , 1992 .

[6]  Mitsuo Gen,et al.  A genetic algorithm-based approach for design of independent manufacturing cells , 1999 .

[7]  Rakesh Nagi,et al.  Multiple routeings and capacity considerations in group technology applications , 1990 .

[8]  Sebastián Lozano,et al.  Machine cell formation in generalized group technology , 2001 .

[9]  Kam-Fai Wong,et al.  A TSP-based heuristic for forming machine groups and part families , 1998 .

[10]  Kazem Abhary,et al.  A genetic algorithm based cell design considering alternative routing , 1997 .

[11]  Kai-Ling Mak,et al.  An Adaptive Genetic Algorithm for Manufacturing Cell Formation , 2000 .

[12]  Sung-Lyong Kang,et al.  A work load-oriented heuristic methodology for manufacturing cell formation allowing reallocation of operations , 1993 .

[13]  Manoj Kumar Tiwari,et al.  Solving machine loading problems in a flexible manufacturing system using a genetic algorithm based heuristic approach , 2000 .

[14]  J. King,et al.  Machine-component group formation in group technology: review and extension , 1982 .

[15]  S. M. Taboun,et al.  Part family and machine cell formation in multiperiod planning horizons of cellular manufacturing systems , 1998 .

[16]  Mahesh Gupta,et al.  Minimizing total intercell and intracell moves in cellular manufacturing: a genetic algorithm approach , 1995 .

[17]  Henri Pierreval,et al.  An evolutionary approach of multicriteria manufacturing cell formation , 1998 .

[18]  John McAuley,et al.  Machine grouping for efficient production , 1972 .

[19]  Lee Luong.  A cellular similarity coefficient algorithm for the design of manufacturing cells , 1993 .

[20]  K. Hitomi,et al.  GT cell formation for minimizing the intercell parts flow , 1992 .

[21]  H. Pierreval,et al.  Cell formation using evolutionary algorithms with certain constraints , 2000 .

[22]  J. King.  Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm , 1980 .

[23]  A. Kusiak,et al.  Similarity coefficient algorithms for solving the group technology problem , 1992 .

[24]  Jeffrey A. Joines,et al.  Manufacturing Cell Design: An Integer Programming Model Employing Genetic Algorithms , 1996 .

[25]  W. S. Chow,et al.  Minimizing intercellular part movements in manufacturing cell formation , 1993 .

[26]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[27]  Rahul Rai,et al.  Machine-tool selection and operation allocation in FMS: Solving a fuzzy goal-programming model using a genetic algorithm , 2002 .

[28]  Zhi-ming Wu,et al.  A genetic algorithm for manufacturing cell formation with multiple routes and multiple objectives , 2000 .

[29]  Avraham Shtubt.  Modelling group technology cell formation as a generalized assignment problem , 1989 .

[30]  Emanuel Falkenauer,et al.  A New Representation and Operators for Genetic Algorithms Applied to Grouping Problems , 1994, Evolutionary Computation.

[31]  Rakesh Nagi,et al.  An efficient heuristic in manufacturing cell formation for group technology applications , 1990 .

[32]  Evelyn C. Brown,et al.  CF-GGA: A grouping genetic algorithm for the cell formation problem , 2001 .