Reconfigurability in cellular manufacturing systems: a design model and multi-scenario analysis

Within cellular manufacturing systems (CMSs), families of parts are assigned to manufacturing cells, composed by homogeneous sets of machines. In conventional CMSs, each cell is devoted to the production of a specific part family, reducing material handling and work-in-process. Despite their flexibility, such systems still suffer from coping with the present market challenges asking for dynamic part mix and the need of agility in manufacturing. To meet these challenges, the recent literature explores the idea of including elements of the emerging reconfigurable manufacturing paradigm in the design and management of CMSs, leading to the cellular reconfigurable manufacturing system (CRMS) concept. The aim of this paper is to propose an original linear programming optimization model for the design of CRMSs with alternative part routing and multiple time periods. The production environment consists of multiple cells equipped with reconfigurable machine tools (RMTs) made of basic and auxiliary custom modules. By changing the auxiliary modules, different operations become available on the same RMT. The proposed approach determines the part routing mix and the auxiliary module allocation best balancing the part flows among RMTs and the effort to install the modules on the machines. The approach discussion is supported by a literature case study, while a multi-scenario analysis is performed to assess the impact of different CMS configurations on the system performances, varying both the number of cells and the RMT assignment to each of them. A benchmarking concludes the paper comparing the proposed CRMS against a conventional CMS configuration. The analysis shows relevant benefits in terms of reduction of the intercellular travel time (− 58.6%) getting a global time saving of about 53.3%. Results prove that reconfigurability is an opportunity for industries to face the dynamics of global markets.

[1]  Mehmet Bulent Durmusoglu,et al.  A performance comparison and evaluation of metaheuristics for a batch scheduling problem in a multi-hybrid cell manufacturing system with skilled workforce assignment , 2017 .

[2]  Akif Asil Bulgak,et al.  A mathematical model for designing reconfigurable cellular hybrid manufacturing-remanufacturing systems , 2016 .

[3]  Marco Bortolini,et al.  Reconfigurable manufacturing systems: Literature review and research trend , 2018, Journal of Manufacturing Systems.

[4]  Felix T.S. Chan,et al.  Cell formation problem with consideration of both intracellular and intercellular movements , 2008 .

[5]  Tang Dunbing Methodology of virtual manufacturing cell formation in reconfigurable manufacturing system for make-to-order manufacturing , 2009 .

[6]  Mingyuan Chen,et al.  A COMPREHENSIVE MATHEMATICAL MODEL FOR THE DESIGN OF CELLULAR MANUFACTURING SYSTEMS , 2006 .

[7]  B. C. Rennie,et al.  On stirling numbers of the second kind , 1969 .

[8]  Mohammad Saidi-Mehrabad,et al.  Designing integrated cellular manufacturing systems with scheduling considering stochastic processing time , 2010 .

[9]  Lyes Benyoucef,et al.  Machine layout design problem under product family evolution in reconfigurable manufacturing environment: a two-phase-based AMOSA approach , 2019, The International Journal of Advanced Manufacturing Technology.

[10]  Mahmoud Houshmand,et al.  Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL) , 2018, Int. J. Prod. Res..

[11]  Pramod Kumar Jain,et al.  Service Level as Performance Index for Reconfigurable Manufacturing System Involving Multiple Part Families , 2014 .

[12]  Bo Xing,et al.  Application of artificial intelligence (AI) methods for designing and analysis of reconfigurable cellular manufacturing system (RCMS) , 2009, 2009 2nd International Conference on Adaptive Science & Technology (ICAST).

[13]  Marco Bortolini,et al.  Similarity-Based Cluster Analysis for the Cell Formation Problem , 2012 .

[14]  Javad Rezaeian,et al.  A multi-objective integrated cellular manufacturing systems design with dynamic system reconfiguration , 2011 .

[15]  A. Galip Ulsoy,et al.  Trends and perspectives in flexible and reconfigurable manufacturing systems , 2002, J. Intell. Manuf..

[16]  Maurizio Faccio,et al.  Stochastic timed Petri nets to dynamically design and simulate industrial production processes , 2016 .

[17]  B.H. Ateme-Nguema,et al.  Optimization of cellular manufacturing systems design using the hybrid approach based on the ant colony and tabu search techniques , 2007, 2007 IEEE International Conference on Industrial Engineering and Engineering Management.

[18]  Marco Bortolini,et al.  An hybrid procedure for machine duplication in cellular manufacturing systems , 2011 .

[19]  L. N. Pattanaik,et al.  Implementing lean manufacturing with cellular layout: a case study , 2009 .

[20]  Sebastián Lozano,et al.  Cell formation and scheduling of part families for reconfigurable cellular manufacturing systems using Tabu search , 2013, Simul..

[21]  Ciro A. Rodríguez,et al.  Next-generation manufacturing systems: key research issues in developing and integrating reconfigurable and intelligent machines , 2005, Int. J. Comput. Integr. Manuf..

[22]  Emre Cevikcan,et al.  Scheduling batches in multi hybrid cell manufacturing system considering worker resources: A case study from pipeline industry , 2016 .

[23]  Lixin Tang,et al.  A hybrid approach of ordinal optimization and iterated local search for manufacturing cell formation , 2009 .

[24]  Bhaba R. Sarker,et al.  Measures of grouping efficiency in cellular manufacturing systems , 2001, Eur. J. Oper. Res..

[25]  Yoram Koren,et al.  General RMS Characteristics. Comparison with Dedicated and Flexible Systems , 2006 .

[26]  Lazhar Homri,et al.  Optimum machine capabilities for reconfigurable manufacturing systems , 2018 .

[27]  D.-H. Lee,et al.  Iterative algorithms for part grouping and loading in cellular reconfigurable manufacturing systems , 2012, J. Oper. Res. Soc..

[28]  Sebastián Lozano,et al.  Cell Design and Loading with Alternative Routing in Cellular Reconfigurable Manufacturing Systems , 2013, MIM.

[29]  Mehmet Bulent Durmusoglu,et al.  Evolutionary Algorithms for Multi-Objective Scheduling in a Hybrid Manufacturing System , 2018 .

[30]  Ayman M. A. Youssef,et al.  A Multi-period Cell Formation Model for Reconfigurable Manufacturing Systems , 2014 .

[31]  N. Singh,et al.  Design of cellular manufacturing systems: An invited review , 1993 .

[32]  Vikas Kumar,et al.  Multiple levels of reconfiguration for robust cells formed using modular machines , 2010 .

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

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

[35]  F. Jovane,et al.  Reconfigurable Manufacturing Systems , 1999 .

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

[37]  P. K. Jain,et al.  Cell formation in the presence of reconfigurable machines , 2007 .

[38]  Mauro Gamberi,et al.  The training of suppliers: a linear model for optimising the allocation of available hours , 2018 .

[39]  Sebastián Lozano,et al.  Cell design and multi-period machine loading in cellular reconfigurable manufacturing systems with alternative routing , 2017, Int. J. Prod. Res..

[40]  A. Galip Ulsoy,et al.  Reconfigurable manufacturing systems: Key to future manufacturing , 2000, J. Intell. Manuf..