Scalability planning for reconfigurable manufacturing systems

Abstract Scalability is a key characteristic of reconfigurable manufacturing systems, which allows system throughput capacity to be rapidly and cost-effectively adjusted to abrupt changes in market demand. This paper presents a scalability planning methodology for reconfigurable manufacturing systems that can incrementally scale the system capacity by reconfiguring an existing system. An optimization algorithm based on Genetic Algorithm is developed to determine the most economical way to reconfigure an existing system. Adding or removing machines to match the new throughput requirements and concurrently rebalancing the system for each configuration, accomplishes the system reconfiguration. The proposed approach is validated through a case study of a CNC-based automotive cylinder head machining system.

[1]  Ahmed M. Deif,et al.  A control approach to explore the dynamics of capacity scalability in reconfigurable manufacturing systems , 2006 .

[2]  Yoram Koren,et al.  Design of reconfigurable manufacturing systems , 2010 .

[3]  Katta G. Murty,et al.  A hybrid genetic/optimization algorithm for a task allocation problem , 1999 .

[4]  E. Degarmo.  Materials and Processes in Manufacturing , 1974 .

[5]  Derek Yip-Hoi,et al.  An approach to scalability and line balancing for reconfigurable manufacturing systems , 2001 .

[6]  Theodor Freiheit,et al.  A case study in productivity-cost trade-off in the design of paced parallel production systems , 2007 .

[7]  Yoram Koren,et al.  The Global Manufacturing Revolution: Product-Process-Business Integration and Reconfigurable Systems , 2010 .

[8]  Hong Chul Lee,et al.  The simulation design and analysis of a Flexible Manufacturing System with Automated Guided Vehicle System , 2009 .

[9]  Xiuli Meng,et al.  Modeling of reconfigurable manufacturing systems based on colored timed object-oriented Petri nets , 2010 .

[10]  Thomas Lorenzer,et al.  Modeling and evaluation tool for supporting decisions on the design of reconfigurable machine tools , 2007 .

[11]  Cheng Wu,et al.  Modeling and analysis of multi‐stage transfer lines with unreliable machines and finite buffers , 2000, Ann. Oper. Res..

[12]  Hui Wang,et al.  Automatic generation of assembly system configuration with equipment selection for automotive battery manufacturing , 2011 .

[13]  Yoram Koren,et al.  Convertibility Measures for Manufacturing Systems , 2003 .

[14]  Derek Yip-Hoi,et al.  Design Principles for Machining System Configurations , 2002 .

[15]  Armin Scholl,et al.  A survey on problems and methods in generalized assembly line balancing , 2006, Eur. J. Oper. Res..

[16]  Yoram Koren,et al.  Impact of Manufacturing System Configuration on Performance , 1998 .

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

[18]  Jeremy J. Michalek,et al.  Balancing Marketing and Manufacturing Objectives in Product Line Design , 2006 .

[19]  Jeonghan Ko,et al.  Design of multi-product manufacturing lines with the consideration of product change dependent inter-task times, reduced changeover and machine flexibility , 2010 .

[20]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[21]  Napsiah Ismail,et al.  An improved algorithm for layout design in cellular manufacturing systems , 2009 .

[22]  Zhen Gao,et al.  An integrated approach for remote manipulation of a high-performance reconfigurable parallel kinematic machine , 2010 .

[23]  P. Spicer *,et al.  Scalable reconfigurable equipment design principles , 2005 .

[24]  Li Tang,,et al.  Concurrent Line-Balancing, Equipment Selection and Throughput Analysis for Multi-Part Optimal Line Design , 2004 .

[25]  Hoda A. ElMaraghy,et al.  Performance analysis of manufacturing systems composed of modular machines using the universal generating function , 2008 .

[26]  Hui Wang,et al.  Multi-objective optimization of product variety and manufacturing complexity in mixed-model assembly systems , 2011 .