Part family grouping method for reconfigurable manufacturing system considering process time and capacity demand

Reconfigurable manufacturing system (RMS) is designed around part family providing exact production function and capacity in cost-effective way when needed. Besides the grouping accuracy of part family impacting the responsiveness of RMS, the efficiency problem of RMS resulting from the difference of process time and capacity demand should be solved. Therefore, a similarity coefficient method for RMS part family grouping considering process time and capacity demand is proposed. First, the longest common subsequence (LCS) among different part process routes is extracted and the shortest composite supersequence (SCS) of parts is constructed. Idle machine (IM) and bypass move (BPM) are analyzed based on SCS. Then, the process time (T) and capacity demand (D) are used as characteristic value of operation. And characteristic value sequences of process route, LCS, SCS, IM and BPM are gained, that is, TDP, TDLCS, TDSCS, TDIM and TDBPM respectively. By analyzing the relationships between TDLCS and TDSCS, the characteristic value sequences of TDLCS, TDIM and TDBPM are used to calculate the similarity between parts. Based on the similarity matrix, the netting clustering algorithm is used for clustering to complete the part family grouping. Finally, a case study is presented to implement the proposed method and validate the effectiveness.

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

[2]  Hoda A. ElMaraghy,et al.  Flexible and reconfigurable manufacturing systems paradigms , 2005 .

[3]  Fujun Liang.  THEORETICAL RESEARCH OF RECONFIGURABLE MANUFACTURING SYSTEM , 2003 .

[4]  D. Canca,et al.  Machine grouping using sequence-based similarity coefficients and neural networks , 2001 .

[5]  Ying-Chin Ho,et al.  Two sequence-pattern, matching-based, flow analysis methods for multi-flowlines layout design , 1993 .

[6]  Yasuhiro Yamada,et al.  Layout optimization of manufacturing cells and allocation optimization of transport robots in reconfigurable manufacturing systems using particle swarm optimization , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[7]  Liu Jie-ping.  Product family partition of reconfigurable manufacturing systems(RMS)based on improved hierarchical clustering algorithm , 2011 .

[8]  Yoram Koren,et al.  Value creation through design for scalability of reconfigurable manufacturing systems , 2017, Int. J. Prod. Res..

[9]  Yoram Koren,et al.  The rapid responsiveness of RMS , 2013 .

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

[11]  H. Seifoddini,et al.  Merits of the production volume based similarity coefficient in machine cell formation , 1995 .

[12]  Qiu Meng.  Problem of classification of product family for reconfigurable manufacturing systems , 2008 .

[13]  Dinesh Kumar,et al.  Availability Modelling of Reconfigurable Manufacturing System , 2012 .

[14]  Luo Zhenbi.  Rapidly Reconfigurable Manufacturing Systems , 2000 .

[15]  H. ElMaraghy,et al.  Product family formation for reconfigurable assembly systems , 2014 .

[16]  Tabitha L. James,et al.  Grouping efficiency measures and their impact on factory measures for the machine-part cell formation problem: A simulation study , 2007, Eng. Appl. Artif. Intell..

[17]  Masood Ashraf,et al.  Product family formation based on multiple product similarities for a reconfigurable manufacturing system , 2015 .

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

[19]  Ashraf Labib,et al.  A design strategy for reconfigurable manufacturing systems (RMSs) using analytical hierarchical process (AHP): A case study , 2003 .

[20]  Alexandre Dolgui,et al.  A taxonomy of line balancing problems and their solutionapproaches , 2013 .

[21]  K. Y. Tam,et al.  An operation sequence based similarity coefficient for part families formations , 1990 .

[22]  Yong Yin,et al.  Similarity coefficient methods applied to the cell formation problem: a comparative investigation , 2005, Comput. Ind. Eng..

[23]  Rakesh Kumar,et al.  Part Family and Operations Group Formation for RMS Using Bond Energy Algorithm , 2017 .

[24]  Pramod Kumar Jain,et al.  A novel methodology to measure the responsiveness of RMTs in reconfigurable manufacturing system , 2013 .

[25]  Ming Zhou,et al.  Formation of independent flow-line cells based on operation requirements and machine capabilities , 1998 .

[26]  Zhao Xiaobo,et al.  A stochastic model of a reconfigurable manufacturing system Part 1: A framework , 2000 .

[27]  Yan Yan,et al.  Formation of part family for reconfigurable manufacturing systems considering bypassing moves and idle machines , 2016 .

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

[29]  Jesús Racero,et al.  A systematic approach for product families formation in Reconfigurable Manufacturing Systems , 2007 .

[30]  Asoo J. Vakharia,et al.  Designing a Cellular Manufacturing System: A Materials Flow Approach Based on Operation Sequences , 1990 .

[31]  M. Reza Abdi,et al.  Product family formation and selection for reconfigurability using analytical network process , 2012 .

[32]  F. Fred Choobineh,et al.  A framework for the design of cellular manufacturing systems , 1988 .

[33]  Jaydeep Balakrishnan,et al.  Manufacturing cell formation using similarity coefficients and a parallel genetic TSP algorithm: Formulation and comparison , 1995 .

[34]  Madhu Jain,et al.  A comprehensive approach to operation sequence similarity based part family formation in the reconfigurable manufacturing system , 2013 .

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