A model and optimisation approach for reconfigurable manufacturing system configuration design

During the expected lifetime, evolving economical and optimum reconfigurable manufacturing system (RMS) configuration in a desired period to give the exact functionality and the exact capacity needed, when it is needed, is an important optimisation problem. This paper presents RMS configuration design methodology in a desired period with optimisation of the present worth of total cost of RMS including capital cost of machine investment, reconfiguration costs, operating costs, maintenance costs and salvage value over time. The configuration decision considers flow line features such as multi-product, number of stages, and functionality blocks in each stage, etc., with various constraints satisfaction. Phase 1 aims at RMS evolution scenario and the mathematical modelling. Phase 2 evolves RMS configuration alternatives. Phase 3 aims at RMS configuration alternative optimisation using mathematical model and artificial immune system. A case study demonstrates the methodology, the effectiveness of the methodology and also some superiority of the proposed methodology compared to existing approaches.

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

[2]  Zhao Xiaobo,et al.  A stochastic model of a reconfigurable manufacturing system Part 3: Optimal selection policy , 2001 .

[3]  L. Kumar,et al.  PART-MACHINE GROUP FORMATION WITH ORDINAL-RATIO LEVEL DATA & PRODUCTION VOLUME , 2009 .

[4]  Lihui Wang,et al.  Reconfigurable manufacturing systems: the state of the art , 2008 .

[5]  Zhenbi Luo,et al.  A stochastic model of a reconfigurable manufacturing system Part 2: Optimal configurations , 2000 .

[6]  Hoda A. ElMaraghy,et al.  Modelling and optimization of multiple-aspect RMS configurations , 2006 .

[7]  Sung-Yong Son,et al.  Design principles and methodologies for reconfigurable machining systems. , 2000 .

[8]  J Kumar,et al.  PART-MACHINE GROUP FORMATION WITH OPERATION SEQUENCE, TIME AND PRODUCTION VOLUME , 2008 .

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

[10]  U. Lorch An introduction to graph algorithms , 2000 .

[11]  P. K. Jain,et al.  Dynamic cellular manufacturing systems design—a comprehensive model , 2011 .

[12]  Jianping Dou,et al.  Graph theory-based approach to optimize single-product flow-line configurations of RMS , 2009 .

[13]  Xianzhong Dai,et al.  Optimisation for multi-part flow-line configuration of reconfigurable manufacturing system using GA , 2010 .

[14]  Xianzhong Dai,et al.  A GA-based approach for optimizing single-part flow-line configurations of RMS , 2011, J. Intell. Manuf..

[15]  Andrea Matta,et al.  Optimal reconfiguration policy to react to product changes , 2008 .

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

[17]  Gun Ho Lee,et al.  Reconfigurability consideration design of components and manufacturing systems , 1997 .

[18]  Alf Kimms,et al.  Minimal investment budgets for flow line configuration , 1998 .

[19]  Alexandre Dolgui,et al.  Integer programming models for logical layout design of modular machining lines , 2006, Comput. Ind. Eng..

[20]  Mingyuan Chen,et al.  A simulated annealing algorithm for dynamic system reconfiguration and production planning in cellular manufacturing , 2009, Int. J. Manuf. Technol. Manag..

[21]  J Kumar,et al.  CONCURRENTLY PART-MACHINE GROUPS FORMATION WITH IMPORTANT PRODUCTION DATA , 2010 .

[22]  D. Dasgupta Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[23]  John Patrick Spicer,et al.  A design methodology for scalable machining systems. , 2002 .

[24]  Leandro Nunes de Castro,et al.  An Overview of Artificial Immune Systems , 2004 .

[25]  S. Jack Hu,et al.  Selecting manufacturing system configurations based on performance using AHP , 2002 .

[26]  Karsten Schierholt,et al.  Process configuration: Combining the principles of product configuration and process planning , 2000, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.