Value creation through design for scalability of reconfigurable manufacturing systems

Rapid and cost-effective scalability of the throughput of manufacturing systems is an invaluable feature for the management of manufacturing enterprises. System design for scalability allows the enterprise to build a manufacturing system to supply the current demand, and upgrade its throughput in the future, in a cost-effective manner, to meet possible higher market demand in a timely manner. To possess this capability, the manufacturing system must be designed at the outset for future expansions in its throughput to enable growths in supply exactly when needed by the market. A mathematical method that maximises the system throughput after reconfiguration is proposed, and an industrial case is presented to validate the method. The paper offers a set of principles for system design for scalability to guide designers of modern manufacturing systems.

[1]  Y. Tang *,et al.  Integrated design approach for virtual production line-based reconfigurable manufacturing systems , 2004 .

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

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

[4]  Chao-Ton Su,et al.  Intelligent scheduling controller for shop floor control systems: A hybrid genetic algorithm/decision tree learning approach , 2003 .

[5]  Alexandre Dolgui,et al.  Graph approach for optimal design of transfer machine with rotary table , 2009 .

[6]  M. Reza Abdi,et al.  Grouping and selecting products: the design key of Reconfigurable Manufacturing Systems (RMSs) , 2004 .

[7]  Carin Rösiö,et al.  Reconfigurable production system design – theoretical and practical challenges , 2013 .

[8]  Yoram Koren,et al.  Scalability planning for reconfigurable manufacturing systems , 2012 .

[9]  Anton V. Eremeev,et al.  Complexity of Buffer Capacity Allocation Problems for Production Lines with Unreliable Machines , 2013, J. Math. Model. Algorithms Oper. Res..

[10]  Alexandre Dolgui,et al.  A special case of transfer lines balancing by graph approach , 2006, Eur. J. Oper. Res..

[11]  Jun Ni,et al.  Manufacturing System Design for Resilience , 2015 .

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

[13]  Goran D. Putnik,et al.  Scalability in manufacturing systems design and operation: State-of-the-art and future developments roadmap , 2013 .

[14]  Madhu Jain,et al.  Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS , 2012 .

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

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

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

[18]  F. Musharavati RECONFIGURABLE MANUFACTURING SYSTEMS , 2010 .

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

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

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

[22]  Brahim Rekiek,et al.  State of art of optimization methods for assembly line design , 2002, Annu. Rev. Control..

[23]  Yoram Koren,et al.  Design Principles of Scalable Reconfigurable Manufacturing Systems , 2013, MIM.

[24]  Lyes Benyoucef,et al.  Developing a reconfigurability index using multi-attribute utility theory , 2011 .

[25]  S. Jack Hu,et al.  Productivity of Paced Parallel-Serial Manufacturing Lines With and Without Crossover , 2004 .

[26]  Zhibin Jiang,et al.  A review on strategic capacity planning for the semiconductor manufacturing industry , 2009 .

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

[28]  Alexandre Dolgui,et al.  Stability analysis of an optimal balance for an assembly line with fixed cycle time , 2006, Eur. J. Oper. Res..

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

[30]  A. Dolgui,et al.  A heuristic approach for transfer lines balancing , 2005, J. Intell. Manuf..

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

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

[33]  S. Gershwin,et al.  A segmentation approach for solving buffer allocation problems in large production systems , 2016 .

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

[35]  M. Tao Zhang,et al.  Dynamic capacity modeling in semiconductor assembly manufacturing , 2008 .

[36]  John H. Holland,et al.  Genetic Algorithms and the Optimal Allocation of Trials , 1973, SIAM J. Comput..

[37]  R. Krygier The Integration of Flexible , Reconfigurable Manufacturing with Quality Roman Krygier , 2014 .

[38]  Yoram Koren,et al.  Reconfigurable machine tools , 2001 .

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

[40]  Onur Kuzgunkaya,et al.  Impact of reconfiguration characteristics for capacity investment strategies in manufacturing systems , 2012 .

[41]  G. Meng,et al.  Reconfigurable layout problem , 2004 .

[42]  G. Ulsoy,et al.  Vision , Principles and Impact of Reconfigurable Manufacturing Systems , 2022 .

[43]  Kamal Kumar Mittal,et al.  Optimal Configuration Selection in Reconfigurable Manufacturing System , 2018, Decision Science in Action.

[44]  M. R. Abdi,et al.  Feasibility study of the tactical design justification for reconfigurable manufacturing systems using the fuzzy analytical hierarchical process , 2004 .

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

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

[47]  Jonathan F. Bard,et al.  AN OPTIMIZATION APPROACH TO CAPACITY EXPANSION IN SEMICONDUCTOR MANUFACTURING FACILITIES , 1999 .

[48]  Yoram Koren,et al.  Productivity of synchronized serial production lines with flexible reserve capacity , 2004 .

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

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

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

[52]  Yoram Koren,et al.  Reconfigurable Manufacturing and Beyond , 2012 .

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

[54]  Hoda A. ElMaraghy,et al.  Availability consideration in the optimal selection of multiple-aspect RMS configurations , 2008 .

[55]  Li Tang,et al.  An AI-Based Computer-Aided Reconfiguration Planning Framework for Reconfigurable Manufacturing Systems , 2004 .