Network Flow Modeling for Flexible Manufacturing Systems with Re-entrant Lines

A relaxed version of the process planning problem for flexible manufacturing systems/cells (FMS/FMC) and processing networks, such as flexible flow shops and general job shops, is formulated using a simple extension of multicommodity network flow problems. Our multistage multicommodity network formulation allows for simultaneous routing and resource allocation and also captures the case of re-entrant lines (recirculation). It can be used to perform rapid, albeit crude, explorations of the combinatorial space of possible configurations and failure scenarios. The technique can also provide bounds on the limits of system performance (eg: throughput, link usage, bottlenecks, etc). This can be used to guide the design of robust FMS architectures with high degree of redundancy in machines and routes, as demonstrated in numerical examples. Being a relaxation to the full discrete problem, our method could potentially be used as an admissible heuristic for pruning Al-based planning methods. We demonstrate our approach on a realistic industrial problem

[1]  Thomas Dean,et al.  Automated planning , 1996, CSUR.

[2]  Gideon Weiss,et al.  A Fluid Heuristic for Minimizing Makespan in Job Shops , 2002, Oper. Res..

[3]  L. V. Kantorovich,et al.  Mathematical Methods of Organizing and Planning Production , 1960 .

[4]  Yves Crama,et al.  Cyclic scheduling in robotic flowshops , 2000, Ann. Oper. Res..

[5]  P. Moran,et al.  Reversibility and Stochastic Networks , 1980 .

[6]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[7]  Wheeler Ruml,et al.  On-line Planning and Scheduling for High-speed Manufacturing , 2005, ICAPS.

[8]  George B. Dantzig,et al.  Linear programming and extensions , 1965 .

[9]  Sean P. Meyn Workload models for stochastic networks: value functions and performance evaluation , 2005, IEEE Transactions on Automatic Control.

[10]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[11]  Daniel G. Bobrow,et al.  Model-Based Computing for Design and Control of Reconfigurable Systems , 2004, AI Mag..

[12]  Stephen P. Boyd,et al.  Simultaneous routing and resource allocation via dual decomposition , 2004, IEEE Transactions on Communications.

[13]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[14]  H. Neil Geismar,et al.  Dominance of Cyclic Solutions and Challenges in the Scheduling of Robotic Cells , 2005, SIAM Rev..

[15]  S. Baum,et al.  Intro , 2003, Science.

[16]  A. Dixit,et al.  Theory of International Trade. A Dual, General Equilibrium Approach , 1980 .

[17]  David Gamarnik,et al.  From Fluid Relaxations to Practical Algorithms for High-Multiplicity Job-Shop Scheduling: The Holding Cost Objective , 2003, Oper. Res..

[18]  Avinash Dixit,et al.  Theory Of International Trade , 1980 .

[19]  Tai-Yue Wang,et al.  Applying the network flow model to evaluate an FMC's throughput , 2002 .

[20]  Paolo Traverso,et al.  Automated planning - theory and practice , 2004 .

[21]  Wheeler Ruml,et al.  On-line Planning and Scheduling in a High-speed Manufacturing Domain , 2022 .

[22]  D. Ricardo On the Principles of Political Economy and Taxation , 1891 .

[23]  Stephen P. Boyd,et al.  Optimal routing and SINR target selection for power-controlled CDMA wireless networks , 2003 .

[24]  Philippe Baptiste,et al.  Constraint-based scheduling , 2001 .

[25]  J. Michael Harrison,et al.  Stochastic Networks and Activity Analysis , 2002 .

[26]  Sean P. Meyn,et al.  Duality and linear programs for stability and performance analysis of queuing networks and scheduling policies , 1996, IEEE Trans. Autom. Control..