An Architecture for Scheduling and Control in Flexible Manufacturing Systems Using Distributed Objects

1
An Architecture for Scheduling and Control in
Flexible Manufacturing Systems Using Distributed
Objects

• TsuTa Tai and
• Thomas O. Boucher
• Presented by
• Ammon Johnson
• November 10, 2008

2
Function of Paper

• Use a decentralized approach to solve scheduling
  problems
• Optimize scheduling when changes happen in the
  system
• Deadlock avoidance
• Compare effectiveness and computation time

3
Importance

• Reducing deadlock, time to manufacture (makespan)
  will improve profitability of the manufacturing
  operation
• Scheduling is dynamic Sudden changes can
  adversely affect productivity

4
References

• REFERENCES
• 1 N. Costa and M. Garetti, Design of a control
  system for a flexible manufacturing
• cell, J. Manuf. Syst., vol. 4, pp. 6584, 1984.
• 2 T. O. Boucher,M. A. Jafari, and G. A.
  Meredith, Petri net control of an
• automated manufacturing cell, Adv. Manuf. Eng.,
  vol. 2, pp. 151157,
• 1990.
• 3 H. P. Huang and P. C. Chang, Specification,
  modeling and control of a
• flexible manufacturing cell, Int. J. Prod. Res.,
  vol. 30, pp. 25152543,
• 1992.
• 4 S. B. Joshi, E. G. Mettala, J. S. Smith, and
  R. A.Wysk, Formal models
• for control of flexible manufacturing cells
  Physical and system models,
• IEEE Trans. Robot. Automat., vol. 11, pp.
  558570, Aug. 1995.
• 5 A.Yalcin and T. O. Boucher, An architecture
  for flexible manufacturing
• cells with alternate machining and alternate
  sequencing, IEEE Trans.
• Robot. Automat., vol. 15, pp. 11261130, Dec.
  1999.
• 6 N. Viswandham, Y. Narahari, and T. L.
  Johnson, Deadlock prevention
• and deadlock avoidance in flexible manufacturing
  systems using Petri
• net models, IEEE Trans. Robot. Automat., vol. 6,
  pp. 713723, Dec.
• 1990.

5
References (cont.)

• 9 R. A.Wysk, N. S. Yang, and S. Joshi,
  Detection of deadlocks in flexible
• manufacturing cells, IEEE Trans. Robot.
  Automat., vol. 7, pp. 853859,
• Dec. 1991.
• 10 , Resolution of deadlocks in flexible
  manufacturing systems
• Avoidance and recovery approaches, J. Manuf.
  Syst., vol. 13, pp.
• 128138, 1999.
• 11 Z. A. Banaszak and B. H. Krogh, Deadlock
  avoidance in flexible manufacturing
• systems with concurrently competing process
  flows, IEEE
• Trans. Robot. Automat., vol. 6, pp. 724734, Dec.
  1990.
• 12 F. S. Hsieh and S. C. Chang,
  Dispatching-driven deadlock avoidance
• controller synthesis for flexible manufacturing
  systems, IEEE Trans.
• Robot. Automat., vol. 10, pp. 196209, Apr. 1994.
• 13 K. Y. Xing, B. S. Hu, and H. X. Chen,
  Deadlock avoidance policy for
• Petri net modeling of flexible manufacturing
  systems with shared resources,
• IEEE Trans. Automat. Contr., vol. 41, pp.
  289295, Feb. 1996.
• 14 M. P. Fanti, B. Maione, S. Mascolo, and B.
  Turchiano, Event-based
• feedback control for deadlock avoidance in
  flexible production systems,
• IEEE Trans. Robot. Automat., vol. 13, pp.
  347363, June 1997.
• 15 M. A. Lawley, S. A. Reveliotis, and P. M.
  Ferreira, A correct and

6
References (cont.)

• 18 A. Yalcin and T. O. Boucher, Deadlock
  avoidance in flexible manufacturing
• systems using finite automata, IEEE Trans.
  Robot. Automat.,
• vol. 16, pp. 424429, Aug. 2000.
• 19 T. O. Boucher, A. Yalcin, and T. Tai,
  Dynamic routing and the performance
• of automated manufacturing cells, IIE Trans.,
  vol. 32, no. 10,
• pp. 975988, 2000.
• 20 D. Y. Lee and F. DiCesare, Scheduling
  flexible manufacturing systems
• using Petri nets and heuristic search, IEEE
  Trans. Robot. Automat., vol.
• 10, pp. 123132, Apr. 1994.
• 21 S. E. Ramaswamy and S. B. Joshi,
  Deadlock-free schedules for automated
• manufacturing workstations, IEEE Trans. Robot.
  Automat., vol.
• 12, pp. 391400, June 1996.
• 22 H. H. Xiong and M. C. Zhou, A Petri net
  method for deadlock-free
• scheduling of flexible manufacturing systems,
  Int. J. Intell. Contr. Syst.,
• vol. 3, pp. 277295, 1999.
• 23 J. Pearl, Heuristics Intelligent Search
  Strategies for Computer Problem
• Solving. Reading, MA Addison-Wesley, 1984.
• 24 R. E. Tarjan, Depth first search and linear
  graph algorithm, SIAM J.
• Comput., vol. 1, pp. 146160, 1972.

7
References (cont.)

• 28 R. Smith and R. Davis, Framework for
  co-operation in distributed
• problem solving, IEEE Trans. Syst., Man,
  Cybern., vol. SMC11, pp.
• 6170, 1981.
• 29 W. D. Kelton, R. P. Sadowski, and D. A.
  Sadowski, Simulation With
• Arena. New York McGraw-Hill, 1998.
• 30 D. C. Montgomery, Design and Analysis of
  Experiments. New York
• Wiley, 1976.
• 31 T. Tai and T. O. Boucher, Scheduling With
  Distributed Objects Source
• Code and Experimental Trials, Ind. Eng. Dept.,
  Rutgers Univ., Piscataway,
• NJ, Working Paper 01-119, 2001.

8
Relation to ME 482

• Scheduling is one of the most complex aspects of
  FMS
• Optimizing the scheduling of tasks is a tedious
  task, so computers are used to optimize the
  scheduling

9
Basic Design Concept
Shop Floor Object (Central control computer)
Cell Object
Cell Object
Cell Object
10
Basic Design Concept
Shop Floor Object (Central control computer)
New Part
Determines cell with shortest makespan
Cell Object
Cell Object
Part goes to cell with shortest makespan
Cell Object
11
Design Principle

• Algorithm development
• DFS (Depth First Search)- looks for end
• DFS with Greedy Heuristic
• DFS Greedy with Knot Detection

12
Process plan and digraphs for one cell object
In this example the cells algorithm generates a
legal sequence of events, and avoids deadlock to
finish both parts. May be more than one legal
sequence. Cell object generates a schedule that
finishes all current parts and the new part.
13
Digraphs

• DFS Greedy Heuristic
• DFS Greedy with
• Knot detection

14
Example Problem
Time for processing parts A and B
Deadlock free schedule
15
Experimental Trials

• Four different scheduling rules
• Queue length (Q)- part goes to shortest line
• Bottleneck machining time (BT)- cell with least
  additional bottleneck time added
• Balanced workload scheduling (BL)
• These three were compared to the distributed
  object method (DO)
• Three cell system
• Ten paired comparisons
• of 100 parts each

16
Experimental Equipment

• The equipment consists of different software
  modules used to simulate the factory environment
• Simulator sends a new part to the cell,
  receives makespans, and assigns the part

17
Experimental Results

• Algorithms discussed were applied
• Distributed object scheduling compared with Q,
  BL, and BT

18
Experimental Results

• Average makespan 9-14 lower than other methods
• Throughput is increased
• Computation is very fast

19
Correlation of Results with Model

• Authors are unsure of source of improvement in
  performance
• Not a very complex system (3 cells)
• The simulation is both model and experiment

20
Practical Industrial Use and Advancement

• Shows that throughput was increased, makespan
  decreased in simulation
• No comparison with actual hardware
• Advancement in scheduling FMS, improving
  production
• Industries that use FMS systems, auto, aerospace,
  etc.