An Architecture for Scheduling and Control in Flexible

An Architecture for Scheduling and Control in Flexible Manufacturing Systems Using Distributed Objects Tsu. Ta Tai and Thomas O. Boucher Presented by: Ammon Johnson November 10, 2008

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

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

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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. 853– 859, Dec. 1991. [10] , “Resolution of deadlocks in flexible manufacturing systems: Avoidance and recovery approaches, ” J. Manuf. Syst. , vol. 13, pp. 128– 138, 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. 724– 734, 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. 196– 209, 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. 289– 295, 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. 347– 363, June 1997. [15] M. A. Lawley, S. A. Reveliotis, and P. M. Ferreira, “A correct and scaleable deadlock avoidance policy for flexible manufacturing systems, ” IEEE Trans. Robot. Automat. , vol. 14, pp. 796– 809, Oct. 1998. [16] N. Q. Wu, “Necessary and sufficient conditions for deadlock-free operation in flexible manufacturing system using colored Petri net model, ” IEEE Trans. Syst. , Man, Cybern. , vol. 29, pp. 192– 204, May 1999. [17] M. A. Lawley, “Deadlock avoidance for production systems with flexible routing, ” IEEE Trans. Robot. Automat. , vol. 15, pp. 497– 509, June 1999.

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. 424– 429, 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. 975– 988, 2000. [20] D. Y. Lee and F. Di. Cesare, “Scheduling flexible manufacturing systems using Petri nets and heuristic search, ” IEEE Trans. Robot. Automat. , vol. 10, pp. 123– 132, Apr. 1994. [21] S. E. Ramaswamy and S. B. Joshi, “Deadlock-free schedules for automated manufacturing workstations, ” IEEE Trans. Robot. Automat. , vol. 12, pp. 391– 400, 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. 277– 295, 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. 146– 160, 1972. [25] A. Yalcin, “Architectures for automated flexible manufacturing cells with routing flexibility, ” Ph. D. dissertation, Rutgers Univ. , New Brunswick, NJ, 2000. [26] J. Ferber, Multi-Agent Systems. Reading, MA: Addison-Wesley, 1999. [27] R. Smith, “The contract net protocol: High-level communication and control in a distributed problem solver, ” IEEE Trans. Comput. , vol. C– 23, pp. 1104– 1113, 1980.

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Relation to ME 482 n n 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

Basic Design Concept Shop Floor Object (Central control computer) Cell Object

Basic Design Concept New Part Shop Floor Object (Central control computer) Determines cell with shortest “makespan” Cell Object Part goes to cell with shortest makespan

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

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.

Digraphs n DFS Greedy Heuristic DFS Greedy with Knot detection n

Example Problem Time for processing parts A and B Deadlock free schedule

Experimental Trials n Four different scheduling rules n n n 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

Experimental Equipment n n 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

Experimental Results n n Algorithms discussed were applied Distributed object scheduling compared with Q, BL, and BT

Experimental Results n n n Average makespan 9 -14% lower than other methods Throughput is increased Computation is very fast

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

Practical Industrial Use and Advancement n n 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.