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

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

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

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References (cont.) [28] R. Smith and R. Davis, “Framework for co-operation in distributed problem solving,” IEEE Trans. Syst., Man, Cybern., vol. SMC–11, pp. 61–70, [29] W. D. Kelton, R. P. Sadowski, and D. A. Sadowski, Simulation With Arena. New York: McGraw-Hill, [30] D. C. Montgomery, Design and Analysis of Experiments. New York: Wiley, [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.

Relation to ME 482 Scheduling is one of the most complex aspects of FMS 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 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 Shop Floor Object (Central control computer) Cell Object New Part Determines cell with shortest “makespan” Part goes to cell with shortest makespan

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

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 DFS Greedy Heuristic DFS Greedy Heuristic DFS Greedy with DFS Greedy with Knot detection

Example Problem Time for processing parts A and BDeadlock free schedule

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

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

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

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

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

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