PRIAM: polite rescheduler for intelligent automated manufacturing

This paper considers the problem of rescheduling in a decentralized manufacturing system. Flexible manufacturing systems must be able to respond to unexpected disruptions; including schedule disruptions. However, when a cell controller in a decentralized system responds to a disruption, it may disrupt some other cells, because the actions taken at one cell may have some consequence at another cells. In the approach we propose, a controller at a disrupted cell tries to respond in a way which is likely to be least disruptive to other cells, through negotiation with controllers at other cells. This approach, which we call "polite replanning", has the advantage of retaining much of the original distributed plan, while avoiding wide propagation of the disruption through the rest of the system. We apply this concept to the domain of distributed factory rescheduling, and describe PRIAM (polite rescheduler for intelligent automated manufacturing), a "polite" rescheduling architecture which is currently under development. Simulation results show that the use of negotiation in "polite" rescheduling prevents the wide propagation of disruption from an initial local disruption.

[1]  Paul Rogers,et al.  Manufacturing Cells: Control, Programming and Integration , 1991 .

[2]  Kang G. Shin,et al.  Polite rescheduling: responding to local schedule disruptions in distributed manufacturing systems , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[3]  David Chapman,et al.  Pengi: An Implementation of a Theory of Activity , 1987, AAAI.

[4]  Monte Zweben,et al.  Scheduling and rescheduling with iterative repair , 1993, IEEE Trans. Syst. Man Cybern..

[5]  Don T. Phillips,et al.  A state-of-the-art survey of dispatching rules for manufacturing job shop operations , 1982 .

[6]  Randall W. Hill,et al.  Representing and Using Organizational Knowledge in Distributed AI Systems , 1989, Distributed Artificial Intelligence.

[7]  Edmund H. Durfee,et al.  Unsupervised Surrogate Agents and Search Bias Change in Flexible Distributed Scheduling , 1995, ICMAS.

[8]  Randall Davis,et al.  Negotiation as a Metaphor for Distributed Problem Solving , 1988, Artificial Intelligence.

[9]  Mark S. Fox,et al.  An investigation into distributed constraint-directed factory scheduling , 1990, Sixth Conference on Artificial Intelligence for Applications.

[10]  James C. Bean,et al.  Matchup Scheduling with Multiple Resources, Release Dates and Disruptions , 1991, Oper. Res..

[11]  Subbarao Kambhampati,et al.  Integrating general purpose planners and specialized reasoners: case study of a hybrid planning architecture , 1993, IEEE Trans. Syst. Man Cybern..

[12]  Stephen F. Smith,et al.  Viewing scheduling as an opportunistic problem-solving process , 1988 .

[13]  Gilberto Nakamiti,et al.  Fuzzy distributed artificial intelligence systems , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[14]  Stephen F. Smith,et al.  ISIS—a knowledge‐based system for factory scheduling , 1984 .

[15]  C. McLean,et al.  A proposed hierarchical control model for automated manufacturing systems , 1986 .

[16]  Edmund H. Durfee,et al.  Coherent Cooperation Among Communicating Problem Solvers , 1987, IEEE Transactions on Computers.

[17]  A. H. Bond,et al.  An Analysis of Problems and Research in DAI , 1988 .

[18]  Neil A. Duffie,et al.  Fault-tolerant heterarchical control of heterogeneous manufacturing system entities , 1988 .