Authors:
T. J. Helliwell
1
;
B. Morgan
2
;
A. Vincent
3
;
G. Forgeoux
3
and
M. Mahfouf
1
Affiliations:
1
Automatic Control & Systems Engineering Department, University of Sheffield, Sheffield, U.K.
;
2
Advanced Manufacturing Research Center (AMRC), University of Sheffield, Sheffield, U.K.
;
3
Safran Landing Systems, Gloucester, U.K.
Keyword(s):
Reconfigurable Scheduling, Autonomous Planning, Discrete-Event Systems, Evolutionary Computing, Generative Models, Manufacturing Systems.
Abstract:
In this paper we introduce a theoretical basis for reconfigurable makespan scheduling that is computationally-efficient and general purpose in manufacturing. A full-scale scale case study for batch production in the aerospace industry is shown. A knowledge-based Discrete-Event System, based on a Timed Petri Net, is injected with the initial - current - state and simulated to generate trajectories that represent valid possible schedules or policies analogous to the Monte-Carlo Tree Search (MCTS) planning algorithm. A new, concise, evolutionary metaheuristic is proposed called Elitist Trajectory Mutation (ETM) in order to exploit high performing schedules in localising search and optimisation. The advantage of this approach is reconfigurability, extensibility and ability to be parallelised to enable satisficing performance for real-time applications such as intelligent industrial cyber-physical systems scheduling, autonomous control of distributed systems and active industrial informat
ics.
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