Abstract
Optimization of industrial processes such as manufacturing cells canhave great impact on their performance. Finding optimal solutions to theselarge-scale systems is, however, a complex problem. They typically include multiplesubsystems, and the search space generally grows exponentially with each subsystem.In previous work we proposed CompositionalOptimization as a method to solve these type of problems. Thisintegrates optimization with techniques from compositional supervisory control,dividing the optimization into separate sub-problems. The main purpose is tomitigate the state explosion problem, but a bonusis that the individual sub-problems can be solved using parallel computation, makingthe method even more scalable. This paper further improves on compositionaloptimization with a novel synchronization method, called partial time-weighted synchronization (PTWS), that is specificallydesigned for time-optimal control of asynchronous systems. The benefit is itsability to combine the behaviour of asynchronous subsystems without introducingadditional states or transitions. The method also reduces the search space furtherby integrating an optimization heuristic that removes many non-optimal or redundantsolutions already during synchronization. Results in this paper show thatcompositional optimization efficiently generates global optimal solutions tolarge-scale realistic optimization problems, too big to solve when based ontraditional monolithic models. It is also shown that the introduction of PTWSdrastically decreases the total search space of the optimization compared toprevious work.
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