A classical problem in grammatical inference is to identify a deterministic finite automaton (DFA) from a set of positive and negative examples. In this paper, we address the related -yet seemingly novel -problem of identifying a set of DFAs from examples that belong to different unknown simple regular languages. We propose two methods based on compression for clustering the observed positive examples. We apply our methods to a set of print jobs submitted to large industrial printers.
Abstract-Today's manufacturing systems are typically complex cyber-physical systems where the physical and control aspects interact with the scheduling decisions. Optimizing such facilities requires ordering jobs and configuring the manufacturing system for each job. This optimization problem can be described as a Multi-Objective Generalized TSP where conflicting objectives lead to a trade-off space. This is the first work to address this TSP variant, introducing a compositional heuristic suitable to online application.
Online scheduling of operations is essential to optimize productivity of exible manufacturing systems (FMSs) where manufacturing requests arrive on the y. An FMS processes products according to a particular ow through processing stations. This work focusses on online scheduling of re-entrant FMSs with ows using processing stations where products pass twice and with limited bu ering between processing stations. This kind of FMS is modelled as a re-entrant ow shop with due dates and sequence-dependent set-up times. Such ow shops can bene t from minimization of the time penalties incurred from set-up times. On top of an existing greedy scheduling heuristic we apply a meta-heuristic that simultaneously explores several alternatives considering trade-o s between the used metrics by the scheduling heuristic. We identify invariants to e ciently remove many infeasible scheduling options so that the running time of online implementations is improved. The resulting algorithm is much faster than the state of the art and produces schedules with on average 4.6% shorter makespan.
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