Abstract
On-demand digital-print service offers mass customization and exemplifies personalized
manufacturing services. We describe a real-time and online optimization technique
based on genetic algorithms (GA) for print factory workflow optimization. We have
simulated digital-print factory manufacturing activities as a heterogeneous, concurrent
and integrated system. The simulation is based on a virtual print factory, which incorporates
real factory characteristics such as successive order acceptances, diverse production
lines, various resource types and quantities, and stochastic machine malfunctions.
The optimization objective is to reduce the number of orders that miss deadlines,
balance resource utilization, and ensure just-in time production. The optimization
technique has been integrated into the virtual factory as a factory scheduler and
resource assignment engine. Significant improvements have been achieved using the
GA heuristic compared to baseline methods that are currently implemented in an actual
industrial setting.
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