Real-Time and Online Digital-Print Factory Workflow Optimization

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.