New AI Algorithm Cuts Production Delays in Complex Manufacturing Plants
Researchers have developed an optimization system that reduces scheduling delays in factories that produce complex, custom-built products like marine engines. The approach uses real rejection rates to intelligently sequence production, helping manufacturers meet deadlines while managing quality issues—a problem that costs factories millions in rework and missed deliveries.
Originaltitel: Simulation-Based Knowledge-Driven Optimization for Efficient Production Sequencing in Hybrid Flow Shops
Marinmotortillverkare som arbetar med montage-till-order kan minska leveransförseningar med 18 procent genom att förlänga produktionsplaneringshorisonten från tre till fem dagar. Högskolan i Skövde och Uppsala University utvecklade en simuleringsbaserad optimeringsmetod som använder historiska felfrekvenser för att gruppera motorvarianter efter felrisk. En förfinad genetisk algoritm (NSGAIIAB-AD) sekvensialiserar produktionen för att minska köbildning vid kvalitetskontroll och prioriterar motorer med tidigare leveransdatum. Metoden uppnår 10 procent högre genomströmning än nuvarande schemaläggning. Eftersom felaktiga motorer repareras istället för att kasseras, blir sekvensering kritisk för att undvika trängsel och förseningar. För produktionschefer med hybridflödesshoppar relevanta är resultaten nu — planeringshorisonten är enkelt att justera och algoritmen löses på befintlig data utan nya investeringar.
<p>In today’s advanced manufacturing landscape, optimizing production processes is crucial for maintaining competitiveness. Among various optimization challenges, production sequencing in make-to-order hybrid flow shops (HFSs) stands out as particularly complex. This study investigates production sequencing in an HFS from the marine engine production industry, characterized by feed-forward quality inspection (FFQI). In FFQI, rejected engines must be repaired rather than scrapped. The complexity is further heightened by the fact that repair capacity is usually limited to a few engines and rejection at quality inspection leads to sequence scrambling at downstream stations. To address this issue, this study employs simulation-based, knowledge-driven optimization that utilizes real-world data on the rejection rates of different engine variants. This data is used to cluster the variants into three groups with different risks of rejection at quality inspection, informing production sequencing decisions. A non-dominated sorting genetic algorithm, enhanced with anti-block (AB) and anti-delay (AD) strategies (NSGAIIAB-AD), is developed to optimize throughput and delivery delay. AB aims to mitigate the succession of high-risk product variants, minimizing blockage probabilities in the quality inspection stage. AD prioritizes engines with earlier due dates from the same risk category to prevent unnecessary delivery delays. The study also evaluates the impact of extending planning horizons beyond the current 3-day standard. Results demonstrate the effectiveness of the AB and AD strategies, yielding a 10% improvement in average current throughput. Moreover, adopting a 5-day planning horizon leads to an 18% decrease in average delay compared to the current 3-day horizon.</p>