Operations management in customized production, models and optimization approaches

ROSSIT, DANIEL ALEJANDRO; RODRÍGUEZ, JEANETTE; CASTELLANO, CAMILA; MARCENAC, FELICITAS; VEGA, TADEO; ROSSIT, DIEGO GABRIEL

Montevideo

Congreso; Actas de las II Jornadas Uruguayas de Ciencias de la Computación 2025 ?Ida Holz?; 2026

UDELAR

The transition from mass production to mass customization in Industry 4.0/5.0 environments has introduced new challenges for production planning and scheduling. This presentation examines two complementary approaches to man-aging operations in personalized production systems. First, we analyze the flowshop scheduling problem with missing operations, where jobs may skip certain stages depending on customer specifications. This variability complicates se-quencing and requires balancing multiple objectives such as makespan, total tar-diness, and completion time. Multiobjective evolutionary algorithms (MOEAs) including NSGA-II, NSGA-III, MOEA/D, SPEA2, and AGEMOEA-II are shown to provide competitive solutions, enabling resilient and agile scheduling in heterogeneous shop-floor environments. Second, we explore additive manu-facturing (AM), a technology that typifies customization by enabling complex geometries and individualized designs. Here, the integrated problem of nesting and scheduling is addressed through hybrid heuristics and mathematical pro-gramming. Results demonstrate that heterogeneous builds improve makespan ef-ficiency and drastically reduce computational time, offering practical rules for grouping parts and enhancing industrial-scale AM competitiveness. By analyzing both approaches together, the study highlights how different optimization modelsconfront variability in personalized production. The findings provide actionable insights for improving efficiency, resilience, and adaptability in modern manu-facturing systems