Integrated optimization of performance-aware flexible job shop scheduling with operation-specific aging-time windows and limited-module machine reconfiguration

Reconfigurable manufacturing systems with auxiliary modules enhance flexibility but complicate resource allocation. Furthermore, traditional scheduling often ignores how post-processing aging treatments affect final product performance. This paper introduces a novel performance-aware flexible job shop scheduling problem with aging-time windows and machine reconfiguration (PFJSP-AW-MR) to simultaneously minimize total weighted tardiness and total performance deviation. First, a mixed-integer linear programming (MILP) model employing piecewise linear fitting is formulated to capture the non-linear temporal evolution of material properties. To solve this strongly NP-hard problem, an improved NSGA-II with a hybrid neighborhood search (INSGA-II_NS) is proposed. A problem-specific three-layer encoding resolves the spatio-temporal coupling of discrete configurations and continuous aging times. Moreover, a customized two-stage neighborhood search, integrating an improved K-insertion method on an extended disjunctive graph, significantly enhances local exploitation. By incorporating aging arcs into the disjunctive graph, the improved K-insertion method enables more accurate critical-path identification and targeted adjustment of operations related to both tardiness and performance deviation. Extensive benchmark experiments and a real-world industrial case demonstrate that INSGA-II_NS outperforms well-known multi-objective algorithms, effectively balancing manufacturing performance and production efficiency.