摘要
With the rapid expansion of smart display applications, LED manufacturing has increasingly relied on surface mount technology (SMT) to achieve high-density and high-precision component placement. However, the highmix, low-volume characteristics of LED production substantially increase the complexity of SMT scheduling and hinder efficient production planning. This study proposes a hybrid fuzzy genetic algorithm with Tabu search (HFGATS) to address the SMT scheduling problem (SMTSP), which is modeled as a flow shop scheduling problem with several real-world constraints, such as batch splitting, machine availability, material availability, designated machine rule, and sequence-dependent setup time constraints. Of the constraints considered, batch splitting poses the most critical limitation. In the LED industry, certain products involve large order volumes, necessitating batch splitting in advance to prevent excessive concentration of production on a single machine. However, batch splitting inevitably increases the number of machine setups. Therefore, decisions regarding batch splitting strongly affect the overall effectiveness of a scheduling system. The proposed framework integrates a fuzzy logic controller to adaptively tune the parameters of the genetic algorithm, and Tabu search is incorporated to enhance local search capability. To validate the proposed HFGATS, an empirical study was conducted using real production data collected from the SMT station of a Taiwanese LED manufacturer. The HFGATS obtained optimal solutions for small-scale problems; for large-scale problems, it outperformed traditional dispatching rules and general metaheuristic algorithms by minimizing total tardiness. This improvement can enhance the overall on-time delivery rate of the factory, thereby increasing customer satisfaction and improving the factory's profitability.
