A hierarchical decoupling framework and hybrid adaptive genetic algorithm for multimachine dual-arm PCB placement scheduling

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Abstract

Printed circuit boards (PCBs) are an indispensable part of electronic products. As the consumer electronics market continues to expand, the PCB assembly rate has become a key factor limiting production line efficiency. PCB placement scheduling is a highly complex, multivariable combinatorial optimization problem characterized by nonlinearity and high coupling. From a system perspective, this scheduling problem is modeled as a complex network optimization, wherein the intricate coupling of resources, time, and processes parallels the node interdependencies and information propagation mechanisms inherent in complex system architectures. Given its nondeterministic polynomial-time (NP)-hard nature and the dynamic constraints of the production environment, this study develops a rigorous mathematical model based on motion trajectory analysis and proposes a three-stage hierarchical decoupling framework. This decomposition transforms the high-dimensional network complexity into manageable subtasks while preserving essential variable interdependencies. To solve this complex network optimization, the framework integrates a hybrid adaptive genetic algorithm (HAGA) implemented through a three-level strategy.Initially, an improved adaptive genetic algorithm (IAGA) handles global load balancing; subsequently, an improved spectral clustering algorithm groups components to minimize tool-changing overhead; finally, a hybrid genetic algorithm and ant colony optimization (GA-ACO) optimizes synchronized intramachine paths. Unlike partial-stage methods, this holistic approach ensures synergistic optimization from global network equilibrium to local machine precision. Numerical experiments using real-world data from Dalian Rijia Co., Ltd. demonstrate that HAGA achieves at least a 10% reduction in the production cycle. This research proves that the hierarchical strategy effectively balances computational efficiency with global optimization performance.

Keywords

printed circuit board / multimachine dual-arm system / hybrid adaptive genetic algorithm (HAGA) / spectral clustering algorithm / production line load balancing

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