A Dense Pixel-Based Genetic Algorithm for Additive Manufacturing Scheduling Problem

Additive manufacturing has revolutionized the way to design and manufacture products by enabling complex geometries and on-demand production. However, 3D printing without scheduling is time-consuming and space-inefficient. To address these issues, a dense pixel-based genetic algorithm (DPGA) is proposed. To fast characterize 3D parts, a tolerant pixel matrix (TPM) is adopted for abstraction. Based on the TPM, a double-layer encoding scheme is designed to represent the solutions. An active decoding strategy is designed to maximize space utilization, which can improve the quality of schemes decoded with the same encoding. In the section of operator design, an initialization strategy based on load balancing is developed, which can effectively improve the quality of initial population. Additionally, a population rebirth mechanism is designed to efficiently escape from local optima. Computational experiments demonstrate the effectiveness of DPGA in solving additive manufacturing scheduling problems with varying sizes and complexities. The proposed DPGA outperforms traditional genetic algorithm and other state-of-the-art methods in terms of processing time and packing density.