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<p>Hybrid manufacturing, which integrates additive, formative, and subtractive processes, is increasingly adopted to improve manufacturing efficiency and enhance product performance. However, the inherent complexity of hybrid process flows, characterized by reentrant operations and diverse machine capabilities, poses significant challenges for effective production scheduling and nesting. This paper proposes an integrated optimization framework addressing the two-stage problem of two-dimensional nesting and multi-objective scheduling in hybrid manufacturing systems. A dual mathematical model is formulated to simultaneously minimize makespan and maximize delivery rate. To solve this complex combinatorial problem, a multi-objective heuristic evolutionary algorithm (MOHEA) is developed, incorporating two domain-specific heuristic rules: one designed for batch nesting layout and the other for hybrid process scheduling. The proposed method is evaluated on a set of computational instances and compared with representative multi-objective optimization algorithms as well as different variants of the MOHEA framework. The results demonstrate superior convergence, solution diversity, and practical effectiveness for hybrid manufacturing scheduling applications.</p>

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Integrated scheduling and nesting optimization for hybrid additive/formative/subtractive manufacturing cells: A multi-objective heuristic approach

  • 期刊论文

  • 发表日期：

    2025年9月19日

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  • Chenfei Liu,
  • Tao Yuan,
  • He Shan,
  • Jihui Wang,
  • Shujun Chen

摘要

Hybrid manufacturing, which integrates additive, formative, and subtractive processes, is increasingly adopted to improve manufacturing efficiency and enhance product performance. However, the inherent complexity of hybrid process flows, characterized by reentrant operations and diverse machine capabilities, poses significant challenges for effective production scheduling and nesting. This paper proposes an integrated optimization framework addressing the two-stage problem of two-dimensional nesting and multi-objective scheduling in hybrid manufacturing systems. A dual mathematical model is formulated to simultaneously minimize makespan and maximize delivery rate. To solve this complex combinatorial problem, a multi-objective heuristic evolutionary algorithm (MOHEA) is developed, incorporating two domain-specific heuristic rules: one designed for batch nesting layout and the other for hybrid process scheduling. The proposed method is evaluated on a set of computational instances and compared with representative multi-objective optimization algorithms as well as different variants of the MOHEA framework. The results demonstrate superior convergence, solution diversity, and practical effectiveness for hybrid manufacturing scheduling applications.