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<p>This research presents a novel energy-efficient task sequencing method for manufacturing operations involving multiple processing points, such as precision drilling and contact welding. The problem is formulated as a multi-dimensional weighted variant of the Traveling Salesman Problem (TSP), and solved using a Multi-gate Mixture of Experts (MMOE) neural architecture. Unlike previous approaches that require separate models for each TSP size, our method employs a single neural network to handle TSPs of all sizes, significantly improving scalability and reducing training overhead. With an uncertainty-based loss weighting strategy, the model effectively balances multiple learning objectives. Experiments show that MMOE-9 achieves performance comparable to state-of-the-art methods with only one-third of the parameters of NAR4TSP, and its training time is similar to that of a single TSP100 model. Further, we extend the model to cover 91 TSP sizes (from 10 to 100) within the same unified framework, demonstrating strong generalization across scales.</p>

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Scheduling all-scale multi-point manufacturing problems with a single neural model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月24日

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  • Jie Liu,
  • Hwa Jen Yap,
  • Anis Salwa Mohd Khairuddin

摘要

This research presents a novel energy-efficient task sequencing method for manufacturing operations involving multiple processing points, such as precision drilling and contact welding. The problem is formulated as a multi-dimensional weighted variant of the Traveling Salesman Problem (TSP), and solved using a Multi-gate Mixture of Experts (MMOE) neural architecture. Unlike previous approaches that require separate models for each TSP size, our method employs a single neural network to handle TSPs of all sizes, significantly improving scalability and reducing training overhead. With an uncertainty-based loss weighting strategy, the model effectively balances multiple learning objectives. Experiments show that MMOE-9 achieves performance comparable to state-of-the-art methods with only one-third of the parameters of NAR4TSP, and its training time is similar to that of a single TSP100 model. Further, we extend the model to cover 91 TSP sizes (from 10 to 100) within the same unified framework, demonstrating strong generalization across scales.