Scholarlink

<p>Additive Manufacturing (AM) has emerged as a transformative technology with wide-ranging industrial applications. Despite its rapid development, research on production scheduling in AM systems remains limited, with fewer than 100 related journal articles indexed in Scopus to date. To investigate this underexplored area, this study addresses the critical issue of minimizing total weighted tardiness in customer order scheduling for unrelated parallel powder bed fusion systems. A novel metaheuristic, the Late-acceptance and Restart Iterated Greedy (LRIG) algorithm, is proposed to effectively and efficiently solve this problem. Extensive computational experiments on a benchmark dataset demonstrate that the proposed algorithm achieves superior performance, delivering high-quality solutions within just 25% of the computation time required by state-of-the-art methods. The findings not only provide an effective scheduling methodology for the AM industry but also establish a valuable benchmark for future research, bridging the gap between theoretical advancements and practical applications in AM scheduling.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Advanced scheduling for unrelated parallel additive manufacturing systems

  • 期刊论文

  • 发表日期：

    2026年7月9日

查看全文
  • Shih-Wei Lin,
  • Kuo-Ching Ying

摘要

Additive Manufacturing (AM) has emerged as a transformative technology with wide-ranging industrial applications. Despite its rapid development, research on production scheduling in AM systems remains limited, with fewer than 100 related journal articles indexed in Scopus to date. To investigate this underexplored area, this study addresses the critical issue of minimizing total weighted tardiness in customer order scheduling for unrelated parallel powder bed fusion systems. A novel metaheuristic, the Late-acceptance and Restart Iterated Greedy (LRIG) algorithm, is proposed to effectively and efficiently solve this problem. Extensive computational experiments on a benchmark dataset demonstrate that the proposed algorithm achieves superior performance, delivering high-quality solutions within just 25% of the computation time required by state-of-the-art methods. The findings not only provide an effective scheduling methodology for the AM industry but also establish a valuable benchmark for future research, bridging the gap between theoretical advancements and practical applications in AM scheduling.