Advanced constraint programming formulations for additive manufacturing machine scheduling problems

In comparison with traditional subtractive manufacturing techniques, additive manufacturing (AM) enables fabricating complex parts through a layer-by-layer process. AM makes it possible to produce one-piece and lightweight functional products, which are traditionally made from several parts. This paper introduces constraint programming (CP) models to minimise makespan in single, parallel identical and parallel unrelated AM machine scheduling environments for selective laser melting. Alternative CP formulations are explored to improve efficiency. The proposed CP model significantly benefits from the introduction of interval variables to replace binary assignment variables, and pre-definitions to narrow the search space, resulting in increased search performance. A computational study has been conducted to compare the performance of our proposed CP model with both a mixed-integer programming and a genetic algorithm from existing literature, evaluating improvements made to its search capability. Computational results indicate that the proposed CP model can obtain high-quality solutions in a timely manner even for several large-size instances.与传统减材制造工艺相比，增材制造（additive manufacturing, AM）可通过逐层累加的工艺制造复杂零部件。增材制造能够实现一体式轻量化功能产品的生产，而这类产品传统上需由多个零件组装而成。本文针对选择性激光熔融（selective laser melting）场景下的三类增材制造设备调度环境——单台设备、同构并行设备及异构并行设备，提出约束规划（constraint programming, CP）模型以最小化总完工时间。文中探索了多种约束规划建模方案以提升求解效率：所提出的约束规划模型通过引入区间变量替代二元赋值变量，以及通过预定义操作缩小搜索空间，显著优化了搜索性能。本文开展了计算对比实验，将所提约束规划模型与现有文献中的混合整数规划（mixed-integer programming）、遗传算法（genetic algorithm）的求解性能进行对比，以评估其搜索能力的提升效果。计算结果表明，即使针对部分大尺寸算例，所提约束规划模型仍可及时获得高质量求解结果。

搜集汇总

背景与挑战

背景概述

该数据集聚焦于增材制造（AM）中的机器调度问题，特别是针对选择性激光熔化技术，提出了高级约束编程（CP）模型以最小化制造时间。数据集包含相关测试数据和文档，支持比较CP模型与混合整数规划、遗传算法的性能，旨在为AM调度提供高效解决方案。