解释累积资源约束下的时间表-边缘查找传播
Explaining Time-Table-Edge-Finding Propagation for the Cumulative Resource Constraint
摘要：
累积资源约束可以模拟调度问题中的稀缺资源或包装和切割问题中的一个维度。为了用约束编程求解器有效地解决这样的问题，重要的是具有用于累积资源约束的强且快速的传播器。一个这样的传播器是最近开发的时间表边缘发现传播器，它认为在边缘发现传播的当前资源配置文件。最近，懒惰条款生成求解器，即约束编程求解器结合nogood学习，已被证明是优秀的解决调度和切割问题。对于此类求解器来说，对传播原因的简洁准确的解释对于强nogood学习至关重要。在本文中，我们开发了第一个解释版本的时间表边缘查找传播和资源受限的项目调度问题，从各种标准的基准套件的初步结果。在标准基准测试套件PSPLib上，我们能够关闭一个开放实例，并提高大约60%的剩余开放实例的下限。此外，其中6起案件已结案。
英文摘要：
Cumulative resource constraints can model scarce resources in scheduling problems or a dimension in packing and cutting problems. In order to efficiently solve such problems with a constraint programming solver, it is important to have strong and fast propagators for cumulative resource constraints. One such propagator is the recently developed time-table-edge-finding propagator, which considers the current resource profile during the edge-finding propagation. Recently, lazy clause generation solvers, i.e. constraint programming solvers incorporating nogood learning, have proved to be excellent at solving scheduling and cutting problems. For such solvers, concise and accurate explanations of the reasons for propagation are essential for strong nogood learning. In this paper, we develop the first explaining version of time-table-edge-finding propagation and show preliminary results on resource-constrained project scheduling problems from various standard benchmark suites. On the standard benchmark suite PSPLib, we were able to close one open instance and to improve the lower bound of about 60% of the remaining open instances. Moreover, 6 of those instances were closed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
