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Horizontally Elastic Not-First/Not-Last Filtering Algorithm for Cumulative Resource Constraint

机译:累积资源约束的水平弹性非优先/非最后过滤算法

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Fast and powerful propagators are the main key to the success of constraint programming on scheduling problems. It is, for example, the case with the CUMULATIVE constraint, which is used to model tasks sharing a resource of discrete capacity. In this paper, we propose a new not-firstot-last rule, which we call the horizontally elastic not-firstot-last, based on strong relaxation of the earliest completion time of a set of tasks. This computation is obtained when scheduling the tasks in a horizontally elastic way. We prove that the new rule is sound and is able to perform additional adjustments missed by the classic not-firstot-last rule. We use the new data structure called Profile to propose a O(n~3) filtering algorithm for a relaxed variant of the new rule where n is the number of tasks sharing the resource. We prove that the proposed algorithm still dominates the classic not-firstot-last algorithm. Experimental results on highly cumulative instances of resource constrained project scheduling problems (RCPSP) show that using this new algorithm can substantially improve the solving process of instances with an occasional and marginal increase of running time.
机译:快速而强大的传播器是对调度问题进行约束编程的成功的关键。例如,在具有CUMULATIVE约束的情况下,该约束用于对共享离散容量资源的任务进行建模。在本文中,我们基于一组任务的最早完成时间的强烈松弛,提出了一个新的非优先/非最后规则,我们将其称为水平弹性非优先/非最后规则。当以水平弹性方式调度任务时,将获得此计算。我们证明新规则是正确的,并且能够执行经典的非第一/非最后规则遗漏的其他调整。我们使用称为Profile的新数据结构为新规则的宽松变体提出O(n〜3)过滤算法,其中n是共享资源的任务数。我们证明了所提出的算法仍在经典的非先/非后算法中占主导地位。对资源受限的项目调度问题(RCPSP)的高度累积实例的实验结果表明,使用这种新算法可以显着改善实例的求解过程,而运行时间有时会略有增加。

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