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A hyper-heuristic based ensemble genetic programming approach for stochastic resource constrained project scheduling problem

机译:用于随机资源受限项目调度问题的超启发式基于集成遗传编程方法

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摘要

In project scheduling studies, to the best of our knowledge, the hyper-heuristic collaborative scheduling is first-time applied to project scheduling with random activity durations. A hyper-heuristic based ensemble genetic programming (HH-EGP) method is proposed for solving stochastic resource constrained project scheduling problem (SRCPSP) by evolving an ensemble of priority rules (PRs). The proposed approach features with (1) integrating the critical path method into the resource-based policy class to generate schedules; (2) improving the existing single hyper-heuristic project scheduling research to construct a suitable solution space for solving SRCPSP; and (3) bettering genetic evolution of each subpopulation from a decision ensemble with three different local searches in corporation with discriminant mutation and discriminant population renewal. In addition, a sequence voting mechanism is designed to deal with collaborative decision-making in the scheduling process for SRCPSP. The benchmark PSPLIB is performed to verify the advantage of the HH-EGP over heuristics, meta-heuristics and the single hyper-heuristic approaches.
机译:在项目调度研究中,据我们所知,超级启发式协作调度首先应用于随机活动持续时间的项目调度。提出了一种超级启发式基于集成遗传编程(HH-EGP)方法,用于通过演变优先规则(PRS)的集合来解决随机资源受限的项目调度问题(SRCPSP)。建议的方法功能(1)将关键路径方法集成到基于资源的策略类以生成计划; (2)改善现有的单一超高启发式项目调度研究,为解决SRCPSP构建合适的解决方案空间; (3)从判别突变和判别人口更新的公司中,从决策集合中从决策集合中获得各个亚群的遗传演变。此外,序列投票机制旨在处理SRCPSP的调度过程中的协同决策。执行基准PSPLIB以验证HH-EGP对启发式的优势,META-HEURISTICS和单一超级启发式方法。

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