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Resource-constrained multi-project scheduling: benchmark datasets and decoupled scheduling
Journal of Scheduling ( IF 1.4 ) Pub Date : 2020-04-10 , DOI: 10.1007/s10951-020-00651-w
Rob Van Eynde , Mario Vanhoucke

In this paper, we propose a new dataset for the resource-constrained multi-project scheduling problem and evaluate the performance of multi-project extensions of the single-project schedule generation schemes. This manuscript contributes to the existing research in three ways. First, we provide an overview of existing benchmark datasets and classify the multi-project literature based on the type of datasets that are used in these studies. Furthermore, we evaluate the existing summary measures that are used to classify instances and provide adaptations to the data generation procedure of Browning and Yassine (J Scheduling 13(2):143-161, 2010a). With this adapted generator we propose a new dataset that is complimentary to the existing ones. Second, we propose decoupled versions of the single-project scheduling schemes, building on insights from the existing literature. A computational experiment shows that the decoupled variants outperform the existing priority rule heuristics and that the best priority rules differ for the two objective functions under study. Furthermore, we analyse the effect of the different parameters on the performance of the heuristics. Third, we implement a genetic algorithm that incorporates specific multi-project operators and test it on all datasets. The experiment shows that the new datasets are challenging and provide opportunities for future research.

中文翻译:

资源受限的多项目调度:基准数据集和解耦调度

在本文中,我们为资源受限的多项目调度问题提出了一个新的数据集,并评估了单项目调度生成方案的多项目扩展的性能。这份手稿在三个方面对现有研究做出了贡献。首先,我们概述了现有的基准数据集,并根据这些研究中使用的数据集类型对多项目文献进行了分类。此外,我们评估了用于对实例进行分类并提供对 Browning 和 Yassine 的数据生成程序(J Scheduling 13(2):143-161, 2010a)的适应的现有汇总度量。有了这个适应的生成器,我们提出了一个新的数据集,它是对现有数据集的补充。其次,我们提出了单项目调度方案的解耦版本,基于现有文献的见解。计算实验表明,解耦变体优于现有的优先规则启发式算法,并且对于所研究的两个目标函数,最佳优先规则不同。此外,我们分析了不同参数对启发式算法性能的影响。第三,我们实现了一种遗传算法,该算法结合了特定的多项目运算符并在所有数据集上对其进行了测试。实验表明,新数据集具有挑战性,并为未来的研究提供了机会。我们分析了不同参数对启发式算法性能的影响。第三,我们实现了一种遗传算法,该算法结合了特定的多项目运算符并在所有数据集上对其进行了测试。实验表明,新数据集具有挑战性,并为未来的研究提供了机会。我们分析了不同参数对启发式算法性能的影响。第三,我们实现了一种遗传算法,该算法结合了特定的多项目运算符并在所有数据集上对其进行了测试。实验表明,新数据集具有挑战性,并为未来的研究提供了机会。
更新日期:2020-04-10
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