Abstract The resource-constrained project scheduling problem (RCPSP) is one of the most studied problems in the project scheduling literature, and aims at constructing a project schedule with a minimum makespan that satisfies both the precedence relations of the network and the limited availability of the renewable resources. The problem has attracted attention due to its NP hardness status, and different algorithms have been proposed that solve a wide variety of RCPSP instances to optimality or near-optimality. In this paper, we analyse the hardness of this problem from an experimental point-of-view by testing different algorithms on a huge set of existing instances and detect which ones are difficult to solve. To that purpose, we propose a three-phased approach that makes use of five elementary blocks, well-performing algorithms and a huge amount of computational power to transform easy RCPSP instances into very hard ones. The purpose of this study is to create insight and understanding into what makes an RCPSP instance hard, and propose a new dataset that consists of a small set of instances that are impossible to solve with the algorithms currently existing in the literature. These instances should be as small as possible in terms of number of activities and resources, and should be as diverse as possible in terms of network structure and resource strictness. Such a dataset should enable researchers to focus their attention on the development of radically new algorithms to solve the RCPSP rather than gradually improving current algorithms that can solve the existing RCPSP instances only slightly better.

中文翻译：

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进入硬资源受限项目调度实例的核心

摘要 资源受限项目调度问题 (RCPSP) 是项目调度文献中研究最多的问题之一，其目的是构建一个满足网络优先关系和有限可用性的最小完工时间的项目调度。可再生资源。该问题因其 NP 硬度状态而受到关注，并且已经提出了不同的算法来解决各种 RCPSP 实例的最优或接近最优。在本文中，我们通过在大量现有实例上测试不同算法并检测哪些算法难以解决，从实验的角度分析了这个问题的难度。为此，我们提出了一种三阶段方法，利用五个基本块，性能良好的算法和大量的计算能力将简单的 RCPSP 实例转换为非常困难的实例。本研究的目的是深入了解是什么让 RCPSP 实例变得困难，并提出了一个新的数据集，该数据集由一小组实例组成，目前文献中存在的算法无法解决这些实例。这些实例在活动数量和资源数量方面应尽可能小，在网络结构和资源严格性方面应尽可能多样化。这样的数据集应该使研究人员能够将注意力集中在开发解决 RCPSP 的全新算法上，而不是逐步改进只能稍微更好地解决现有 RCPSP 实例的当前算法。