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Efficient Hybrid Genetic Algorithm for Resource Leveling via Activity Splitting

机译:通过活动分割实现资源均衡的高效混合遗传算法

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摘要

Resource leveling problem is an attractive field of research in project management. Traditionally, a basic assumption of this problem is that network activities could not be split. However, in real-world projects, some activities can be interrupted and resumed in different time intervals but activity splitting involves some cost. The main contribution of this paper lies in developing a practical algorithm for resource leveling in large-scale projects. A novel hybrid genetic algorithm is proposed to tackle multiple resource-leveling problems allowing activity splitting. The proposed genetic algorithm is equipped with a novel local search heuristic and a repair mechanism. To evaluate the performance of the algorithm, we have generated and solved a new set of network instances containing up to 5,000 activities with multiple resources. For small instances, we have extended and solved an existing mixed integer programming model to provide a basis for comparison. Computational results demonstrate that, for large networks, the proposed algorithm improves the leveling criterion at least by 76% over the early schedule solutions. A case study on a tunnel construction project has also been examined.
机译:资源均衡问题是项目管理研究的一个有吸引力的领域。传统上,此问题的基本假设是无法拆分网络活动。但是,在现实世界的项目中,某些活动可以在不同的时间间隔内中断和恢复,但活动拆分会涉及一些成本。本文的主要贡献在于开发一种用于大型项目中资源均衡的实用算法。提出了一种新颖的混合遗传算法来解决允许活动分割的多个资源均衡问题。提出的遗传算法具有新颖的局部搜索启发式和修复机制。为了评估算法的性能,我们已经生成并解决了一组新的网络实例,其中包含多达5,000个具有多种资源的活动。对于小型实例,我们扩展并解决了现有的混合整数编程模型,以提供比较的基础。计算结果表明,对于大型网络,所提出的算法比早期调度解决方案至少提高了76%的均衡标准。还研究了一个隧道建设项目的案例研究。

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