基于净现值最大化的项目调度算法的实证评价
Empirical Evaluation of Project Scheduling Algorithms for Maximization of the Net Present Value
摘要：
本文提出了一个实证性能分析的三个项目调度算法处理最大化项目的净现值与不受限制的资源。所选的算法，是最近在文献中引用的，是：递归搜索（RS），最速上升法（SAA）和混合搜索（HS）。这项研究的主要动机是缺乏有关RS，SAA和HS算法的计算复杂性的知识，因为迄今为止的所有研究都显示出一些差距的分析。此外，迄今为止进行的经验分析没有考虑这样一个事实，即一个算法（HS）使用双搜索策略，这显着提高了算法的性能，而其他人没有。为了得到一个公平的性能比较，我们实现了双重搜索策略的其他两个算法（RS和SAA），新的算法被称为递归搜索前向-后向（RSFB）和最速上升法前向-后向（SAAFB）。算法RSFB，SAAFB和HS提交了三个不同的项目网络抽样特征的析因实验。使用广义线性模型（GLM）统计建模技术对结果进行分析，显示：a）RSFB、SAAFB和HS的一般计算成本; b）在生成树中重新开始搜索的成本作为算法总成本的一部分; c）算法结果分布之间的统计学显著差异。
英文摘要：
This paper presents an empirical performance analysis of three project scheduling algorithms dealing with maximizing projects' net present value with unrestricted resources. The selected algorithms, being the most recently cited in the literature, are: Recursive Search (RS), Steepest Ascent Approach (SAA) and Hybrid Search (HS). The main motivation for this research is the lack of knowledge about the computational complexities of the RS, SAA, and HS algorithms, since all studies to date show some gaps in the analysis. Furthermore, the empirical analysis performed to date does not consider the fact that one algorithm (HS) uses a dual search strategy, which markedly improved the algorithm's performance, while the others don't. In order to obtain a fair performance comparison, we implemented the dual search strategy into the other two algorithms (RS and SAA), and the new algorithms were called Recursive Search Forward-Backward (RSFB) and Steepest Ascent Approach Forward-Backward (SAAFB). The algorithms RSFB, SAAFB, and HS were submitted to a factorial experiment with three different project network sampling characteristics. The results were analyzed using the Generalized Linear Models (GLM) statistical modeling technique that showed: a) the general computational costs of RSFB, SAAFB, and HS; b) the costs of restarting the search in the spanning tree as part of the total cost of the algorithms; c) and statistically significant differences between the distributions of the algorithms' results.
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