Traditional Critical Path Method (CPM) and its different generalizations have become the prevailing technique in managing complex projects over recent decades. Among them, Precedence Diagramming Method (PDM), with its four precedence relationships, has become the most popular due to its increased flexibility in modeling complex project logic. Despite the four well-known precedence relationships, CPM/PDM still suffers from modeling shortcomings.

Developments such as conditional logic, maximal relationships, point-to-point relationships, continuous relationships, and non-linear activities attempt to remedy these problems. However, these improvements require more complex and sometimes iterative time analysis.

Monte Carlo (MC) simulation, which calculates the effects of risks on the project duration, also requires numerous repetitions of the deterministic time analysis. It is especially true if the network contains the developments mentioned above. In these cases, the time analysis speed is of great importance.

The goal of this study is a) to implement different algorithms for time analysis that perform better than the traditional time analysis, b) to compare their speed on large-scale artificially created projects, c) to make suggestions regarding which algorithms are suitable in different cases d) to develop a database containing artificially created networks to serve as a basis for benchmarking for researchers and developers working in the field of project planning.

Comparisons were based on networks with at least 1000 activities. Results show that, depending on the sample network's structure, different algorithms perform well. Therefore, different time analysis algorithms are necessary to implement into the scheduling tools, and they can decide which algorithms perform better.

在过去的几十年中，传统的关键路径方法（CPM）及其不同的概括已成为管理复杂项目的主要技术。其中，具有四个优先级关系的“优先级图示方法（PDM）”由于其在复杂项目逻辑建模中增加的灵活性而成为最受欢迎的方法。尽管存在四个众所周知的优先级关系，但是CPM / PDM仍然存在建模缺陷。

条件逻辑，最大关系，点对点关系，连续关系和非线性活动之类的开发试图解决这些问题。但是，这些改进需要更复杂的，有时是迭代的时间分析。

计算风险对项目工期的影响的蒙特卡洛（MC）模拟也需要重复进行确定性时间分析。如果网络包含上述发展，则尤其如此。在这些情况下，时间分析速度非常重要。

这项研究的目的是：a）实施比传统时间分析性能更好的不同的时间分析算法； b）在大型人工创建的项目上比较它们的速度； c）提出关于哪种算法适合不同的建议。案例d）开发一个包含人为创建的网络的数据库，以此作为在项目规划领域工作的研究人员和开发人员进行基准测试的基础。

比较是基于至少有1000项活动的网络进行的。结果表明，根据示例网络的结构，不同的算法性能良好。因此，有必要将不同的时间分析算法实施到调度工具中，并且它们可以决定哪种算法性能更好。