LAUSR

Abstract 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.