Redundancy based methods are proactive scheduling methods for solving the Project
Scheduling Problem (PSP) with non-deterministic activities duration. The fundamental
strategy of these methods is to estimate the activities duration by adding extra time to the
original duration. The extra time allows to consider the risks that may affect the activities
durations and to reduce the number of adjustments to the baseline generated for the project.
In this article, four methods based on redundancies were proposed and compared from two
robustness indicators. These indicators were calculated after running a simulation process.
On the other hand, linear programming was applied as the solution technique to generate
the baselines of 480 projects analyzed. Finally, the results obtained allowed to identify the
most adequate method to solve the PSP with probabilistic activity duration and generate
robust baselines.
Most scheduling methods used in the construction industry to plan repetitive projects assume that process durations are deterministic. This assumption is acceptable if actions are taken to reduce the impact of random phenomena or if the impact is low. However, construction projects at large are notorious for their susceptibility to the naturally volatile conditions of their implementation. It is unwise to ignore this fact while preparing construction schedules. Repetitive scheduling methods developed so far do respond to many constructionspecific needs, e.g. of smooth resource flow (continuity of work of construction crews) and the continuity of works. The main focus of schedule optimization is minimizing the total time to complete. This means reducing idle time, but idle time may serve as a buffer in case of disruptions. Disruptions just happen and make optimized schedules expire. As process durations are random, the project may be delayed and the crews’ workflow may be severely affected to the detriment of the project budget and profits. For this reason, the authors put forward a novel approach to scheduling repetitive processes. It aims to reduce the probability of missing the deadline and, at the same time, to reduce resource idle time. Discrete simulation is applied to evaluate feasible solutions (sequence of units) in terms of schedule robustness.