The interest in prefabricated building modules is constantly growing due to the increasing possibilities of analysing extensive data sets in computers and the popularity of BIM technology. The ability to manage the position, size and properties of many different elements make it easy to create and evaluate complete modular models at the design stage. Benefits of prefabrication include, among the others, decreased cost, minimisation of environmental impact, and reduced labour on-site. However, making structures and buildings suitable for prefabrication puts additional responsibility on the designer, who needs to choose the modular system, partition the structure and prepare detailed schedules. The article refers to digital control over modular design in the context of the increasing complexity of structures. It focuses on methods and tools that either reduce the designer’s labour or provide him with information that can be used to optimise the structure in terms of efficiency or cost. The article organises the existing trends and presents three experiments on algorithmic control of modular structures to outline the differences in computational methods suitable for particular technologies: masonry, steel, glass and timber construction. The research illustrated in the article was undertaken in response to the need to develop construction technologies in line with the sustainable development trend.

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.