Modular construction offers several advantages, such as safe working conditions, fast construction, and high-quality end products. The project scheduling of modular construction should consider the time involved in the factory production, transportation, and erection processes of the units given limited resources (e.g., the number of modular units, available transportation, and workers) and project restrictions (e.g., the sequences of manufacturing production and on-site installation). However, current modular construction project scheduling techniques and previous academic approaches cannot deal with the overall project processes and production/on-site installation sequences simultaneously. This is one reason few mass-production multiple projects exist in the manufacturing industry. Moreover, it can lead to inappropriate project management guidelines and fail to minimize the overall project duration. This paper suggests a model for modular construction projects scheduling using a genetic algorithm. The results show that implementing both overall project processes and project requirements is an efficient way to develop a modular construction project schedule. The proposed optimization tool will help project managers allocate resources efficiently and reduce the completion time of multiple projects.

The modular construction method is efficient for line production processes in factories. However, many modular unit production factories are having difficulty that how to prevent bottleneck effect, rework, and work congestion through the appropriate level of work packages composition by each work stations to achieve the smooth production. To solve these problems and improve production flow, the authors of this paper propose an optimized construction process at the factory production stage using a Dependency Structure Matrix (DSM) that considers the process approach based on flow of construction information in work activities. To test its effectiveness, the proposed process was applied to a modular factory and was found to reduce the number of main activities from 65 to 43 in comparison with as-is processes. Using this method, a manufacturing manager can thus improve production flow by analyzing the characteristics of work activities that form the plan for the manufacturing sequence decision phase. Moreover, while modular activities are independent of main assembly, there exist strong internal dependencies among these activities such that the manager can check the quality of each module. Furthermore, the research here might help save time in the manufacturing phase in factories.