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In the context of postdisaster recovery, housing reestablishment has been known to be a critical factor as it has ripple effects on the overall timing of recovery. This reestablishment is based on the dynamic behavior of homeowners in the aftermath of disasters. Therefore, to have an effective disaster planning it is essential that policy makers recognize this dynamic aspect of disaster recovery to effectively enhance the recovery process. This entails a thorough understanding of how homeowners react to recovery signals. One of the main sources of these signals comes from neighbors’ activities including reconstruction and relocation. The goal of this research is to develop a preliminary temporospatial agent‐based model that can account for homeowners’ dynamic interactions with their neighbors. These interactions are based on homeowners’ equilibrium strategy derived from a game‐theoretical approach, which is modeled in a multiagent system framework. The results highlight the significant impact of discount factor and the accuracy of the signals on homeowners’ reconstruction decisions as well as formation clusters by reconstructed properties.

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Automating the development of construction schedules has been an interesting topic for researchers around the world for almost three decades. Researchers have approached solving scheduling problems with different tools and techniques. Whenever a new artificial intelligence or optimization tool has been introduced, researchers in the construction field have tried to use it to find the answer to one of their key problems-the "better" construction schedule. Each researcher defines this "better" slightly different. This article reviews the research on automation in construction scheduling from 1985 to 2014. It also covers the topic using different approaches, including case-based reasoning, knowledge-based approaches, model-based approaches, genetic algorithms, expert systems, neural networks, and other methods. The synthesis of the results highlights the share of the aforementioned methods in tackling the scheduling challenge, with genetic algorithms shown to be the most dominant approach. Although the synthesis reveals the high applicability of genetic algorithms to the different aspects of managing a project, including schedule, cost, and quality, it exposed a more limited project management application for the other methods.