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The thermal comfort of individuals is considered an important factor that affects the health, well-being, and productivity of the occupants. However, only a small proportion of people are satisfied with the thermal environment of their current workplace. Therefore, this paper proposes a novel framework to simulate and optimize thermal comfort by controlling room conditions and matching them with occupants. The method is developed based on personalized thermal comfort prediction models and the Large Neighborhood Search (LNS) algorithm. To illustrate and validate the algorithm, a case study is provided. The results compare the thermal comfort of the occupants before and after the optimization and show a significant improvement in the thermal comfort. The proposed simulation method is proven to be feasible and efficient in providing an optimal match of occupants and rooms with specific settings, and therefore, can be of great value for the decision-making of the building management. 

Environmental change interacts with population migration in complex ways that depend on interactions between impacts on individual households and on communities. These coupled individual-collective dynamics make agent-based simulations useful for studying environmental migration. We present an original agent-based model that simulates environment-migration dynamics in terms of the impacts of natural hazards on labor markets in rural communities, with households deciding whether to migrate based on maximizing their expected income. We use a pattern-oriented approach that seeks to reproduce observed patterns of environmentally-driven migration in Bangladesh. The model is parameterized with empirical data and unknown parameters are calibrated to reproduce the observed patterns. This model can reproduce these patterns, but only for a narrow range of parameters. Future work will compare income-maximizing decisions to psychologically complex decision heuristics that include non-economic considerations.