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Natural disasters, such as hurricanes, earthquakes, and tsunamis often cause large-scale destruction in residential areas. In the aftermath of these disasters, emergency management agencies need to urgently develop and implement a temporary housing plan that provides displaced families with satisfactory and safe accommodations. This paper presents the computational implementation of a newly developed multiobjective optimization model to support decision-makers in emergency management agencies in optimizing large-scale temporary housing arrangements. The model is capable of simultaneously minimizing ͑1͒ postdisaster social and economic disruptions suffered by displaced families; ͑2͒ temporary housing vulnerabilities to postdisaster hazards; ͑3͒ adverse environmental impacts on host communities; and ͑4͒ public expenditures on temporary housing. The model is implemented in four main phases and it incorporates four optimization modules to enable optimizing each of the aforementioned important objectives. A large-scale temporary housing application example is presented to demonstrate the unique capabilities of the model and illustrate the performed computations in each of the implementation phases.

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In the aftermath of catastrophic natural disasters such as hurricanes, tsunamis and earthquakes, emergency management agencies come under intense pressure to provide temporary housing to address the large-scale displacement of the vulnerable population. Temporary housing is essential to enable displaced families to reestablish their normal daily activities until permanent housing solutions can be provided. Temporary housing decisions, however, have often been criticized for their failure to fulfil the socioeconomic needs of the displaced families within acceptable budgets. This paper presents the development of (1) socioeconomic disruption metrics that are capable of quantifying the socioeconomic impacts of temporary housing decisions on displaced populations; and (2) a robust multi-objective optimization model for temporary housing that is capable of simultaneously minimizing socioeconomic disruptions and public expenditures in an effective and efficient manner. A large-scale application example is optimized to illustrate the use of the model and demonstrate its capabilities ingenerating optimal plans for realistic temporary housing problems.

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