Minimization of socioeconomic disruption for displaced populations following disasters

Abstract

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.

An evaluation of medium-term recovery processes after the 6 April 2009 earthquake in L'Aquila, Central Italy

“…One was the degradation of the infrastructure and services of the affected area and another was loss of productive employment. It is axiomatic that recovery from disaster needs sources of work and income (El-Anwar et al, 2010). The primary source of these is usually the construction industry (Le Masurier et al, 2008), although, if recovery does not empower other sectors of the economy, the result can be a 'boom' followed by a 'bust' when the reconstruction is either finished or stalls (Haas et al, 1977).…”

Maximising the net social benefit of the construction of post‐disaster alternative housing projects

“…Previous research developed metrics to enable objective quantification of the multiple and significant consequences of decisions on temporary housing and alternative housing for the socioeconomic welfare of displaced families (El-Anwar, El-Rayes, and Elnashai, 2008, 2010b, 2010c. These metrics include:…”

“…Several models have been developed to optimise decisions on either temporary housing or alternative housing. For example, a number of optimisation models have been developed to identify the optimal temporary housing assignments that can minimise socioeconomic disruption for displaced families considering various social and economic factors (El-Anwar and El-Rayes, 2007;El-Anwar, El-Rayes, and Elnashai, 2010c;Rakes et al, 2010). Each of these models considers a subset of a list of socioeconomic factors, including educational and employment opportunities at the housing location, availability of healthcare facilities, access to public transportation and other utilities and services, neighbourhood safety, displacement distance from preferred location, housing quality, and temporary housing delivery time.…”

“…The socioeconomic model is designed to quantify the positive social and economic impacts of offering alternative housing units (instead of temporary housing) on displaced families. Previous research developed metrics to enable objective quantification of the multiple and significant consequences of decisions on temporary housing and alternative housing for the socioeconomic welfare of displaced families (El-Anwar, El-Rayes, and Elnashai, 2008Elnashai, , 2010bElnashai, , 2010c. These metrics include:…”

“…The collective socioeconomic effects of these eight metrics can be computed using indexes that aggregate the weighted performance of any candidate housing project in relation to these eight metrics. Examples of these indexes for temporary and alternative housing projects include the socioeconomic disruption index (El-Anwar, El-Rayes, and Elnashai, 2008Elnashai, , 2010c and the social welfare index (El-Anwar, El-Rayes, and Elnashai, 2010b). The proposed model adopts a variation derived from these indexes to compute a socioeconomic welfare index (SWI) for each candidate housing project, as shown in Equation 1.…”