Omar El-Anwar

Optimization research: Enhancing the robustness of large-scale multiobjective optimization in construction

Many construction planning problems require optimizing multiple and conflicting project objectives such as minimizing construction time and cost while maximizing safety, quality, and sustainability. To enable the optimization of these construction problems, a number of research studies focused on developing multiobjective optimization algorithms (MOAs). The robustness of these algorithms needs further research to ensure an efficient and effective optimization of large-scale real-life construction problems. This paper presents a review of current research efforts in the field of construction multiobjective optimization and two case studies that illustrate methods for enhancing the robustness of MOAs. The first case study utilizes a multiobjective genetic algorithm (MOGA) and an analytical optimization algorithm to optimize the planning of postdisaster temporary housing projects. The second case study utilizes a MOGA and parallel computing to optimize the planning of construction resource utilization in large-scale infrastructure projects. The paper also presents practical recommendations based on the main findings of the analyzed case studies to enhance the robustness of multiobjective optimization in construction engineering and management.

Maximizing the Sustainability of Integrated Housing Recovery Efforts

The large-scale and catastrophic impacts of Hurricanes Katrina and Rita in 2005 challenged the efficacy of traditional postdisaster temporary housing methods. To address these challenges, the U.S. Congress appropriated

Multi-Objective Optimization of Temporary Housing for the 1994 Northridge Earthquake

400 million to the Department of Homeland Security to support alternative housing pilot programs, which encourage innovative housing solutions that will facilitate sustainable and permanent affordable housing in addition to serving as temporary housing. Facilitating and maximizing the sustainability of postdisaster alternative housing is an important objective that has significant social, economic, and environmental impacts. This paper presents the development of a novel optimization model that is capable of 1 evaluating the sustainability of integrated housing recovery efforts under the alternative housing pilot program and 2 identifying the housing projects that maximize sustainability. An application example is analyzed to demonstrate the use of the developed model and its unique capabilities in maximizing the sustainability of integrated housing recovery efforts after natural disasters.

Minimization of socioeconomic disruption for displaced populations following disasters

Earthquakes and tsunamis often cause significant damages in civil infrastructure systems and housing units, leading to large-scale displacement of families. These displaced families need temporary housing arrangements, such as travel trailers and leased hotels, until permanent housing solutions are available. This article presents a multi-objective optimization model to support decision-makers in identifying optimal temporary housing arrangements for displaced families after natural disasters. The model is developed using weighted linear programming and is capable of optimizing four possibly conflicting objectives, namely: (1) minimizing negative socioeconomic impacts on displaced families; (2) maximizing safety of displaced families; (3) minimizing adverse environmental impacts; and (4) minimizing total public expenditures. The model was used to analyze and optimize a case study of temporary housing arrangements after the 1994 Northridge Earthquake. The analysis of this case study illustrates the potential use of the model by emergency management agencies and demonstrates its capabilities in optimizing temporary housing arrangements after earthquakes and other natural disasters.

Civil engineering grand challenges: Opportunities for data sensing, information analysis, and knowledge discovery

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.

Maximizing Temporary Housing Safety after Natural Disasters

AbstractThis paper presents an exploratory analysis to identify civil engineering challenges that can be addressed with further data sensing and analysis (DSA) research. An initial literature review was followed by a web-based survey to solicit expert opinions in each civil engineering subdiscipline to select challenges that can be addressed by civil engineering DSA research. A total of 10 challenges were identified and evidence of economic, environmental, and societal impacts of these challenges is presented through a review of the literature. The challenges presented in this paper are high building energy consumption, crude estimation of sea level, increased soil and coastal erosion, inadequate water quality, untapped and depleting groundwater, increasing traffic congestion, poor infrastructure resilience to disasters, poor and degrading infrastructure, need for better mining and coal ash waste disposal, and low construction site safety. The paper aims to assist the civil engineering research community ...

Computing a Displacement Distance Equivalent to Optimize Plans for Postdisaster Temporary Housing Projects

In the aftermath of large-scale natural disasters, emergency management organizations are expected to provide safe temporary housing for a large number of displaced families and to ensure that these housing arrangements are not located in hazardous areas. Potential postdisaster hazards can take many forms such as earthquake aftershocks, landslides, postearthquake soil liquefaction, flooding, hazardous material releases, etc. This paper presents the development of a multiobjective optimization methodology to support decision- makers in emergency management organizations in optimizing postdisaster temporary housing arrangements. The developed methodology incorporates 1 a safety model to measure and quantify temporary housing safety in the presence of multiple potential postdisaster hazards; 2 a cost model to minimize total public expenditures on temporary housing; and 3 a multiobjective optimization model to simultaneously maximize temporary housing safety and minimize public expenditures on temporary housing. An application to a large region is presented to illustrate the use of the models and demonstrate their capabilities in optimizing postdisaster temporary housing arrangements. DOI: 10.1061/ASCEIS.1943-555X.0000018 CE Database subject headings: Optimization; Housing; Natural disasters; Safety; Temporary structures. Author keywords: Optimization; Temporary housing; Postdisaster hazards; Housing safety; Postdisaster recovery.

Maximizing the Computational Efficiency of Temporary Housing Decision Support Following Disasters

Residence in temporary housing is a critical period for the social, economic, and psychological recovery of displaced families following disasters. Temporary housing locations define the displacement distance between families and their essential needs. The objective of this paper is to develop a novel methodology to capture the specific proximity needs and preferences of displaced families. This paper proposes a displacement distance equivalent as an objective metric to evaluate the performance of temporary housing locations in meeting the needs of displaced families. Moreover, the paper describes the development of an integer programming optimization model capable of optimizing temporary housing assignments to minimize total displacement distance equivalent while meeting budget constraints. The main contribution of this paper to the body of knowledge is in transforming the purpose of temporary housing programs from offering general accommodation to providing customized housing solutions tailored to the individual proximity needs of each household using the proposed displacement metric. In addition, the proposed optimization model enables decision makers to set budget constraints to ensure the economic feasibility of identified temporary housing solutions.

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

AbstractPostdisaster temporary housing has long been a challenging problem because of its interlinked socioeconomic, political, and financial dimensions. A significant need for automated decision support was obvious to address this problem. Previous research achieved considerable advancements in developing optimization models that can quantify and optimize the impacts of temporary housing decisions on the socioeconomic welfare of displaced families and total public expenditures on temporary housing as well as other objectives. However, the computational complexity of these models hindered its practical use and adoption by emergency planners. This article analyzes the computational efficiency of the current implementation of the most advanced socioeconomic formulation of the temporary housing problem, which uses integer programming. Moreover, it presents the development of a customized variant of the Hungarian algorithm that has a superior computational performance while maintaining the highest quality of ...

Advancing Optimization of Hybrid Housing Development Plans Following Disasters: Achieving Computational Robustness, Effectiveness, and Efficiency

The widespread destruction that follows large-scale natural disasters, such as Hurricane Katrina in August 2005, challenges the efficacy of traditional temporary housing methods in providing adequate solutions to housing needs. Recognising these housing challenges, the Congress of the United States allocated, in 2006, USD 400 million to the Department of Homeland Security to support Alternative Housing Pilot Programs, which are intended to explore the possibilities of providing permanent and affordable housing to displaced families instead of traditional temporary housing. This paper presents a new methodology and optimisation model to identify the optimal configurations of post-shelter housing arrangements to maximise the overall net socioeconomic benefit. The model is capable of quantifying and optimising the impacts of substituting temporary housing with alternative housing on the social and economic welfare of displaced families as well as the required additional costs of doing so. An application example is presented to illustrate the use of the model and its capabilities.