El-Anwar, Omar; Chen, Lei. (2014). Maximizing the Computational Efficiency of Temporary Housing Decision Support Following Disasters. Journal Of Computing In Civil Engineering, 28(1), 113 – 123.
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Abstract
Postdisaster 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 solutions. An application example is presented to demonstrate the unique capabilities of the new algorithm in solving large-scale problems.
Keywords
Decision Support Systems; Emergency Management; Integer Programming; Computational Efficiency; Temporary Housing Decision Support Following Disasters; Financial Dimensions; Political Dimensions; Socioeconomic Dimensions; Socioeconomic Welfare; Emergency Planners; Socioeconomic Formulation; Hungarian Algorithm; Multiobjective Optimization; Maeviz-hazturk; Housing; Computation; Disasters; Temporary Structures; Temporary Housing; Optimization; Disaster Management
Gatti, Umberto C.; El-anwar, Omar; Migliaccio, Giovanni C.; Lin, Ken-yu; Medina, Yvonne. (2014). Quantifying The Impacts Of Failures Of Departments Of Transportation Building Systems On Road System Users. Transportation Research Record, 2440, 85 – 93.
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Abstract
Because of the financial crisis of 2007 to 2008 and the subsequent economic downturn, funding for transportation agencies has been consistently reduced. This lack of funds prevents the building assets of transportation agencies from being efficiently maintained, so failures may occur that discontinue employees' operations and activities and affect transportation system users. Thus, to maximize the use of available funding, it is compelling to create innovative tools and techniques capable of estimating how potential failures can affect employees' activities and, eventually, transportation system users. Facility managers and decision makers could use such estimates to make decisions on maintenance of building assets that would minimize the risks of disruptions to employees and transportation system users. Among the capital assets of the Washington State Department of Transportation (DOT), transportation equipment fund (TEF) shops are crucial in ensuring timely and effective care and maintenance of the majority of state vehicles and equipment Therefore, any disruption of the operations of TEF shop facilities could significantly affect not only the Washington State DOT's vehicles and equipment maintenance but also the department's ability to fulfill its core mission. Given the importance of TEF shops, this exploratory case study investigates the failures that have occurred or are likely to occur in these facilities and employs discrete-event simulation to quantify the consequences of such failures on the shop activities and road users.
Keywords
Simulation
El-Anwar, Omar; Chen, Lei. (2016). Automated Community-Based Housing Response: Offering Temporary Housing Solutions Tailored to Displaced Populations Needs. Journal Of Computing In Civil Engineering, 30(6).
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Abstract
Following disasters, emergency management agencies are under immense pressure to make quick decisions regarding the provision of temporary housing, including their locations and types. Such decisions can significantly impact the socioeconomic recovery of displaced families and available budgets for other postdisaster activities. To address these challenges, a new holistic temporary housing planning framework is proposed to offer customized housing plans tailored to the specific social, economic, and psychological needs of displaced families while controlling expenditures. This paper presents the theoretical formulation and implementation details of the community-based housing response pool, which is a comprehensive framework that aims at (1)quantifying the specific needs and preferences of each displaced family, (2)evaluating the ability of housing alternatives to meet those needs, (3)computing temporary housing life cycle costs, and (4)optimizing housing decisions accordingly. The paper also presents an application example to demonstrate and evaluate the optimization model capabilities.
Keywords
Decision Making; Disasters; Emergency Management; Life Cycle Costing; Optimisation; Socio-economic Effects; Town And Country Planning; Automated Community-based Housing Response; Temporary Housing Solutions; Displaced Population Needs; Emergency Management Agencies; Temporary Housing Provision; Housing Locations; Housing Types; Socioeconomic Recovery; Displaced Families; Postdisaster Activity Budgets; Holistic Temporary Housing Planning Framework; Customized Housing Plans Tailored; Expenditure Control; Community-based Housing Response Pool; Housing Alternatives Ability Evaluation; Temporary Housing Life Cycle Cost Computing; Housing Decisions Optimization; Optimization Model Capabilities; Multiobjective Optimization; Maeviz-hazturk; Earthquake
El-Anwar, Omar; Ye, Jin; Orabi, Wallied. (2016). Efficient Optimization of Post-Disaster Reconstruction of Transportation Networks. Journal Of Computing In Civil Engineering, 30(3).
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Abstract
Catastrophes, such as hurricanes, earthquakes, and tsunamis often cause large-scale damage to transportation systems. In the aftermath of these disasters, there is a present challenge to quickly analyze various reconstruction plans and assess their impacts on restoring transportation services. This paper presents a new methodology for optimizing post-disaster reconstruction plans for transportation networks with superior computational efficiency employing mixed-integer linear programming (MILP). The model is capable of optimizing transportation recovery projects prioritization and contractors assignment in order to simultaneously: (1)accelerate networks recovery; and (2)minimize public expenditures. The full methodology is presented in two companion publications, where the focus of this paper is to propose new methods for (1)decomposing traffic analysis; (2)assessing the traffic and cost performance of reconstruction plans; (3)reducing the massive solution search space; and (4)phasing the use of mixed-integer linear programming to optimize the problem. An illustrative example is presented throughout the paper to demonstrate the implementation phases. (C) 2015 American Society of Civil Engineers.
Keywords
Cost Reduction; Disasters; Emergency Management; Integer Programming; Linear Programming; Project Management; Public Finance; Search Problems; Town And Country Planning; Transportation; Solution Search Space Reduction; Cost Performance Assessment; Traffic Performance Assessment; Traffic Analysis; Public Expenditure Minimization; Network Recovery Acceleration; Contractor Assignment; Transportation Recovery Project Prioritization; Milp; Mixed-integer Linear Programming; Post-disaster Reconstruction Plan Optimization; Transportation Service Restoration; Reconstruction Plans; Transportation System Large-scale Damage; Tsunami; Earthquake; Hurricane; Catastrophe; Transportation Network; Post-disaster Reconstruction Optimization; Optimizing Resource Utilization; Natural Disasters; Housing Projects; Construction; Performance; Robustness; Recovery; Plans; Transportation Network Reconstruction; Post-disaster Recovery; Multi-objective Optimization; Computational Cost; Contractors Assignment; Search Space