| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | April 28, 2021 |
| Latest Amendment Date: | April 28, 2021 |
| Award Number: | 2114102 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Daan Liang
dliang@nsf.gov (703)292-2441 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | June 1, 2021 |
| End Date: | May 31, 2025 (Estimated) |
| Total Intended Award Amount: | $315,580.00 |
| Total Awarded Amount to Date: | $315,580.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
6425 BOAZ ST RM 130 DALLAS TX US 75205-1902 (214)768-4708 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
6425 BOAZ Dallas TX US 75275-0302 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CIS-Civil Infrastructure Syst |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Effective response to foreseen natural disasters requires careful infrastructure development decisions made in strategic sense and coordination of planning/management efforts in the tactical sense for evacuation and relief supply activities. The complexity of such coordination became obvious in the recent events of Hurricanes Harvey and Irma in 2017 as well as earlier ones, such as Katrina and Rita in 2005, which created significant awareness in the shortcomings in existing response plans. This research is motivated by the recognition of the stakeholders involved and their individual objectives in the course of decision-making for preparedness planning activities upon an imminent hurricane. As commonly witnessed in real situations, conflicting motivations of stakeholders can lead to decisions driven by individual objectives rather than what is best under a system view, and, thus, lead to unintended consequences. One main reason for this occurrence is the current paradigm of emergency logistics that has largely overlooked the important interdependence between evacuation (demand) and relief (supply) sides. This grant will investigate such integration from the perspective of both cost- and operation-effectiveness in emergency preparedness planning. If successful, the outcome of this grant will facilitate future development of sophisticated and easy-to-use decision support tools for emergency logistics planners. It will help to bring services to a wider group of citizens by making it possible to move out of harm?s way efficiently in case of an impending disaster. Undergraduate and graduate students from diverse backgrounds and underrepresented groups will be engaged and trained with state-of-the-art tools and means to conduct socially impactful research.
While significant advances have been made in past decades, comprehensive efforts to understand and leverage the linkage between demand and supply sides within an integrated framework do not exist. Especially in the presence of uncertainty that carries from hurricane characteristics to supply planning through evacuation decision-making and evacuee behavior presents a major challenge in optimization when employing an integrated systems framework. This grant addresses this critical gap in theory and practice. This work will be the first to systematically explore the integration of demand and supply sides in infrastructure design and operations planning for emergency preparedness/response logistics. The overall system will be considered in two layers of decisions within an optimization framework: strategic infrastructure related and tactical/operational decisions by explicitly modeling uncertainties based on potential disaster events and evacuee behavior. Thus, collectively, the components of the research fully capture the stakeholder activities, and rather than planning for their actions independently, as is commonly done in existing literature, this research aims to improve efficiency and cost-effectiveness at the system level. The methodology brings together new perspectives and purpose to investigate new and challenging optimization models under uncertainty and solution algorithms for their optimal solutions and it will also establish a framework to examine the benefits of integration over the existing approaches by addressing optimization of current decision-making in a comparable manner.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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