https://hdl.handle.net/1721.1/123320 Wed, 08 Apr 2026 20:50:26 GMT 2026-04-08T20:50:26Z http://dspace.mit.edu:80/bitstream/id/d2420c61-f59d-4b93-af47-24ee42973057/ https://hdl.handle.net/1721.1/123320 https://hdl.handle.net/1721.1/155788 Scaling Post-Disaster Housing Capacity: Roundtable Report Finegan, Lauren; Goentzel, Jarrod; Reisman, Erin; Russell, Timothy Edward; Story, Drew In January 2024, the MIT Humanitarian Supply Chain Lab held a roundtable on the theme of scaling construction capacity after disasters. The roundtable convened participants from academia, non-profit organizations, and both the public and private sectors. Participants brought varied perspectives to this issue, including considerations of supply chains, local, state, and federal policies, building codes, and private sector construction operations. The roundtable used recent natural disasters and their subsequent housing challenges to frame discussions around two goals: 1) identify approaches to increase capacity for rapidly deployable housing solutions after disasters, and 2) capture policy and operational constraints that hinder implementation of those rapidly deployable housing solutions. The roundtable and this report seek to catalyze systemic research and provide discrete recommendations to address the challenges and opportunities to restore housing for disaster survivors. Thu, 25 Jul 2024 00:00:00 GMT https://hdl.handle.net/1721.1/155788 2024-07-25T00:00:00Z https://hdl.handle.net/1721.1/150581 Stochastic Analysis of Logistics Capacity in Disaster Response Networks Goentzel, Jarrod; Rothkopf, Alexander Quickly deploying relief items is key to reducing a population’s burden in case of sudden onset disasters. Emergency response organizations, such as FEMA or local and state agencies hold a strategic stockpile of critical relief items and contract for contingency stock in preparation for emergencies. Their response capacity depends on their decision to stock items at different depots, contracts with contingency suppliers, and procurement of transportation capacity to move these items. Building on prior work of Acimovic & Goentzel (2016) we develop a stochastic linear programming model to capture carrier capacity and contingency suppliers. Inputs to the model are a risk portfolio reflecting the particular disasters and the inherent uncertainty with respect to when an organization needs to address a large or a small disaster. Further inputs are the organic stockpile of critical relief items, referred to as the inventory portfolio, contracts with contingency suppliers, which we term the supplier portfolio, and the portfolio of carriers at any depot location. The model allows to conduct a system assessment and a system optimization. System assessment evaluates the current state and answers how well the current inventory, supplier, and carrier portfolio is able to meet a given risk portfolio. We present aggregate metrics to assess a system in three dimensions. We evaluate service metrics to answer how well the network meets demand of the affected population and how rapidly we reach the affected population, and efficiency metrics to indicate how much resources are necessary to meet demand. Taken together these metrics allow to evaluate the state of an emergency response network. System optimization identifies the optimal allocation of inventory for a given supplier and carrier portfolio against a given risk portfolio. The models provides the above mentioned metrics for a decision-maker to compare to optimal network to the current on. In addition, we prescribe an inventory balance, a carrier contract, and a carrier utilization metric to capture the value of improvement. In both – system assessment and system optimization – we evaluate a time-based model and a cost-based model to capture the inherent cost-time trade-off. Typically, more responsive suppliers and carriers are more expensive and less responsive suppliers and carriers are less expensive. When choosing where to allocate inventory, and which suppliers and carriers to contract an organization has to resolve this trade-off between cost and time. Our model provides insight into this trade-off and the impact on different performance metrics. We use data from the openFEMA API to construct a new risk portfolio and estimate an inventory and a carrier portfolio to show the feasibility and functionality of our approach. Thu, 01 Nov 2018 00:00:00 GMT https://hdl.handle.net/1721.1/150581 2018-11-01T00:00:00Z https://hdl.handle.net/1721.1/138837 Preparing PPE stockpiles for the next pandemic Finegan, Lauren; McGuigan, Molly From June 2020 - June 2021, members of Massachusetts General Hospital Center for Disaster Medicine and the MIT Humanitarian Supply Chain Lab conducted a year-long research project to support public health planners in creating a state-level emergency stockpile of personal protective equipment (PPE) for healthcare workers. The research revealed opportunities for policymakers and emergency management professionals to improve PPE preparedness for the next pandemic. Wed, 05 Jan 2022 00:00:00 GMT https://hdl.handle.net/1721.1/138837 2022-01-05T00:00:00Z https://hdl.handle.net/1721.1/127779 Optimization-based Evaluation of Global Logistics Capacity Rothkopf, Alexander; Graham, Chelsey Diane; Goentzel, Jarrod Humanitarian organizations, donor countries, and governments pre-position emergency supplies worldwide to facilitate rapid response to crisis needs. These organizations often pre-position stock at various warehouses around the world without formally analyzing how effectively this rapid response capacity can address future humanitarian needs. This may result in surplus stock, positioned too far for effective deployment, sitting idle (or expiring) in some locations and insufficient stock in other locations to provide timely response. USAID/OFDA has such response capacity through pre-positioned stock and could set an example for evidence-based resource allocation to address future humanitarian responses. Optimization-based metrics could assess the effectiveness of its global stock portfolio in addressing a portfolio of anticipated global disaster risks in the future. Such analysis could also be extended to consider contingent capacity from suppliers on contract, and incorporate a portfolio of transportation resources to move items from pre-positioning warehouses to disaster locations. Optimization-based metrics could then inform USAID/OFDA decision-making regarding stockpile deployment, and potentially contract negotiation for suppliers and transportation providers. This evidence base could also foster coordination efforts for humanitarian supply pre-positioning across organizations at the international, regional and national levels. Wed, 30 Sep 2020 00:00:00 GMT https://hdl.handle.net/1721.1/127779 2020-09-30T00:00:00Z