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Food banks operate with an objective to serve as many of food-insecure people as possible with the limited supply available to them. This paper presents a mixed-integer programming model to identify the efficient assignment of demand zones (counties) to distribution centers (branches) and equitable allocation of donated food from the food bank branches to the demand zones. The model objective function minimizes the total cost of branch operation, the cost of receiving and distributing food, the cost of undistributed food while maintaining the maximum allowed deviation from perfect equity. Data from the Food Bank of Central and Eastern North Carolina (FBCENC) are used to characterize the major attributes controlling the food distribution system of a food bank. Results from the optimization model using FBCENC data show that the optimal allocation under perfect equity follows a particular structure depending on the shipping cost and the cost of undistributed supply. Sensitivity analyses exploring the trade-offs between efficiency and effectiveness as a function of the cost of shipping, truck capacity, and a user-specified maximum inequity cap show that marginal sacrifice in equity can significantly improve effectiveness. The corresponding improvement in effectiveness is greater when comparatively larger trucks are used and the cost of shipping is relatively higher. The analyses also suggest that while efficiency is less sensitive to the allowable limit on the deviation from perfect equity, it is sensitive to truck size. A comparison of direct shipping to branches to operating a local hub suggests the former option to be more cost efficient.
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This content will become publicly available on June 1, 2026
Multi‐hazard probabilistic risk assessment and equitable multi‐objective optimization of building retrofit strategies in hurricane‐vulnerable communities
Abstract Coastal communities are increasingly vulnerable to hurricanes, which cause billions of dollars in damage annually through wind, storm surge, and flooding. Mitigation efforts are essential to reduce these impacts but face significant challenges, including uncertainties in hazard prediction, damage estimation, and recovery costs. Resource constraints and the disproportionate burden borne by socioeconomically vulnerable groups further complicate retrofitting strategies. This study presents a probabilistic methodology to assess and mitigate hurricane risks by integrating hazard analysis, building fragility, and economic loss assessment. The methodology prioritizes retrofitting strategies using a risk‐informed, equity‐focused approach. Multi‐objective optimization balances cost‐effectiveness and risk reduction while promoting fair resource allocation among socioeconomic groups. The novelty of this study lies in its direct integration of equity as an objective in resource allocation through multi‐objective optimization, its comprehensive consideration of multi‐hazard risks, its inclusion of both direct and indirect losses in cost assessments, and its use of probabilistic hazard analysis to incorporate varying time horizons. A case study of the Galveston testbed demonstrates the methodology's potential to minimize damage and foster equitable resilience. Analysis of budget scenarios and trade‐offs between cost and equity underscores the importance of comprehensive loss assessments and equity considerations in mitigation and resilience planning. Key findings highlight the varied effectiveness of retrofitting strategies across different budgets and time horizons, the necessity of addressing both direct and indirect losses, and the importance of multi‐hazard considerations for accurate risk assessments. Multi‐objective optimization underscores that equitable solutions are achievable even under constrained budgets. Beyond a certain point, achieving equity does not necessarily increase expected losses, demonstrating that more equitable solutions can be implemented without compromising overall cost‐effectiveness.
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- Award ID(s):
- 2052930
- PAR ID:
- 10607999
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Computer-Aided Civil and Infrastructure Engineering
- Volume:
- 40
- Issue:
- 15
- ISSN:
- 1093-9687
- Page Range / eLocation ID:
- 2074 to 2097
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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