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Math programming to facilitate exploration of decision alternatives for community resilience planning
Published
Author(s)
Kenneth Harrison
Abstract
In the development of models to support community resilience planning, the scale and interdependencies of the system and the complexities of the planning process must be acknowledged and addressed. This work focuses on the development of math programming models that are to be the basis of an interactive screening tool for community-scale resilience planning. The goal of the tool is for stakeholders to interactively explore solutions that perform well with respect to modeled (e.g., cost, resilience) and un-modeled objectives (e.g., social and political feasibility). It is characterized as a "screening" tool as significant model simplifications are made to permit quick solution (via a class of optimization algorithms); interesting solutions found would undergo more detailed analysis. To address unmodeled objectives, multi-objective programming and modeling-to-generate-alternatives are applied to explore tradeoffs and efficiently search for maximally different alternatives, respectively. "Community-scale" is meant to imply that a broad range of decisions across the community are to be considered in a joint manner and with recognition of their dependencies (e.g., home retrofits lessening the need for emergency response). Challenges in the design of the tool include the need to tie together pre-disaster (i.e., preparedness) and post-disaster (i.e., recovery) decisions and to account for the stochastic nature of the hazards and system failure.
Proceedings Title
International Workshop on Modelling of Physical, Economic, and Social Systems for Resilience
Assessment, Ispra, Italy, 12/14/2017 to 12/16/2017
Harrison, K.
(2019),
Math programming to facilitate exploration of decision alternatives for community resilience planning, International Workshop on Modelling of Physical, Economic, and Social Systems for Resilience
Assessment, Ispra, Italy, 12/14/2017 to 12/16/2017, Ispra, IT, [online], https://doi.org/10.2760/727592
(Accessed December 14, 2024)