English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Critical infrastructure systems such as electric power networks, waternetworks, and transportation systems play a major role in the welfare of anycommunity. In the aftermath of disasters, their recovery is of paramountimportance; orderly and efficient recovery involves the assignment of limitedresources (a combination of human repair workers and machines) to repairdamaged infrastructure components. The decision maker must also deal withuncertainty in the outcome of the resource-allocation actions during recovery.The manual assignment of resources seldom is optimal despite the expertise ofthe decision maker because of the large number of choices and uncertainties inconsequences of sequential decisions. This combinatorial assignment problemunder uncertainty is known to be \mbox{NP-hard}. We propose a novel decisiontechnique that addresses the massive number of decision choices for large-scalereal-world problems; in addition, our method also features an experientiallearning component that adaptively determines the utilization of thecomputational resources based on the performance of a small number of choices.Our framework is closed-loop, and naturally incorporates all the attractivefeatures of such a decision-making system. In contrast to myopic approaches,which do not account for the future effects of the current choices, ourmethodology has an anticipatory learning component that effectivelyincorporates \emph{lookahead} into the solutions. To this end, we leverage thetheory of regression analysis, Markov decision processes (MDPs), multi-armedbandits, and stochastic models of community damage from natural disasters todevelop a method for near-optimal recovery of communities. Our methodcontributes to the general problem of MDPs with massive action spaces withapplication to recovery of communities affected by hazards.

Decision Automation for Electric Power Network Recovery