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ARXIV:2605.04510 · OPTIMIZATION AI · SUBMITTED 07 MAY · 20:32 UTC · FRESHNESS STALE
ARXIV:2605.04510OPTIMIZATION AISUBMITTED 07 MAY · 20:32 UTCFRESHNESS STALELeonard Boussioux · Alexandre Jacquillat · Ryne Reger · Jacob Wachspress · arXiv
A predictive and prescriptive AI approach to jointly optimize wildfire suppression resource allocation and crew assignments using integer optimization and double machine learning.
Opportunity summary
Pain A predictive and prescriptive AI approach to jointly optimize wildfire suppression resource allocation and crew assignments using integer optimization and double machine learning.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A predictive and prescriptive AI approach to jointly optimize wildfire suppression resource allocation and crew assignments using integer optimization and double machine learning. This paper develops a predictive and prescriptive approach to jointly optimize…
Intense wildfire seasons require critical prioritization decisions to allocate scarce suppression resources over a dispersed geographical area. This paper develops a predictive and prescriptive approach to jointly optimize crew assignments and wildfire suppression.
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Extensive computational experiments show that the optimization algorithm scales to otherwise intractable real-world instances; and that the methodology can enhance suppression effectiveness in practice,…
Optimization AI moved forward this cycle; last verified May 2026. Public score 4.0/10.
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A predictive and prescriptive AI approach to jointly optimize wildfire suppression resource allocation and crew assignments using integer optimization and double machine learning.
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10.48550/arXiv.2605.04510A predictive and prescriptive AI approach to jointly optimize wildfire suppression resource allocation and crew assignments using integer optimization and double machine learning.
Abstract
Intense wildfire seasons require critical prioritization decisions to allocate scarce suppression resources over a dispersed geographical area. This paper develops a predictive and prescriptive approach to jointly optimize crew assignments and wildfire suppression. The problem features a discrete resource-allocation structure with endogenous wildfire demand and non-linear wildfire dynamics. We formulate an integer optimization model with crew assignments on a time-space-rest network, wildfire dynamics on a time-state network, and linking constraints between them. We develop a two-sided branch-and-price-and-cut algorithm based on: (i) a two-sided column generation scheme that generates fire suppression plans and crew routes iteratively; (ii) a new family of cuts exploiting the knapsack structure of the linking constraints; and (iii) novel branching rules to accommodate non-linear wildfire dynamics. We also propose a data-driven double machine learning approach to estimate wildfire spread as a function of covariate information and suppression efforts, mitigating observed confounding between historical crew assignments and wildfire growth. Extensive computational experiments show that the optimization algorithm scales to otherwise intractable real-world instances; and that the methodology can enhance suppression effectiveness in practice, resulting in significant reductions in area burned over a wildfire season and guiding resource sharing across wildfire jurisdictions.
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Dimensions overall score 4.0
PROBLEM
A predictive and prescriptive AI approach to jointly optimize wildfire suppression resource allocation and crew assignments using integer optimization and double machine learning. This paper develops a predictive and prescriptive approach to jointly optimize crew assignments and...
METHOD
Intense wildfire seasons require critical prioritization decisions to allocate scarce suppression resources over a dispersed geographical area. This paper develops a predictive and prescriptive approach to jointly optimize crew assignments and wildfire suppression.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Extensive computational experiments show that the optimization algorithm scales to otherwise intractable real-world instances; and that the methodology can enhance suppression effectiveness in practice, r...
WHY NOW
Optimization AI moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A predictive and prescriptive AI approach to jointly optimize wildfire suppression resource allocation and crew assignments using integer optimization and double machine learning. This paper develops a predictive and prescriptive approach to jointly optimize crew assignments and wildfire suppression.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Intense wildfire seasons require critical prioritization decisions to allocate scarce suppression resources over a dispersed geographical area. This paper develops a predictive and prescriptive approach to jointly optimize crew assignments and wildfire suppression.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Extensive computational experiments show that the optimization algorithm scales to otherwise intractable real-world instances; and that the methodology can enhance suppression effectiveness in practice, resulting in significant reductions in area burned over a wildfire season and guiding resource sharing across wildfire jurisdictions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Optimization AI moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A predictive and prescriptive AI approach to jointly optimize wildfire suppression resource allocation and crew assignments using integer optimization and double machine learning.
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Optimization AI
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Commercial read
4.0/10 public viability
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reason
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proof status
unverified
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Build readiness
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passport absent
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stale
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Technical feasibility
partial
Current read
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missing
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ARTIFACTS
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DEFENSIBILITY
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