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Record W3159012693 · doi:10.1007/s42452-021-04558-9

A budget-constrained partial protection planning of a rail intermodal terminal network

2021· article· en· W3159012693 on OpenAlexaff
Hassan Sarhadi, Satyaveer S. Chauhan, Manish Verma

Bibliographic record

VenueSN Applied Sciences · 2021
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsMcMaster UniversityConcordia UniversityAcadia University
Fundersnot available
KeywordsMetaheuristicComputer scienceTruckOperations researchDecompositionComponent (thermodynamics)Flexibility (engineering)Mathematical optimizationRisk analysis (engineering)EngineeringBusinessArtificial intelligenceEconomicsMathematics

Abstract

fetched live from OpenAlex

Abstract Rail-truck intermodal transportation is an essential component of freight transportation in North America, and thus, its associated infrastructure is deemed crucial for the wellbeing of the society. In this paper, a budget-constrained partial protection planning model is proposed that addresses the fortification of a rail intermodal network such that the effects of an intentional disruption are minimized. Novel solution methodologies that make use of metaheuristic approaches and a decomposition approach are proposed to solve the challenging tri-level mixed-integer mathematical model. The proposed analytical approaches are then used to solve and analyze problem instances generated using the realistic infrastructure of a major railroad operator. Finally, the computational efficiency and effectiveness of the proposed approaches over the existing solution techniques in the literature are discussed and future research directions are outlined. Article Highlights The paper suggests partial protection of facilities together with two metaheuristic approaches to solve the model. The proposed metaheuristic approaches are efficient in solving the model compared to the existing exact approach. Having the option to partially protect facilities provides effective use of scarce defensive resources.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.241
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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