MétaCan
Menu
Back to cohort

Simulation-Based Approach for Estimating Project Completion Time of Stochastic Resource–Constrained Project Networks

2012· article· en· W2081143485 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Computing in Civil Engineering · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicResource-Constrained Project Scheduling
Canadian institutionsUniversity of AlbertaNatural Sciences and Engineering Research Council of CanadaCanadian Natural Resources
Fundersnot available
KeywordsCritical path methodDuration (music)Probabilistic logicEvent (particle physics)Path (computing)Computer scienceDiscrete event simulationResource (disambiguation)Project managementOperations researchMathematical optimizationIndustrial engineeringSimulationEngineeringSystems engineeringMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper proposes a new approach, entitled discrete event simulation with probabilistic event list (DESPEL), for approximating the project completion time and the critical path in a stochastic resource–constrained project network. Unlike the traditional discrete event simulation approach, which is based on replications to find the project completion time, this approach only requires one simulation run. This approach is similar to the program evaluation and review technique (PERT) approach in the sense that it considers the path with the highest expected activity duration as the critical path. However, it extends the capability of the PERT by considering the resource constraints when finding the critical activities and project completion time.

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.

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)0.011
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.566
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.071
GPT teacher head0.348
Teacher spread0.278 · 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