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Record W4285250623 · doi:10.5829/ije.2022.35.08b.14

A Constraint Programming Approach to Solve Multi-skill Resource-constrained Project Scheduling Problem with Calendars

2022· article· en· W4285250623 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

VenueInternational Journal of Engineering · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicResource-Constrained Project Scheduling
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsConstraint programmingComputer scienceMathematical optimizationScheduling (production processes)Constraint (computer-aided design)Resource constraintsJob shop schedulingTime constraintConstraint satisfactionStochastic programmingArtificial intelligenceMathematicsDistributed computingSchedule

Abstract

fetched live from OpenAlex

The multi-skill resource constrained project scheduling is an important and challenging problem in project management. Two key issues that turn this topic into a challenging problem are the assumptions that are considered to approximate the model to a real-world problem and exact solution approach for the model. In this paper, we deal with this two issues. To consider real-word situations, we take into account calendars specifying time intervals during which the resources are available. We proposed a constraint programming approach to solve the problem exactly. The problem with and without resource calendars are modeled with mathematical programming (MP) and constraint programming (CP). In addition, the performance of CP approach is evaluated by comparing Time-Indexed Model (TIM) and Branch and Price (B&P) approaches. Computational results show that the proposed approach can efficiently solve real-size instances.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.311
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.000
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 head0.060
GPT teacher head0.336
Teacher spread0.277 · 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