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Record W6891677303 · doi:10.4230/lipics.cp.2024.7

Cumulative Scheduling with Calendars and Overtime

2024· article· en· W6891677303 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

VenueDROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicResource-Constrained Project Scheduling
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsOvertimeConstraint (computer-aided design)Scheduling (production processes)Task (project management)Duration (music)Resource constraintsConstraint programmingTime constraintDecomposition

Abstract

fetched live from OpenAlex

In project scheduling, calendar considerations can increase the duration of a task when its execution overlaps with holidays. On the other hand, the use of overtime may decrease the task’s duration. We introduce the CalendarOvertime constraint which verifies that a task follows a calendar with overtime and holidays. We also introduce the CumulativeOvertime constraint, a variant of the Cumulative constraint, that also reasons with the calendars when propagating according to the resource consumption, the overtime, and the holidays. Experimental results of a RCPSP model on the PSPLIB, BL, and PACK instances augmented with calendars and overtime show that the use of the CalendarOvertime constraint offers a speedup greater than 2.9 on the instances optimally solved and finds better solutions on more than 79% of the remaining instances when compared to a decomposition of the constraint. We also show that the use of our CumulativeOvertime constraint further improves these results.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
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 head0.061
GPT teacher head0.368
Teacher spread0.307 · 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