Overload-Checking and Edge-Finding for Robust Cumulative Scheduling
Bibliographic record
Abstract
Scheduling frameworks are not necessarily stable. The aim is to introduce schedules resistant to disruptions such as when resources become unavailable, the supply chain for them breaks down, etc. A schedule is robust if it absorbs some level of unforeseen events when at most a certain number of activities are delayed. Taking advantage of constraint programming, we present two new filtering algorithms for a constraint that models cumulative scheduling problems in robust contexts where up to [Formula: see text] out of [Formula: see text] tasks can be concurrently delayed while keeping the schedule valid. We adapt the overload-checking and edge-finding filtering rules for this framework. We show that our robust versions of these algorithms run in [Formula: see text] and [Formula: see text], respectively, where [Formula: see text] denotes the number of distinct capacities of all tasks. This achievement implies that the complexities of the state-of-the-art algorithms for these techniques are invariable when [Formula: see text] is constant. Experiments illustrate that our algorithms scale, with respect to [Formula: see text] and [Formula: see text]. As a practical application, the experimental results on a special case of crane assignment problem also verify a stronger filtering for these methods in terms of backtrack numbers as well as computation times when used in conjunction with time tabling. Finally, in order to show that our CP-based algorithms improve to solve a robust scheduling problem, we make a comparison against temporal protection as an external robust scheduling approach. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms – Discrete. Funding: H. Fahimi received funding from Shahid Chamran University of Ahvaz with [Grant number SCU.MC1402.44132]. C.-G. Quimper was funded by a NSERC Discovery Grant from Canada. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2021.0138 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".