Generalizing the Edge-Finder Rule for the Cumulative Constraint

Authors: Vincent Gingras, Claude-Guy Quimper

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experiments show that these algorithms are competitive with the stateof-the-art algorithms, by doing a greater filtering and having a faster runtime.
Researcher Affiliation Academia Vincent Gingras Universit e Laval, Qu ebec, QC, Canada vincent.gingras.5@ulaval.ca Claude-Guy Quimper Universit e Laval, Qu ebec, QC, Canada Claude-Guy.Quimper@ift.ulaval.ca
Pseudocode Yes Algorithm 1: Schedule Tasks( , c); Algorithm 2: Overload Check(I, C); Algorithm 3: Detection(I, C); Algorithm 4: Detect Precedences( , h, h, lct); Algorithm 5: Adjustment(Prec, C); Algorithm 6: Compute Bound(i, , ovmax)
Open Source Code No The paper does not contain any statement about releasing source code or provide any links to a code repository for the methodology described.
Open Datasets Yes We implemented the algorithms for the Choco 2 solver and tried them on the benchmarks BL [Baptiste and Le Pape, 2000] and PSPLib [Kolisch and Sprecher, 1997] of Resource Constrained Project Scheduling Problems (RCPSP).
Dataset Splits No The paper mentions using benchmark suites but does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing.
Hardware Specification Yes All experiments were run on an Intel Xeon X5560 2.667GHz quad-core processor.
Software Dependencies Yes We implemented the algorithms for the Choco 2 solver
Experiment Setup Yes We used three different branching heuristics: a static variable and value ordering, Dom Over WDeg [Boussemart et al., 2004], and Impact Based Search [Refalo, 2004].