Ranking Constraints

Authors: Christian Bessiere, Emmanuel Hebrard, George Katsirelos, Zeynep Kiziltan, Toby Walsh

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

Reproducibility Variable Result LLM Response
Research Type Experimental report experimental results demonstrating the promise of our proposed approach. 4 Experimental Evaluation
Researcher Affiliation Academia Christian Bessiere LIRMM-CNRS Montpellier, France; Emmanuel Hebrard LAAS-CNRS Toulouse, France; George Katsirelos INRA Toulouse, France; Zeynep Kiziltan Universit di Bologna Bologna, Italy; Toby Walsh University of New South Wales Sydney, Australia
Pseudocode Yes Algorithm 1: DCSupport(X1, . . . , Xn); Algorithm 2: RCSupport(X1, . . . , Xn)
Open Source Code No No statement regarding open-source code availability or links to code repositories was found.
Open Datasets No No concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset was found. The paper describes generating its own data instances.
Dataset Splits No The paper describes generating instances and solving them, but does not provide specific training/validation/test dataset split information.
Hardware Specification Yes we ran all the experiments on a cluster of AMD opteron 6176 2.3 GHz processors.
Software Dependencies Yes We used Choco 3 to implement the two propagators
Experiment Setup Yes We used lexicographic variable ordering and branched on the minimum value in all cases, so the same search tree is explored, modulo pruning. For a number of tasks ranging from n = 5 to 10, we generate 50 such scheduling instances, choosing duration constants pi and demand at random. Then we post a CUMULATIVE constraint, as well as the channeling constraints between overlap and ranking and between ranking and actual durations ripi.