Learning Sensitivity of RCPSP by Analyzing the Search Process
Authors: Marc-André Ménard, Claude-Guy Quimper, Jonathan Gaudreault
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate our method with the RCPSP problem.5 Experiments We use the RCPSP benchmarks PSPLib [Kolisch and Sprecher, 1997] and Pack [Carlier and N eron, 2003].We compare the accuracy and the f1-score for the classification problem and the mean squared error for the regression problem. |
| Researcher Affiliation | Academia | Marc-Andr e M enard , Claude-Guy Quimper and Jonathan Gaudreault Universit e Laval, Qu ebec, Canada |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology described in this paper is publicly available. |
| Open Datasets | Yes | We use the RCPSP benchmarks PSPLib [Kolisch and Sprecher, 1997] and Pack [Carlier and N eron, 2003]. |
| Dataset Splits | No | We randomly separate the instances of a benchmark into a training and a testing set with ratio 80/20. (No explicit mention of a validation split for reproduction.) |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | We use the model provided by Minizinc [Stuckey et al., 2014]. We use the random forest classifier and the random forest regressor from Scikit-Learn [Pedregosa et al., 2011]. (Specific version numbers for these software components are not provided within the text.) |
| Experiment Setup | Yes | We use the default parameters except for the number of trees (n Estimators) for which we set the value to 100. We apply a min-max normalization on all features to scale them between 0 and 1 using the relation x i = xi min( x) max( x) min( x). We use a timeout of 10 minutes per instance for PSBLib and 3 hours for Pack. |