Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Representative Solutions for Bi-Objective Optimisation
Authors: Emir Demirovi?, Nicolas Schwind1436-1443
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented our algorithms and performed a numerical study. The goal was to verify the theoretical result and evaluate the empirical performance. |
| Researcher Affiliation | Academia | Emir Demirovi c School of Computing and Information Systems University of Melbourne Melbourne, Australia EMAIL Nicolas Schwind National Institute of Advanced Industrial Science and Technology Tokyo, Japan EMAIL |
| Pseudocode | Yes | Algorithm 1: RS-Rp PF, Rq |
| Open Source Code | Yes | Our code and benchmarks are available online: bitbucket.org/Emir D/representative-solutions-for-biobjective-optimisation. |
| Open Datasets | Yes | Resource-constrained project scheduling problems with weighted earliness and tardiness objectives, labelled as RCPSP-wet in the Mini Zinc Challenge 2016 and 2017. Generated large bi-objective set covering benchmarks. Similar instances were used in other singleand multiobjective works (Musliu 2006; Bergman and Cire 2016). |
| Dataset Splits | No | The paper uses benchmarks and instances (RCPSP-wet, set covering benchmarks) but does not specify any training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation details). |
| Hardware Specification | Yes | Experiments were performed on a machine with an i77700HQ CPU @ 2.80GHz processor and 32 GB of RAM, running one instance at a time with a time limit of ten hours. |
| Software Dependencies | No | The paper mentions using 'Gurobi as the optimisation solver' and 'Mini Zinc', but it does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Experiments were performed on a machine with an i77700HQ CPU @ 2.80GHz processor and 32 GB of RAM, running one instance at a time with a time limit of ten hours. We consider values r1, 2, 3, 4, 5s for k, the number of desired representative solutions. |