Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Bin Packing with Predictions
Authors: Spyros Angelopoulos, Shahin Kamali, Kimia Shadkami
IJCAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,389 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,829 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive experiments on our algorithms. Specifically, we evaluate them on a variety of publicly available benchmarks, such as the BPPLIB benchmarks [Delorme et al., 2018], but also on distributions studied in the context of offline bin packing, such as the Weibull distribution [Casti neiras et al., 2012]. The results show that our algorithms outperform the known, and efficient algorithms without any predictions that are typically used in practice. |
| Researcher Affiliation | Academia | 1Centre National de la Recherche Scientifique (CNRS) 2LIP6, Sorbonne Universit e, Paris, France 3University of Manitoba, Winnipeg, Manitoba, Canada |
| Pseudocode | No | The paper describes the algorithms PROFILEPACKING and HYBRID(λ) in natural language, but it does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that the source code for the methodology is released or provide a link to a code repository. An arXiv link is provided in the references, but it's for the paper itself, not source code. |
| Open Datasets | Yes | We evaluate our algorithms on a variety of publicly available benchmarks, such as the BPPLIB benchmarks [Delorme et al., 2018], but also on distributions studied in the context of offline bin packing, such as the Weibull distribution [Casti neiras et al., 2012]. |
| Dataset Splits | No | The paper describes how input sequences are generated and how predictions are derived (using a prefix), but it does not specify explicit train/validation/test dataset splits (e.g., percentages or sample counts) for the experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper mentions algorithms used (e.g., FIRSTFITDECREASING) but does not list any software dependencies with specific version numbers (e.g., Python 3.x, PyTorch 1.x, specific solver versions). |
| Experiment Setup | Yes | We fix the size of the sequence to n = 10^6. We set the bin capacity to k = 100, and we also scale down each item to the closest integer in [1, k]. [...] In our experiments, we chose sh [1.0, 4.0]. [...] we thus set sc = 1000, as in [Casti neiras et al., 2012]. We fix the size of the profile set to m = 5000. |