Online Bin Packing with Predictions
Authors: Spyros Angelopoulos, Shahin Kamali, Kimia Shadkami
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |