Chance-Constrained Scheduling via Conflict-Directed Risk Allocation
Authors: Andrew Wang, Brian Williams
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through a set of simulated car-sharing scenarios, it is empirically shown that conflict-directed risk allocation computes solutions nearly an order of magnitude faster than prior art does, which considers all constraints in a single lump-sum optimization.This work is evaluated against two previous probabilistic approaches: the risk-minimization method by (Tsamardinos 2002), and the single-optimization chance-constrained method by (Fang, Yu, and Williams 2014). Benchmarks are run on each method over a range of problem sizes, and their runtime performances are compared. |
| Researcher Affiliation | Academia | Andrew J. Wang and Brian C. Williams Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology {wangaj, williams}@mit.edu |
| Pseudocode | Yes | Algorithm 1: Rubato |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that its source code is open or publicly available. |
| Open Datasets | No | The authors of (Fang, Yu, and Williams 2014) kindly made available their collection of car-sharing scenarios as a common benchmark set.While the scenarios were provided to the authors, there is no concrete access information (link, DOI, formal citation for public access) indicating the dataset is publicly available to all. |
| Dataset Splits | No | The paper does not provide specific details on train/validation/test dataset splits, only stating that benchmarks were run over a range of problem sizes. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The algorithms are implemented in Common Lisp and linked to the Ipopt nonlinear optimizer (W achter and Biegler 2006), which is written in C++.While Ipopt is mentioned, a specific version number is not provided, which is necessary for reproducible software dependencies. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values, model initialization, or training schedules. |