Robust Optimization for Tree-Structured Stochastic Network Design
Authors: Xiaojian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, our approach scales well to large river networks. We also provide insights into the solutions generated by our robust approach, which has significantly higher robust ratio than the baseline solution with mean parameter estimates. and 5 Experiments We use data from the CAPS project (Mc Garigal et al. 2011) for the river networks in Massachusetts and synthetically define the missing parameters from the data. |
| Researcher Affiliation | Academia | 1 Department of Computer Science, Cornell University, USA 2 School of Information Systems, Singapore Management University, Singapore 3 College of Information and Computer Sciences, University of Massachusetts Amherst, USA 4 Department of Computer Science, Mount Holyoke College, USA |
| Pseudocode | Yes | Algorithm 1 Robust Policy Optimization |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the methodology or provide a link to a code repository. |
| Open Datasets | Yes | We use data from the CAPS project (Mc Garigal et al. 2011) for the river networks in Massachusetts and synthetically define the missing parameters from the data. |
| Dataset Splits | No | The paper describes experiments and datasets but does not explicitly state specific training/validation/test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers with their versions). |
| Experiment Setup | Yes | We set Ku in two different ways ϵ = 0.1 (denoted by μ ) and Ku = 5 (denoted by constant ). and Budget sizes are relative to the cost of removing all barriers. and We use the method in (Kumar et al. 2016) to define the intervals of initial passage probabilities before taking actions. The interval of an initial passage probability is [p βp, p + βp] where p is an point estimate and β is a parameter controlling the interval sizes. |