Robust Decision Making for Stochastic Network Design
Authors: Akshat Kumar, Arambam Singh, Pradeep Varakantham, Daniel Sheldon
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that previous approaches that work on point estimates of network parameters result in high regret on several standard benchmarks, while our approach provides significantly more robust solutions. |
| Researcher Affiliation | Academia | School of Information Systems, Singapore Management University {akshatkumar,ajsingh,pradeepv}@smu.edu.sg College of Information and Computer Sciences, University of Massachusetts Amherst tsheldon@cs.umass.edu |
| Pseudocode | No | The paper includes 'Table 1: Constraint set Ω for max-regret mixed-integer program', which lists mathematical constraints, but not a pseudocode block or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We used a publicly available conservation planning benchmark which represents a geographical region on the coast of North Carolina (Ahmadizadeh et al. 2010). |
| Dataset Splits | No | The paper mentions 'N training cascades' for the SAA approximation but does not explicitly provide information on training/validation/test dataset splits, percentages, or sample counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, processor types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'mixed-integer solver' and 'Python', but does not provide specific version numbers for any software components, libraries, or solvers. |
| Experiment Setup | Yes | Parameter Setting We first compute pbase e for each edge e using the equations provided in (Sheldon et al. 2010). Then we add uncertainty ϵ to each parameter to get the range [pbase e ϵ pbase e , pbase e +ϵ pbase e ]. The range for suitability scores is also computed in a similar manner. Edge Activation Regret Analysis For this analysis, we used a time horizon of T = 5, 9, 10, number of SAA samples N = 20, and the budget B = 10%. We set uncertainty level ϵ=30% as it provided pronounced effects of the MMR solution while also being a realistic level of noise. |