Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Optimizing Resilience in Large Scale Networks
Authors: Xiaojian Wu, Daniel Sheldon, Shlomo Zilberstein
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On a small existing benchmark, our algorithm produces near-optimal solutions and the SAA method converges quickly with a small number of samples. We then apply our algorithm to a large real-world problem to optimize the resilience of a road network to failures of stream crossing structures to minimize travel times of emergency medical service vehicles. |
| Researcher Affiliation | Academia | 1 College of Information and Computer Sciences, University of Massachusetts, Amherst, MA 01003, USA 2 Department of Computer Science, Mount Holyoke College, South Hadley, MA 01075, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Primal-Dual Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | No | The paper mentions using 'Istanbul Earthquake Preparation' (Peeta et al. 2010) as a benchmark and a dataset from 'the Deerfield river watershed in Massachusetts', but does not provide specific links, DOIs, repository names, or explicit statements of public availability for these datasets. |
| Dataset Splits | No | The paper mentions using 'training samples' and 'testing samples' for evaluation but does not explicitly define distinct 'validation' splits for model tuning or hyperparameter selection. |
| Hardware Specification | Yes | We experimented on two different domains, using a 2.2GHz Intel Core i7 CPU with 16GB of RAM. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We used the basic settings described by Peeta et al. (2010), with Mo,d =120.Each crossing has a survival probability pe in the range [0.2 0.4]. An edge, if associated with a crossing, has length le with probability pe and has length with probability 1 pe. pe is raised to 1.0 if the corresponding crossing is fixed. We used a constant investment cost for all crossings. |