Optimizing Resilience in Large Scale Networks
Authors: Xiaojian Wu, Daniel Sheldon, Shlomo Zilberstein
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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 {xiaojian,sheldon,shlomo}@cs.umass.edu |
| 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. |