Predisaster Preparation of Transportation Networks
Authors: Hermann Schichl, Meinolf Sellmann
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show several orders of magnitude improvements over existing approaches, allowing us to close an existing real-world benchmark and to solve to optimality other, more challenging benchmarks. |
| Researcher Affiliation | Collaboration | Hermann Schichl Faculty of Mathematics University of Vienna, Austria Meinolf Sellmann IBM Research Yorktown Heights, NY, U.S.A. |
| Pseudocode | No | The paper describes algorithms in text and provides an illustration in Figure 1, but does not include a formal pseudocode block or an algorithm labeled as such. |
| Open Source Code | No | The paper states: 'The code was compiled with g++ 4.8.3 and full optimization. No external software was used.' However, it does not provide an explicit statement about releasing their source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | We test this algorithm on the Istanbul benchmark introduced in (Peeta et al. 2010):... These values were determined by structural engineers using domain-speciļ¬c information, as summarized in the 2003 Master Earthquake Plan of the Istanbul Municipality. ...Based on the Highway Evacuation Plan of the Federal Highway Administration (FHWA) we consider the emergency mass transportation highway evacuation network for the California bay area (V asconez and Kehrli 2010)). |
| Dataset Splits | No | The paper discusses benchmark instances and scenarios but does not explicitly provide training/validation/test dataset splits needed for reproduction. |
| Hardware Specification | Yes | All experiments were conducted on 1.6 GHz Intel Core i7 Q170 machines with a 4-core CPU, 6 MB cache, and 12 GB RAM, running 64-bit Fedora 20. |
| Software Dependencies | Yes | The code was compiled with g++ 4.8.3 and full optimization. |
| Experiment Setup | No | The paper describes the general approach to finding the optimal investment ('plain branch-and-bound,' 'constraint filtering,' 'dual bound,' 'branching heuristic') but does not list specific hyperparameter values or detailed system-level training settings like learning rates or batch sizes typically found in experimental setups. |