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 [1].
Placement of Loading Stations for Electric Vehicles: No Detours Necessary!
Authors: Stefan Funke, Andre Nusser, Sabine Storandt
JAIR 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposed techniques for computing ESC solutions were evaluated in a multi-threaded implementation written in C++ and executed on 2nd generation Intel Core desktop hardware, an i7-3930 (6 cores, 64GB of RAM) for complete set generations and an i7-2700 (4 cores, 32GB RAM) for the multi-stage construction with nested Hitting Sets. We use the following abbreviations to state results: K=103, M=106, s=seconds, m=minutes, h=hours, d=days, GB=109Bytes. We distinguish between CPU time (total CPU usage) and real time (wall clock time). Several road networks of Germany derived from Open Street Map data (OSM, 2015) were used for evaluation, see Table 1 for an overview. |
| Researcher Affiliation | Academia | Stefan Funke EMAIL André Nusser EMAIL Universität Stuttgart Institut für Formale Methoden der Informatik 70569 Stuttgart, Germany Sabine Storandt EMAIL Albert-Ludwigs-Universität Freiburg Institut für Informatik 79110 Freiburg, Germany |
| Pseudocode | No | The paper describes algorithms like PNM, greedy Hitting Set, and multi-stage construction, but does not present them in structured pseudocode or algorithm blocks. The descriptions are in paragraph form. |
| Open Source Code | No | The paper does not contain any explicit statement about the release of source code or a link to a code repository. |
| Open Datasets | Yes | Several road networks of Germany derived from Open Street Map data (OSM, 2015) were used for evaluation... Energy consumption of an EV was modeled as explained in the introduction using distance data from OSM and elevations provided by the Shuttle Radar Topography Mission (NASA, 2015). |
| Dataset Splits | No | The paper uses road network data and discusses 'set systems' and 'k-Hop Path Covers' as part of the problem formulation and solution, but it does not describe specific training, validation, or test dataset splits typically found in machine learning experiments. |
| Hardware Specification | Yes | Our proposed techniques for computing ESC solutions were evaluated in a multi-threaded implementation written in C++ and executed on 2nd generation Intel Core desktop hardware, an i7-3930 (6 cores, 64GB of RAM) for complete set generations and an i7-2700 (4 cores, 32GB RAM) for the multi-stage construction with nested Hitting Sets. |
| Software Dependencies | No | The paper mentions that the implementation was 'written in C++', but does not provide any specific version numbers for the C++ compiler, libraries, or other software dependencies. |
| Experiment Setup | Yes | As edge cost function c we used travel time along an edge... Energy consumption of an EV was modeled as explained in the introduction using distance data from OSM and elevations provided by the Shuttle Radar Topography Mission (NASA, 2015). B corresponds to a battery capacity which translates to a certain terrain dependent cruising range. We use a capacity B for PF and TU that allows to drive 40 kilometers on average, and about 125 kilometers for the larger graphs. Our α, which models how much going uphill increases the energy consumption, equals 4... For the BW network we computed a k = 32-Hop Cover C (146, 494 nodes) which corresponds to an ESC solution with B = 8832 (and cruising range of about 9km in flat terrain). |