Rotational Diversity in Multi-Cycle Assignment Problems
Authors: Helge Spieker, Arnaud Gotlieb, Morten Mossige7724-7731
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show the applicability on a multi-cycle variant of the multiple knapsack problem and a real-world case study on the test case selection and assignment problem, an example from the software engineering domain, where test cases have to be distributed over compatible test machines." and "5 Experimental Evaluation We consider two problem types for evaluation: a) a multi-cycle variant (MCMKP) of the known multiple knapsack problem (MKP) to evaluate trade-offs between the strategies; b) test case selection and assignment (TCSA) as a real-world case study from the area of software testing to evaluate the practical interest of our approach. |
| Researcher Affiliation | Collaboration | Helge Spieker, Arnaud Gotlieb Simula Research Laboratory P.O. Box 134 1325 Lysaker, Norway {helge,arnaud}@simula.no Morten Mossige University of Stavanger Stavanger, Norway ABB Robotics Bryne, Norway morten.mossige@uis.no |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | We evaluate the strategies on TCSA, based on actual test data from our industrial partner." and "To generate problem instances, we follow the procedure by Pisinger (1999), as described in Fukunaga (2011)". While it references generation procedures, it doesn't provide access to a public dataset used. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | Yes | Our strategies and the experimental setup are implemented in Python. The assignment problem is modeled with Mini Zinc 2.0 (Nethercote et al. 2007), following the presented GAP formulation, and is solved with IBM CPLEX 12.7.1. |
| Experiment Setup | Yes | All strategies are run on each scenario with a 60 second timeout for the GAP solver. The thresholds γ for the Objective Switch strategy are 10, 20, 30, and 40. |