A Framework for Constraint Based Local Search using Essence
Authors: Özgür Akgün, Saad Attieh, Ian P. Gent, Christopher Jefferson, Ian Miguel, Peter Nightingale, András Z. Salamon, Patrick Spracklen, James Wetter
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare SNS to the other solvers on 5 problem classes: Capacitated Vehicle-Routing Problem (CVRP); Progressive Party Problem (PPP, CSPLib 13); Minimum Energy Broadcast (MEB, CSPLib 48); SONET (CSPLib 56); Rack Configuration Problem (Rack Conf, CSPLib 31). ... Table 3 summarises our results. |
| Researcher Affiliation | Academia | School of Computer Science, University of St Andrews, UK {ozgur.akgun, sa74, ian.gent, caj21, ijm, pwn1, Andras.Salamon, jlps}@st-andrews.ac.uk |
| Pseudocode | Yes | Algorithm 1 SNS() ... Algorithm 2 SNS-Improve(Active solution σ) |
| Open Source Code | No | The paper does not provide a direct statement or link for the open-sourcing of the SNS framework described in the paper. It mentions a third-party tool 'Chuffed' which is available on GitHub. |
| Open Datasets | Yes | We compare SNS to the other solvers on 5 problem classes: Capacitated Vehicle-Routing Problem (CVRP); Progressive Party Problem (PPP, CSPLib 13); Minimum Energy Broadcast (MEB, CSPLib 48); SONET (CSPLib 56); Rack Configuration Problem (Rack Conf, CSPLib 31). |
| Dataset Splits | No | The paper mentions running experiments on 'instances' and describes the process of finding 'random feasible solutions' and 'random restarts', but it does not specify explicit training, validation, or test dataset splits or percentages. |
| Hardware Specification | Yes | Experiments were run on an Intel Xeon E5-2640 v4 at 2.40GHz with 20 cores (40 hyper-threads) with 20 processes run in parallel. |
| Software Dependencies | Yes | The Mini Zinc instance is then specialised via Mini Zinc 2.1.7 for Osca R/CBLS and Chuffed. For the LNS methods, each problem class was implemented in Choco 4.0.6 |
| Experiment Setup | Yes | Once Optimise finds a solution, it will continue to search for a solution with better objective value, until the time limit of 500ms is reached. ... In this paper we set α to 0.001 and β to 1/16. ... the time limit is initially 1 second rather than 100 ms. ... a limit of 50 backtracks |