Citation
Self-Adapting Large Neighborhood Search:Application to Single-Mode Scheduling Problems. In proceedings of the 3rd Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2007), 28 -31 August 2007, Paris, France, pages 276-284, 2007.
Paper
Abstract
Providing robust scheduling algorithms that can solve a large variety of scheduling problems with good performance is one of the biggest challenge of practical schedulers today. In this paper we present a robust scheduling algorithm based on Self-Adapting Large Neighborhood Search and apply it to a large panel of single-mode scheduling problems. The approach combines Large Neighborhood Search with a portfolio of neighborhoods and completion strategies together with Machine Learning techniques to converge on the most efficient neighborhoods and completion strategies for the problem being solved. The algorithm is evaluated on a set of 21 scheduling benchmarks, most of which are well established in the scheduling community. Despite the generality of the approach, for 17 benchmarks out of 21, its mean relative distance to state-of-the-art problem specific algorithms is less than 4%. It even outperforms state-of-the-art problem-specific algorithms on 7 benchmarks clearly showing that our algorithm offers a valuable compromise between robustness and performance.
You can download the pdf of this publication from here
doi
This publication does not have a doi, so we cannot provide a link to the original source
What is a doi?: A doi (Document Object Identifier) is a unique identifier for sicientific papers (and occasionally other material). This provides direct access to the location where the original article is published using the URL http://dx.doi/org/xxxx (replacing xxx with the doi). See http://dx.doi.org/ for more information
URL
This pubication does not have a URL associated with it.
The URL is only provided if there is additional information that might be useful. For example, where the entry is a book chapter, the URL might link to the book itself.
Bibtex
@INPROCEEDINGS{2007-276-284-P, author = {P. Laborie and D. Godard},
title = {Self-Adapting Large Neighborhood Search:Application to Single-Mode Scheduling Problems},
booktitle = {In proceedings of the 3rd Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2007), 28 -31 August 2007, Paris, France},
year = {2007},
editor = {P. Baptiste and G. Kendall and A. Munier-Kordon and F. Sourd},
pages = {276--284},
note = {Paper},
abstract = {Providing robust scheduling algorithms that can solve a large variety of scheduling problems with good performance is one of the biggest challenge of practical schedulers today. In this paper we present a robust scheduling algorithm based on Self-Adapting Large Neighborhood Search and apply it to a large panel of single-mode scheduling problems. The approach combines Large Neighborhood Search with a portfolio of neighborhoods and completion strategies together with Machine Learning techniques to converge on the most efficient neighborhoods and completion strategies for the problem being solved. The algorithm is evaluated on a set of 21 scheduling benchmarks, most of which are well established in the scheduling community. Despite the generality of the approach, for 17 benchmarks out of 21, its mean relative distance to state-of-the-art problem specific algorithms is less than 4%. It even outperforms state-of-the-art problem-specific algorithms on 7 benchmarks clearly showing that our algorithm offers a valuable compromise between robustness and performance.},
owner = {user},
timestamp = {2012.05.21},
webpdf = {2007-276-284-P.pdf} }