Proactive Algorithms for Scheduling with Probabilistic Durations
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 1201-1205.
Abstract
Proactive scheduling seeks to generate high quality solutions despite execution time uncertainty. Building on work in [Beck and Wilson, 2004], we conduct an empirical study of a number of algorithms for the job shop scheduling problem with probabilistic durations. The main contributions of this paper are: the introduction and empirical analysis of a novel constraint-based search technique that can be applied beyond probabilistic scheduling problems, the introduction and empirical analysis of a number of deterministic filtering algorithms for probabilistic job shop scheduling, and the identification of a number of problem characteristics that contribute to algorithm performance.
@inproceedings{ijcai2005p72,
title = {Proactive Algorithms for Scheduling with Probabilistic Durations},
author = {Beck, J. Christopher and Wilson, Nic},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1201--1205},
year = {2005},
url = {https://ijcai.org/proceedings/2005/72},
}
TY - CPAPER TI - Proactive Algorithms for Scheduling with Probabilistic Durations AU - Beck, J. Christopher AU - Wilson, Nic T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 1201 EP - 1205 UR - https://ijcai.org/proceedings/2005/72 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
