New strategies for stochastic resource-constrained project scheduling
| Autor: | Salim Rostami, Roel Leus, Stefan Creemers |
|---|---|
| Přispěvatelé: | Lille économie management - UMR 9221 (LEM), Université d'Artois (UA)-Université catholique de Lille (UCL)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Rate-monotonic scheduling
Earliest deadline first scheduling Mathematical optimization 021103 operations research Computer science 0211 other engineering and technologies General Engineering Uncertainty Stochastic activity durations 02 engineering and technology Flow shop scheduling Dynamic priority scheduling Management Science and Operations Research Fair-share scheduling Artificial Intelligence Genetic algorithm scheduling Two-level scheduling Lottery scheduling Scheduling policies 0202 electrical engineering electronic engineering information engineering [SHS.GESTION]Humanities and Social Sciences/Business administration 020201 artificial intelligence & image processing Project scheduling Software |
| Zdroj: | Journal of Scheduling Journal of Scheduling, Springer Verlag, 2017, 21, pp.349-365. ⟨10.1007/s10951-016-0505-x⟩ Journal of Scheduling, 2017, 21, pp.349-365. ⟨10.1007/s10951-016-0505-x⟩ |
| ISSN: | 1094-6136 1099-1425 |
| Popis: | © 2017 Springer Science+Business Media New York We study the stochastic resource-constrained project scheduling problem or SRCPSP, where project activities have stochastic durations. A solution is a scheduling policy, and we propose a new class of policies that is a generalization of most of the classes described in the literature. A policy in this new class makes a number of a priori decisions in a preprocessing phase, while the remaining scheduling decisions are made online. A two-phase local search algorithm is proposed to optimize within the class. Our computational results show that the algorithm has been efficiently tuned toward finding high-quality solutions and that it outperforms all existing algorithms for large instances. The results also indicate that the optimality gap even within the larger class of elementary policies is very small. ispartof: Journal of Scheduling vol:21 issue:3 pages:349-365 status: published |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink