Using priority rules for resource-constrained project scheduling problem in static environment
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{DUMIC:2022:cie,
-
author = "Mateja Dumic and Domagoj Jakobovic",
-
title = "Using priority rules for resource-constrained project
scheduling problem in static environment",
-
journal = "Computer \& Industrial Engineering",
-
volume = "169",
-
pages = "108239",
-
year = "2022",
-
ISSN = "0360-8352",
-
DOI = "doi:10.1016/j.cie.2022.108239",
-
URL = "https://www.sciencedirect.com/science/article/pii/S0360835222003096",
-
keywords = "genetic algorithms, genetic programming, Resource
constrained project scheduling problem, Priority rules,
Iterative priority rules, Rollout, Static environment",
-
abstract = "The resource-constrained project scheduling problem
(RCPSP) is one of the scheduling problems that belong
to the class of NP-hard problems. Therefore, heuristic
approaches are usually used to solve it. One of the
most commonly used heuristic approaches are priority
rules (PRs). PRs are easy to use, fast and able to
respond to system changes, which makes them applicable
in a dynamic environment. The disadvantage of PRs is
that when applied in a static environment, they do not
achieve results of the same quality as heuristic
approaches designed for a static environment. Moreover,
a new PR must be evolved separately for each
optimization criterion, which is a challenging process.
Therefore, recently significant effort has been put
into the automatic development of PRs. Although PRs are
mainly used in a dynamic environment, they are also
used in a static environment in situations where speed
and simplicity are more important than the quality of
the obtained solution. Since PRs evolved for a dynamic
environment do not use all the information available in
a static environment, this paper analyzes two
adaptations for evolving PRs in a static environment
for the RCPSP - iterative priority rules and rollout
approach. This paper shows that these approaches
achieve better results than the PRs evolved and used
without these adaptations. The results of the
approaches presented in the paper were also compared
with the results obtained with the genetic algorithm as
a representative of the heuristic approaches used
mainly in the static environment",
- }
Genetic Programming entries for
Mateja Dumic
Domagoj Jakobovic
Citations