TELKOMNIKA Telecommunication, Computing, Electronics and Control
Solving software project scheduling problem using grey wolf optimization

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Solving software project scheduling problem using grey wolf optimization

Grey wolf optimization
Resource-constrained project
scheduling
Software project management
Software project scheduling
problem

In this paper, we will explore the application of grey wolf optimization
(GWO) methodology in order to solve the software project scheduling
problem (SPSP) to seek an optimum solution via applying different instances
from two datasets. We will focus on the effects of the quantity of employees
as well as the number of tasks which will be accomplished. We concluded
that increasing employee number will decrease the project’s duration, but we
could not find any explanation for the cost values for all instances that
studied. Also, we concluded that, when increasing the number of the tasks,
both the cost and duration will be increased. The results will compare with a
max-min ant system hyper cube framework (MMAS-HC), intelligent water
drops algorithm (IWD), firefly algorithm (FA), ant colony optimization
(ACO), intelligent water drop algorithm standard version (IWDSTD), and
intelligent water drop autonomous search (IWDAS). According to these
study and comparisons, we would like to say that GWO algorithm is a better
optimizing tool for all instances, except one instance that FA is outperform
the GWO.

Marrwa Abd-AlKareem Alabajee, Dena Rafaa Ahmed, Taghreed Riyadh Alreffaee

http://journal.uad.ac.id/index.php/TELKOMNIKA

Oct 14, 2021

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