MODIFIED GENETIC PROJECT PLANNING ALGORITHM IMPLEMENTED WITH THE USE OF CLOUD COMPUTING

Abstract

The paper proposes a structure of a modified genetic algorithm for solving resource constrained
project scheduling problem implemented with the use of cloud computing, a computational
experiment was conducted, during which the results of the proposed algorithm were compared
with the best known, at the moment, results. Based on the results of the experiment, it was concluded
that the proposed algorithm can be used to plan the work of real projects, since it is possible
to draw up schedules for projects with the number of works n = 90 for an acceptable period of
time. When planning projects with the number of jobs n = 30, n = 60, n = 90, 120, the execution
time of the proposed algorithm was less than the execution time of the standard genetic algorithm
by 2.8, 4, 5.5 and 6.8 times, respectively. Due to the fact that the task of constructing a project
schedule taking into account limited resources is NP-difficult, the problem of creating new and
modifying existing methods for solving it remains relevant. For planning projects with a large
number of works, it is advisable to use cloud computing, since planning such projects can require
a lot of time and computing resources. In this regard, the algorithm proposed in this paper differs
from the existing ones by using cloud computing to distribute the load between workstations on
which this algorithm is simultaneously running. The use of modified operators in the genetic algorithm,
as well as the use of cloud infrastructure as a service for implementing a distributed genetic
algorithm, determines the scientific novelty of the study.