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.
Nowadays, due to the rapid increment of the pandemic COVID-19 which affects the teaching environment, a need appeared to use a new alternative education style, which helps to decrement the injuries and the continuation of the education. This study designs an application to measure the teachers' readiness to use e-learning. A questionnaire was designed to collect as much information as possible to measure the teachers' readiness to adopt e-learning in Iraqi schools after pandemic COVID-19 by analyzing the factors that affect the education process. The questionnaire consists of thirty-three questions in the Arabic language and includes three sections (background information, attitude toward e-learning, and computer skills). The collected responses are taken from Mosul's primary and secondary schools' teachers and the sample size is (261). A test of reliability was carried out on the study instrument, and the value of Cronbach's alpha was 0.913. The MATLAB R2014a was used to build an application in order to do the analyzing process and determine the readiness of e-learning.
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