Dynamic Software Project Scheduling through a Proactive-Rescheduling Method

Abstract

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An Evolutionary Hyper-heuristic for the Software Project Scheduling Problem

“…The experiments performed on a set of 48 benchmark instances showed that the proposed algorithm can solve the SPSP effectively and outperform a strategy based on a simple random selection of the operators as well as a state-of-the-art approach from the literature. Future work includes a detailed analysis of the behavior of the proposed algorithm and the reasons for its ability to generate better solutions; an extension of the proposed algorithm in order to deal with the dynamic SPSP [13]; the use of alternative AOS strategies; and the inclusion of more aspects that could affect software projects into the problem formulation.…”

A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms

“…As can be seen in the results section, the algorithm always converges to the best solution while the number of evaluations to the fitness function decreases monotonically. To calculate the population's entropy we used the Shannon entropy equation as shown in Equation 1.…”

Dynamic Multiobjectives Optimization With a Changing Number of Objectives

“…In project scheduling, we usually want to minimize the makespan and total salary cost simultaneously. However, given a tighter deadline must be met, the salary cost may not be important and would not be considered as an objective any longer under the new circumstance [23][24][25]. In some other examples, if an application is running on a system with a wired power supply, there is no need to consider the energy consumption [26].…”