Hybrid approach for solving resourceconstrained software project scheduling problem

A software project schedule management tool is essential for monitoring the project duration and budget, two key factors that will directly affect project success. Assignment of personnel for tasks, initiation order of tasks, task completion time, possible delays in starting a task, and money already spent and the remaining budget are tracked via the software project tool. This study aims to provide software project managers with a powerful tool to solve the Resource-Constrained Software Project Scheduling Problem (RCSPSP) with minimum project duration, minimum project budget, and minimum waiting time of tasks. For this purpose, a hybrid approach is used in this study, in which the Genetic Algorithm (GA) is supported by Grey Wolf Optimization (GWO), Artificial Bee Colony Algorithm (ABC) and chaotic logistic map. The pack hierarchy model in GWO is used to contribute to the convergence success of GA, and scout bee methodology in ABC is adopted into the method to avoid being trapped at local minima. The chaotic logistic map technique is also used to improve randomness. The developed hybrid method has been tested with datasets in the Intelligent Multi-Objective Project Scheduling Environment (iMOPSE). The results are compared with in literature algorithms and statistically analyzed using non-parametrik tests. According to test results, an improvement of up to 7% in the one employee assignment model and up to 15% in the multiemployee assignment model has been observed. The results show that the method has good and competitive performance in terms of solution stability and closeness to optimal solutions.