Multi-Objective Genetic Algorithm for Software Project Scheduling

Software project scheduling is an important aspect which influences the duration, cost, and risks involved in a software project. The conventional scheduling approaches like Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT) consider mainly the time factor and do not help in optimizing the multi-objective problem. In this paper, a scheduling algorithm based on Multi-Objective Genetic Algorithm (MOGA) is designed for managing the software project schedule. The proposed system takes into account the dependencies between different software tasks, their durations, cost, resources, and risks involved. The genetic algorithm operations like selection, crossover, and mutation are performed to provide an optimized schedule by considering dependency and resource constraints. The proposed system optimizes the project schedule by minimizing its duration, cost, and risks. The performance of the proposed system is compared with the conventional methods CPM and PERT. It has been found from experimental analysis that the proposed MOGA-based scheduling approach improves scheduling efficiency and provides optimized schedules with minimum duration, cost, and risk.