Quantum-Inspired Optimization for Task Scheduling in Software Development Projects

Software project development, characterized by numerous tasks and several engineers, necessitates effective project scheduling and personnel allocation for successful and timely completion. Tackling the inherent complexities of Software Project Scheduling (SPS) including personnel quality requirements and capability constraints is of paramount importance for software companies. This study aims to engineer robust project scheduling to enhance task completion efficiency, reduce resource waste, and ensure the punctual delivery of project milestones. Our proposed solution models the problem as a Software Project Scheduling Problem (SPSP), subsequently transformed into a Quadratic Unconstrained Binary Optimization (QUBO) model using quantum-inspired techniques. This model is then solved using a digital annealing device. We examine our proposed quantum-inspired method's effectiveness in solving SPSP through this experimental implementation, comparing its performance with the Simulated Annealing (SA) algorithm. The experimental findings reveal that the objective function, encapsulating the weighted sum of all job personnel costs and job end times, produced superior outcomes under the Digital Annealing (DA) algorithm compared to the SA algorithm. Moreover, the DA algorithm demonstrated greater stability and reduced runtime as the volume of input data expanded.