Quantum-inspired African vultures optimization algorithm with elite mutation strategy for production scheduling problems

Abstract The production scheduling (PS) problem is a challenging task that involves assigning manufacturing resources to jobs while ensuring that all constraints are satisfied. The key difficulty in PS is determining the appropriate order of operations. In this study, we propose a novel optimization algorithm called the quantum-inspired African vultures optimization algorithm with an elite mutation strategy (QEMAVOA) to address this issue. QEMAVOA is an enhanced version of the African vulture optimization algorithm that incorporates three new improvement strategies. Firstly, to enhance QEMAVOA’s diversification ability, the population diversity is enriched by the introduction of quantum double-chain encoding in the initialization phase of QEMAVOA. Secondly, the implementation of the quantum rotating gate will balance QEMAVOA’s diversification and exploitation capabilities, leading the vulture to a better solution. Finally, with the purpose of improving the exploitability of QEMAVOA, the elite mutation strategy is introduced. To evaluate the performance of QEMAVOA, we apply it to two benchmark scheduling problems: flexible job shop scheduling problem and parallel machine scheduling. The results are compared to those of existing algorithms in the literature. The test results reveal that QEMAVOA surpasses comparison algorithms in accuracy, stability, and speed of convergence.