A Novel Quantum Dandelion Reproduction Optimization Algorithm for solving Low-carbon FJSP

Faced with complex and ever-changing market demands, enterprises not only need to meet customer needs by providing better products and services to maximize their overall efficiency, but also need to adjust their production strategies to achieve energy conservation and emission reduction. Considering that the dandelion algorithm is not easily trapped in local optima and has strong robustness, a solution strategy based on the dandelion reproduction algorithm is proposed in this paper, but the algorithm drifting down operation adopts a fixed step size, and the wandering search near the optimal position consumes too long a time, which will result in the loss of dandelion individuals that have a high degree of adaptability. Combining the properties of quantum state entanglement, superposition and interference, this paper further proposes Hybrid Quantum Dandelion Reproduction Optimization Algorithm (HQDRO). The performance of the algorithm is verified and comparatively analyzed through the classical examples of workshop scheduling, and the results show that the use of HQDRO to solve the low-carbon FJSP problem not only improves the convergence speed of the algorithm, obtains high-quality feasible solutions quickly, but also provides an effective solution to the FJSP problem.