Low-carbon supply chain resources allocation based on quantum chaos neural network algorithm and learning effect

This paper focuses on designing a novel quantum chaos neural network algorithm for low-carbon supply chain resources allocation problem (LCSCRAP) which is an efficient extension of the resources allocation. Quantum chaos neural network algorithm based on cloud model (C-QCNNA) is put forward to solve the LCSCRAP with several conflicting and incommensurable multi-objectives. The results of simulation experiments have been obtained from the set of standard instances, and the C-QCNNA is confirmed to be very competitive after extensive experiments. The computational results have proved that the C-QCNNA is an efficient and it is effective for the LCSCRAP. This study can not only develop the C-QCNNA for the LCSCRAP, but also promote the C-QCNNA and cloud model theory themselves. Simultaneously, it has important theoretical and practical significance.