On Designing Learning Control Scheme for Multilayer Supply Chain Networks With Constraints

In this study, a new learning control scheme is designed to investigate the stability of a multilayer supply chain network (SCN) and to further improve the convergence speed of the nodes' states of such a multilayer SCN. Specifically, a multilayer SCN model with three layers is first established and some practical constraints on the states of the proposed SCN model are involved and discussed. By taking the quantities of goods transmitted between different nodes as control inputs, a new kind of learning control scheme is subsequently proposed to discuss the stability of the nodes' states within the SCN. It is further shown that the convergence speed of nodes' states with this scheme is faster than that yielded by using some traditional schemes. The contributions of our scheme are twofold: 1) it can save the limited control resource and 2) it can improve the convergence speeds of the states of all nodes. Numerical simulations are finally given to illustrate the effectiveness and advantages of the designed learning scheme.