Adaptive Task Migration in Multiplex Networked Industrial Chains

Abstract

In recent years, the cooperation structures of industrial chains have evolved into multiplex networks, in which product agents are connected through various types of links. Due to the constraints of the multi-coupled interaction structure of the multiplex networked industrial chains, the load imbalances generated by the industrial production processes will cascade in and between different network layers, thus affecting the load balance of the whole system. The challenges that arise when attempting such load balancing among multiplex networked industrial chains are twofold: 1) The multiplex networked interaction structure adds new constraints to traditional multiagent task migration problems, which increases the solution space dimension, and 2) The cascaded load imbalances require tasks to be migrated adaptively, which complicates the solution space structure, and it is proven $\mathcal {NP}$ -hard to achieve such load balancing. Then, a hierarchical cascade-triggered task migration algorithm is designed, where key agents are selected to cooperate with each other in a hierarchical control form to achieve load balancing between network layers, and appropriate agents are cascade-triggered to migrate tasks adaptively to achieve load balancing in network layers. Finally, the algorithm is extensively evaluated in experiments, concluding that it can significantly increase the resulting utility and task completion proportion, while efficiently reducing the task completion cost. In particular, the algorithm does not appear to be statistically different in the resulting optimization objectives from the optimal result computed by the CPLEX solver, but it may consume less runtime.