Abstract
Industrial Internet-of-Things brings cloudCloud and edge resources together to support customized manufacturing. With cloud-edgeCloud-edge collaboration, large-scale computational tasks of product and process simulation, force and torque analysis, real-timeReal-Time process controlControl, and so forth, are to be executed in cloud or edge resources, while related manufacturing tasks are to be executed in distributed end devices simultaneously. In this circumstance, hybrid task schedulingTask scheduling becomes a key to implement efficient and intelligent manufacturing. In this paper, a multi-indicatorMulti-indicator-assisted dynamic Bees Algorithm (MIDBA) is presented to solve large-scale task scheduling problem for cloud-edgeCloud-edge collaborative manufacturing. The operators of the Bees AlgorithmBees Algorithm, THE are modified according to multiple indicators to find suitable cloud-edgeCloud-edge collaborative modes, cloud and edge resources. A parallelParallel search scheme is also designed to accelerate the scheduling process for large-scale tasks. We implement numerical studies to examine the proposed algorithm on this problem. Compared to the state-of-the-art algorithms, the parallel MIDBA can find better solutions with lesser time.
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