With the deep integration of industrial internet of things (IIoT) and artificial intelligence (AI) technology

automated guided vehicles (AGVs) and mobile robots have been widely used in internet of things-enabled floor shop. In view of many complex factors such as real-time dynamic changes and uncertain conditions in the workshop

the SP– MCTS (Single-player Monte-Carlo tree search algorithm) method with each job group as a subtree and real-time state as the root node is proposed to implement adaptive scheduling of workshop. The problem of robot scheduling is formulated as a Markov decision process (MDP) in which state representation

action representation

reward function

and optimal policy

are described in detail. In the real-time scheduling process

the proposed search method establishes a subtree for each job group

and only the state relationship between two adjacent groups is considered in optimization

thereby the calculation difficulty is simplified. In the subtree search process

SP–MCTS is used to search with the real-time state as the root node. At the same time

the expansion method and the pruning method are used to carry out strategy exploration and information accumulation respectively. Therefore

the deeper the real-time status node in the subtree

the faster and more accurate the optimal strategy is obtained. The case study based on a real-world shop is proven and the results validate the effectiveness and superiority of the proposed approach.