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To address the issue of further collaboratively optimizing process continuity, time cost, and equipment utilization in identical two-workshop distributed integrated scheduling, an identical two-workshop distributed integrated scheduling algorithm based on the improved bipartite graph (DISA-IBG) is proposed. The method introduces an improved bipartite graph cyclic decomposition strategy that incorporates both the topological characteristics of the process tree and the dynamic resource constraints of the workshops. Based on the resulting substrings, a multi-substring weight scheduling strategy is constructed to achieve a systematic evaluation of substring priorities. Finally, a substring pre-allocation strategy is designed to simulate the scheduling process through virtual allocation, which enables dynamic adjustments to resource allocation schemes during the actual scheduling process. Experimental results demonstrate that the algorithm reduces the total product makespan to 37 h while improving the overall equipment utilization to 67.8%, thereby achieving the synchronous optimization of \"shorter processing time and higher equipment efficiency.\" This research provides a feasible scheduling framework for intelligent sensor-enabled manufacturing environments and lays the foundation for data-driven collaborative optimization in cyber-physical production systems.

In the mobile edge computing environment, aiming at the problems of few classifications of resource nodes and low resource utilization in the process of multi-user and multi-server resource allocation, a resource optimization algorithm based on comprehensive utility is proposed. First, the algorithm improves the Naive Bayes algorithm, obtains the conditional probabilities of job types based on the established Naive Bayes formula and calculates the posterior probabilities of different job types under specific conditions. Second, the classification method of resource service nodes is designed. According to the resource utilization rate of the CPU and I/O, the resource service nodes are divided into CPU main resources and I/O main resources. Finally, the resource allocation based on comprehensive utility is considered. According to three factors, resource location, task priority and network transmission cost, the matching computing resource nodes are allocated to the job, and the optimal solution of matching job and resource nodes is obtained by the weighted bipartite graph method. The experimental results show that, compared with similar resource optimization algorithms, this method can effectively classify job types and resource service nodes, reduce resource occupancy rate and improve resource utilization rate.