Optimization of enterprise organizational structure based on community detection algorithms in complex networks

As businesses get more complicated, figuring out how to best optimize their organizational structure and improve the efficiency of work scheduling and resource allocation has become a major concern for managers. We suggest a model called NetOrg-Opt for improving the structure of a company, and we apply the Louvain algorithm to find communities. The results of tests to improve organizational structure demonstrate that the NetOrg-Opt model works the best of all the algorithms, with a modularity value of 0.91. The task assignment and resource optimization tests demonstrate that NetOrg-Opt can finish a task in 120 h and use 92% of its resources. This shows that the NetOrg-Opt model has a lot of potential for improving the structure of a business. The NetOrg-Opt approach has a lot of potential for improving the structure of businesses in both experiments.