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Abstract: US railroad companies spend billions of dollars every year on track maintenance in order to ensure safety and operational efficiency. Optimizing the production team (i.e., large maintenance team) schedule is a very complex problem with major cost implications. In current practice, the decision making process for production team scheduling is largely manual and primarily relies on the knowledge and judgment of experts. This paper addressed the production team scheduling problem by formulating it as a time–space network model with many types of challenging side constraints. Some of these constraints are identified from industry practice and formulated for the first time. Multiple neighborhood search and other enhancement algorithms were proposed to solve the model. The proposed modeling approach has been tested through numerical experiments and also applied to large-scale real-world problem instances, and superior computational performances were found. The proposed approach has been adopted by a Class I railroad to help make annual network maintenance scheduling decisions. [Copyright &y& Elsevier]