Mining companies that carry out material movement activities for the heap leaching process maintain a constant sequence of operations. Properly scheduling these operations is crucial to reduce operating costs. The scheduling of mining activities can be modeled as a flow shop problem. The efficient scheduling of mine operations must be continuously monitored to ensure positive impacts. Implementing technologies such as Digital Twin for process planning and control in industries such as mining is now expected. This paper presents a methodology based on an asynchronous team of metaheuristics to solve the flow shop problem resulting from a case study of a Chilean mining company. Tabu search algorithms were used as team members. An asynchronous team was selected because it maximizes the use of computational resources, thus providing the Digital Twin with a sophisticated optimization algorithm for operation scheduling. The performance of the asynchronous team was validated by benchmarking against the best works published in the literature. A set of classical instances was used due to the company's limitations in using their information. As future work, it is expected to contrast the improvements in planning and control of mining activities with the asynchronous team embedded in the Digital Twin.