We consider the maintenance process of gas turbines used in the Oil and Gas industry: the capital parts are first removed from the gas turbines and replaced by parts of the same type taken from the warehouse; then, they are repaired at the workshop and returned to the warehouse for use in future maintenance events. Experience-based rules are used to manage the flow of the parts for a profitable gas turbine operation. In this article, we formalize the part flow management as a sequential decision problem and propose reinforcement learning for its solution. An application to a scaled-down case study derived from real industrial practice shows that reinforcement learning can find policies outperforming those based on experience-based rules.

For maintenance of gas turbines (GTs) in oil and gas applications, capital parts are removed and replaced by parts of the same type taken from the warehouse. When the removed parts are found not broken, they are repaired at the workshop and returned to the warehouse, ready to be used in future maintenance. The management of this flow is of great importance for the profitability of a GT plant. In this paper, we adopt a previously developed formalized framework of the part flow and reinforcement learning (RL) to optimize part flow management. The formal framework and RL algorithm are extended to account for the stochastic failure process of the involved parts. An application to a scaled-down case study derived from an industrial application is illustrated.

In this paper, we present the work conducted by Vodafone to enrich the understanding of people movement in Italy during the outbreak of the Coronavirus in 2020, and the tool developed to support the decisions taken by the authorities during that period. We have developed a solution to anonymously monitor the daily movements of Vodafone SIMs in Italy, at aggregate level, at different spatial and temporal granularity, to provide insights into the movements of Italians.