Task allocation with tardiness minimization for maintenance delivery of Smart Product-Service Systems

Data collection, analysis, and exploitation are among the most discussed topics in current researches related to Industry 4.0, especially as far as production themes are concerned. Some authors have discussed the benefits that could originate from the exploitation of product functioning data for maintenance delivery in the Product-Service System (PSS) field. Despite this, a comprehensive approach considering both the asset and the service perspectives for the improvement of the service delivery process is still missing. The authors try to deal with the service delivery decision-making problem presenting a task allocation model aimed at minimizing the total tardiness in maintenance delivery. The model considers actual information from both the asset (e.g. the Residual Useful Life (RUL) of a component) and the service (e.g. the operator’s calendar, the various resolution approaches available and the mean time to repair with a specific approach, customer features) to match the tasks and the operators. The paper describes the model development, discussing the benefits of data exploitation in the decision-making process. Moreover, the role and benefits of such a model in a data-driven decision-making process for PSS delivery are discussed. Finally, the paper presents the model limitations and its possible extensions, considering additional constraints and different objective functions.