Proactive, dynamic and multi-criteria scheduling of maintenance activities

In maintenance services skills management is directly linked to the performance of the service. A good human resource management will have an effect on the performance of the plant. Each task which has to be performed is characterized by the level of competence required. For each skill, human resources have different levels. The issue of making a decision about assignment and scheduling leads to finding the best resource and the correct time to perform the task. To solve this problem, managers have to take into account the different criteria such as the number of late tasks, the workload or the disturbance when inserting a new task into an existing planning. As there is a lot of estimated data, the managers also have to anticipate these uncertainties. To solve this multi-criteria problem, we propose a dynamic approach based on the kangaroo methodology. To deal with uncertainties, estimated data is modeled with fuzzy logic. This approach then offers the maintenance expert a choice between a set of the most robust possibilities. Proactive, dynamic and multi-criteria scheduling of maintenance activities In maintenance services skills management is directly linked to the performance of the service. A good human resource management will have an effect on the performance of the plant. Each task which has to be performed is characterized by the level of competence required. For each skill, human resources have different levels. The issue of making a decision about assignment and scheduling leads to finding the best resource and the correct time to perform the task. To solve this problem, managers have to take into account the different criteria such as the number of late tasks, the workload or the disturbance when inserting a new task into an existing planning. As there is a lot of estimated data, the managers also have to anticipate these uncertainties. To solve this multi-criteria problem, we propose a dynamic approach based on the kangaroo methodology. To deal with uncertainties, estimated data is modeled with fuzzy logic. This approach then offers the maintenance expert a choice between a set of the most robust possibilities.