Efficient Location-Aware Scheduling of Maintenance Tasks in Shop Floors

In this paper, the core functionalities of a state-of-the-art Human Resources (HR) optimization engine of a decision support system (DSS) are presented. The main objective is to schedule maintenance tasks and assign them to human resources. Each task is scheduled and assigned to the most suitable employee based on multiple criteria (such as trade, workload, etc.) by using static and dynamic information. A heuristic branch and bound static scheduling method which creates the initial work schedule given a set of tasks and a list of employees is presented. Furthermore, a dynamic scheduling method which schedules arriving critical tasks in real-time has been extended in order to utilize additional information, such as the position and the availability of assets and employees. Short response times are important when a corrective maintenance task of high priority has to be handled as soon as possible; therefore the distance between an employee and the task's location is considered. Employees are equipped with sensors for monitoring their current location in real-time. In case sensors are not available, approximate localization is performed indirectly based on context information. A case study demonstrates how the approach works when a critical new task has to be performed.