Intelligent scheduling method for life science automation systems

Modern life science automation combines laboratory automation systems and mobile transportation systems. The distributed automated workstations integrate different kinds of automatic devices to increase the throughput and quality. The mobile transportation system manages mobile robots and human laboratory assistants for transfer services between interacting automation systems. In order to combine laboratory automation systems and mobile transportation systems, a superordinate management system - Hierarchical Workflow Management System (HWMS) - is established to handle the life science laboratories (celisca Germany). In this paper, a new scheduling method for life science automation systems is proposed to reduce the costs of workflows. Typically, the methods of automation systems are prepared and executed directly on the local computer of the distributed automated stations. According to this feature, the strategy for the scheduling is determined. The algorithm of scheduling in this paper is a Genetic Algorithm (GA). The simulation of this scheduler is used to evaluate this approach and to make a comparison with an intrinsic solution. Results indicate that this approach improves the efficiency and stability of the life science automation system.