A framework for adaptive offline task scheduling and resources assignment in industrial environments
The proliferation of IoT (Internet of Things) devices and networks along with the use of interactive user interfaces in the modern production environments, provide new opportunities for more effective assignment of tasks to resources. This paper proposes a novel integrated framework for offline task scheduling and resources assignment, which can be used in manufacturing industries. The proposed framework can be a part of an intelligent production system in an Industry 4.0 shop floor. For the creation of the work schedule referring to a time interval given a set of tasks and resources, such as workers and machines, multiple criteria are considered: execution restrictions, capabilities of resources, preferences, and past performance data. The main advantage of the scheduling framework is its potential to support adaptive and evolving decision-making based on feedback information received from various sources of the environment. Continuously updated data models that include the characteristics, requirements and performance metrics for each resource in a dynamic environment, as well as configuration settings and feedback provided by the manager, are taken into account in order to generate work schedules which can be efficient in terms of task-to-resource mapping and in accordance with manager’s preferences and policies. The implementation requirements of the proposed framework along with an initial system towards the implementation of the framework are also presented in this paper.