Accuracy and Localization-Aware Rescheduling for Flexible Flow Shops in Industry 4.0

摘要

Industry 4.0 revolution aims to satisfy the manufacturing systems need to deal with the unexpected customers behaviour and market variation. Thanks to Internet of Things (IoT) technology, Industry 4.0 enables to collect and analyze real-time data about Cyber Physical System (CPS) components and hence to detect and react to emergent disruptive situations as quick as possible. In such context, tasks rescheduling becomes a crucial research topic, which aims to revise the initial schedule in cost-effective way. In this paper, we focus on system disruption related to resources unavailability of a resource, or when it is in an unexpected location. We propose a new tasks rescheduling module based on a reference schedule generated by an Initial Planning and Scheduling system (IPS). Our module considers the main schedule objective and aims to assign tasks to the nearest resources while improving the execution accuracy. To do so, we formulate an optimization problem of tasks rescheduling, before solving it using the meta-heuristic Tabu-search. The experimental results show the efficiency of our module to optimize the tasks rescheduling when considering both localization and accuracy information, in addition to the ability of Tabu-Search algorithm finding an optimal solution.