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

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.