Analysing the impact of rescheduling time in hybrid manufacturing control

Hybrid manufacturing control architectures merge the benefits of hierarchical and heterarchical approaches. Disturbances can be handled at upper or lower decision levels, depending on the type of disturbance, its impact and the time the control system has to react. This paper focuses particularly on a disturbance handling mechanism at upper decision levels using a rescheduling manufacturing method. Such rescheduling is more complex that the offline scheduling since the control system must take into account the current system status, obtain a satisfactory performance under the new conditions, and also come up with a new schedule in a restricted amount of time. Then, this paper proposes a simple and generic rescheduling method which, based on the satisfying principle, analyses the trade-off between the rescheduling time and the performance achieved after a perturbation. The proposed approach is validated on a simulation model of a realistic assembly cell and results demonstrate that adaptation of the rescheduling time might be beneficial in terms of overall performance and reactivity.info:eu-repo/semantics/publishedVersio

Design of a self-learning multi-agent framework for the adaptation of modular production systems

YesThis paper presents the design of a multi-agent framework that aids engineers in the adaptation of modular production systems. The framework includes general implementations of agents and other software components for self-learning and adaptation, sensor data analysis, system modelling and simulation, as well as human-computer interaction. During an adaptation process, operators make changes to the production system, in order to increase capacity or manufacture a product variant. These changes are automatically captured and evaluated by the framework, building an experience base of adjustments that is then used to infer adaptation knowledge. The architecture of the framework consists of agents divided in two layers: the agents in the lower layer are associated with individual production modules, whereas the agents in the higher layer are associated with the entire production line. Modelling, learning, and adaptations can be performed at both levels, using a semantic model to specify the structure and capabilities of the production system. An evaluation of a prototype implementation has been conducted on an industrial assembly system. The results indicate that the use of the framework in a typical adaptation process provides a significant reduction in time and resources required.This work was supported in part by the European Commission [grant agreement n. 314762]