OPTIMISED is developing and demonstrating a manufacturing scheduling optimisation system, applied to three industrial businesses in manufacturing and maintenance repair and overhaul (MRO) which uses sensor technology, simulation in the form of a digital twin, data analytics and artificial intelligence techniques to monitor, react to and improve manufacturing performance.
Productivity and sustainability is important for European society to ensure that manufacturing remains a cost-effective and competitive industry and employment sector.
OPTIMISED Vision
Developing methods and tools for deployment of highly optimised and reactive planning systems is our vision. This can be done using factory modelling and simulation based on empirical data. The data is captured using smart sensors as well as pro-active human-machine interfaces.
The OPTIMISED vision has been achieved by developing systems which are able to Monitor system performance through an integrated sensor network, automatically detecting bottlenecks, faults and performance drop-off. Continuously evolve (through optimisation) to respond to disruptive events, supply chain disruptions and non-quality issues through factory simulation modelling. Improve understanding and monitoring of energy demand curve and energy usage per industrial process and globally improve efficiency of production line through reduced energy waste. Understand potential benefits, added value and impacts of participating in Demand Side Response (DSR) processes and becoming an active player in the changing energy industry, instead of remaining a conventional passive element that simply acquires a service from energy providers.
Energy is the future
The impact of energy management on factory planning and optimisation is specifically assessed in the project. Reducing energy waste on one side, while understanding how energy is used in detail on the other side, allows future factories to reschedule production according to desired energy consumption. This is especially beneficial to energy providers, who seek to balance energy demand.
The research has been guided by the following specific scientific and technological objectives:
• Developing methods for real-time system awareness using integrated sensor networks
• Developing robust scheduling optimisation of the entire assembly line as well as of individual production units and operative resources
• Developing a multi-level scheduling simulation to address the multiple timescales that impact factory operations
• Developing systems with the ability to respond to changes in offsite power supply by reducing factory energy demands to agreed capped levels.
• Investigate and assess the impact on factory operations of energy demand management
• Designing advanced data gathering and distributed control infrastructures that integrate with an information management backbone
• Designing methodologies for assembly requirements and capabilities modelling and system and station behaviour simulation
• Designing smart human-machine interfaces that pro-actively support the user throughout factory operations
