Discrete Event Simulation of Biological Control Processes and its Application to Autonomous Decision-Making in Manufacturing Systems |
Relevance to Beneficiaries
The research has potential benefits for:
- Operations Managers and Manufacturing System Researchers/Engineers who develop design, plan and control complex operations and manufacturing systems. Here the benefits to industry of a feasibility study that could lead to an autonomous operations control system would be significant reductions in inventory costs, improved material flow synchronisation leading to reduced lead times, and improved use of processing capacity leading to increased throughput. In addition, such integral design, planning and control systems would be better able to deal effectively with the increasing levels of product and process variability being experienced by manufacturers. Such a control system would enable autonomous planning decisions to be made in terms of order schedules, capacity plans, part sequencing, master production scheduling, distribution requirements scheduling, lot sizing, process batch sizing, procurement batch sizing, kanban sizing, setting reorder levels, work loading, line balancing, and buffer and kanban placements. In addition, the control processes involved in managing supply chain operations would benefit.
The above benefits would depend on being able to undertake practical implementation of biological inspired control processes that are significantly more complex then current traditional methods. During this feasibility study the issues surrounding implementation will be explored during General Meetings particularly in relation to the use of shopfloor visual management and data collection technologies to ensure responsive signal-information flows within the manufacturing system. - Systems Biologists who will be provided with a feasibility study for integrating their existing modelling and simulation (M&S) functionality to provide integration of the range of biological M&S levels needed to ensure that the significance to cell control functionality of individual reactions can be identified. This study will also contribute to the specification of the elements that should be represented in the interface of a biochemical reaction network model, and therefore to the development of a general model composition schema (currently a hotly debated issue within the SBML community).
The successful achievement of Main Aim 2 (MA2) will provide the knowledge and focus for more detailed development of DES as a key M&S tool for Systems Biologists.