Abstract
The peak power of the manufacturing systems can increase electricity costs and reduce the use of renewable energy suppliers. The power of the machining processes depends on the processing time of the operations. Then, the allocation of the power to the machines of a manufacturing system controls the processing time of the manufacturing operations. An efficient allocation model can reduce the peak power, keeping the throughput performance level. This paper proposes a game theory to allocate the power to the machines including the dependence of the processing time from the power allocated. The game model uses the Gale-Shapley algorithm that forms couples of under and overloaded machines. Then, each couple exchanges the power from the underloaded to overloaded machines. The model considers the global workload and the jobs in queue for each machine. A simulation model tests the proposed method compared to a benchmark where each machine works with fixed power. The simulation results show how the model can improve the performance of the manufacturing system in several conditions tested. In particular, the main benefits can be obtained when the manufacturing system has high or medium utilization or the uncertainty affects the processing time.
Highlights
The new legislation, national reduction targets and cost pressure drive the production companies to improve the energy efficiency [1].Recent international research denoted an increment of the world industrial energy consumption by 3.4% annually until 2040 [2]
This paper proposes the real-time control of the energy consumption of flexible manufacturing systems
The second research question regards the possibility to reduce the peak power keeping the same performance of the manufacturing system: RQ2: Can the peak power load reduce without negative effect on the performance of the manufacturing system?
Summary
The new legislation, national reduction targets and cost pressure drive the production companies to improve the energy efficiency [1]. One field of the research concerns the reduction in energy consumption in manufacturing systems considering the switch-off of the machines [6,7,8]. The off-line and on-line approaches can support the control and reduction in the peak power in manufacturing systems. The computational complexity of these models limits the potential application of real-time control in manufacturing systems when the number of machines increases. Open innovation is defined as follows: “open innovation is a paradigm that assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as the firms look to advance their technology [16]” These infrastructures are necessary to manage complex manufacturing systems and develop algorithms for improving flexibility and saving costs. This paper proposes the real-time control of the energy consumption of flexible manufacturing systems.
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