Peak Electricity Demand Control of Manufacturing Systems by Gale-Shapley Algorithm with Discussion on Open Innovation Engineering

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.

Enhancement Of Gale-Shapley algorithm with imbalanced sets for hiring and job finding applications

“…The researchers investigated a method for determining the instability of unequal sets by measuring the average energies of men and women in the three different equality and inequality cases (equal men and women, number of men is greater than number of women, and number of women is greater than number of men) and came up with the results shown [19], [25] in Figure 1.…”

Capacity and resource allocation in flexible production networks by a game theory model

“…The resource allocation/ sharing resources can be solved as a stable matching problem. Several studies have proposed the Gale-Shapley algorithm to share resources in cloud manufacturing [19], capacity sharing in a network of enterprises [5], and power allocation to machines of a manufacturing system with a peak power constraint [24].…”

Workload Control Order Release With Controllable Processing Time Policies: An Assessment by Simulation

“…This approach can be related to several situations where limited resources are shared among the machines as the power that have to respect the peak power constraints, tools, or worker allocation. One example is described in Renna [37] where some machines can reduce the processing time by increasing the power and others increase the processing time by reducing the power under the peak power constraint; allocation of operations and their tools with controllable processing time to minimize the processing costs [38].…”