Manufacturing Energy Models based on Probabilistic Approaches

Open Access
Jeon, Hyun Woo
Graduate Program:
Industrial Engineering
Doctor of Philosophy
Document Type:
Date of Defense:
May 07, 2015
Committee Members:
  • Vittaldas V Prabhu, Dissertation Advisor
  • Vinayak V Shanbhag, Committee Member
  • Guodong Pang, Committee Member
  • Hosam Kadry Fathy, Committee Member
  • manufacturing energy
  • queueing energy models
  • probabilistic manufacturing energy
Many managerial decisions impact the energy consumption of discrete manufacturing firms. Since an energy amount to be consumed in manufacturing systems is closely connected to energy costs and environmental consequences, these managerial decisions can have long-lasting effects. Hence, making informed decisions with the aid of energy estimation tools is important to manufacturing firms. Estimating energy consumption in manufacturing is not, however, straightforward. There are a number of different manufacturing processes, and the energy consumption of each process is dependent on many operational parameters. Thus, for a better manufacturing energy analysis, the power profiles need to be collected and analyzed from real manufacturing machines, and various methods including analytical and simulation approaches should be proposed and tested based on the collected data. Furthermore, since many previous studies are focusing mainly on mean power demands for evaluating energy consumption, variability of manufacturing power demands also has to be investigated for exploring how the uncertainty impacts on manufacturing energy. In order to address the issues, this dissertation proposes various but useful methods. At the beginning, this study shows an analytical manufacturing energy model based on queueing network theory. Considering Markovian and Non-Markovian assumptions, we present the manufacturing energy consumption in a closed form equation. Then, our analysis develops the previous model further for energy efficiency benchmarking. Comparing the manufacturing energy in a hypothetical system with peers in the U.S., the proposed model shows how to assess the energy efficiency in a manufacturing plant based on the simulation and stochastic frontier analysis approaches. After the energy estimation and energy efficiency assessment are discussed, our study transcends the previous studies by considering uncertainty and variability on the manufacturing electrical demands. Our approach presents the benefits of considering uncertainty in the manufacturing power demands, and proposes a systemic method to estimate the mean and uncertainty by applying probabilistic techniques. At each discussion, the proposed method is validated and verified in a suitable manner, and the accuracy of the proposed method is also checked in detail.