Simulation based evaluation and optimization for energy consumption of a typical welding shop

Manufacturing facility's energy consumption depends on series of factors such as machines and equipment used for production, manufacturing procedures, building design, outside weather conditions and indoor environmental requirements, where many of them are stochastic in nature, thus makes energy consumption optimization of manufacturing facility a very difficult and challenging problem. It is almost impossible to solve the problem via an analytical study, while experimental studies usually are very costly and time consuming. To overcome these difficulties, this paper proposes a simulation based approach to evaluate and optimize the energy consumption of a manufacturing facility, and we use a typical welding shop as an example. To achieve long term low energy consumption, we face a two-level optimization problem: the building design and daily production scheduling. In the proposed approach, we use EnergyPlus for integrated energy usage estimation and we apply Ordinal Optimization (OO) method and Genetic Algorithms (GA) for optimization of building design and production scheduling, respectively. Numerical examples and results are provided. The optimization concept and the modeling framework could be used for manufacturing facility design and production scheduling to minimize the total energy consumption while maintaining production throughput.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[3]  Ernst Worrell,et al.  Energy efficiency improvement and cost saving opportunities for the Corn Wet Milling Industry: An ENERGY STAR Guide for Energy and Plant Managers , 2003 .

[4]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[5]  R. H. Smith Optimization for Simulation : Theory vs . Practice , 2002 .

[6]  Hao Liu,et al.  Review and some progresses on energy consumption models of a class of production lines , 2011, 2011 9th World Congress on Intelligent Control and Automation.

[7]  Michael C. Fu,et al.  Feature Article: Optimization for simulation: Theory vs. Practice , 2002, INFORMS J. Comput..

[8]  K. F. Fong,et al.  HVAC system optimization for energy management by evolutionary programming , 2006 .

[9]  Jiang Hua-sheng Energy Assessment of Indoor Ventilation System , 2008 .

[10]  Y. Ho,et al.  Ordinal Optimization: Soft Optimization for Hard Problems , 2007 .

[11]  Christoph Weber,et al.  Energy Efficiency in Innovative Industries: Application and Benefits of Energy Indicators in the Automobile Industry , 2009 .

[12]  Loo Hay Lee,et al.  Stochastic Simulation Optimization - An Optimal Computing Budget Allocation , 2010, System Engineering and Operations Research.

[13]  C. Galitsky,et al.  Energy Efficiency Improvement and Cost Saving Opportunities for the Vehicle Assembly Industry , 2008 .

[14]  N. Athi,et al.  Energy reduction for the spot welding process in the automotive industry , 2007 .