Multi-objective optimization of facility planning for energy intensive companies
Because of the energy shortage and energy price rise, energy efficiency becomes a worldwide hot spot problem. It is not only a problem about cost reduction, but also a great contribute to the environmental protection. However, the energy efficiency was always ignored in the past decades. In order to gain more benefit and become more competitive in the market, energy efficiency should be considered as an essential factor in early planning phase. To overcome these problems, a new approach, which introduces energy efficiency as a key criterion into the planning process, is presented in this article. An energy recovery network is built according to the analysis of process and product demands. Afterwards the energy loss of the whole system, transport performance and space demand are simultaneously taken into account with the purpose of finding good facility planning from both energy and economic aspects. Finally, a practical expanding case is used to validate the correctness and effectiveness of the proposed approach.
KeywordsEnergy efficiency Facility planning Multi objective optimization Local search
Unable to display preview. Download preview PDF.
- Abyaneh, S. H., Zandieh, M. (2011) Bi-objective hybrid flow shop scheduling with sequence-dependent setup times and limited buffers. The International Journal of Advanced Manufacturing Technology. doi: 10.1007/s0017001133685.
- Bandyopadhyay, S.,& Bhattacharya, R. (2011). Applying modified NSGA-II for bi-objective supply chain problem. Journal of Intelligent Manufacturing. doi: 10.1007/s1084501106172.
- Cai, M., & Kagawa, T. (2001). Design and application of air power meter in compressed air systems. In Second international symposium on environmentally conscious design and inverse manufacturing (pp. 208–212). 2001 Japan, Dec 11–15.Google Scholar
- Cheshmehgaz, H. R., Desa, M. I., & Wibowo, A. (2011). A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm. Journal of Intelligent Manufacturing. doi: 10.1007/s1084501105847.
- Duzanec, D., & Kovacic, Z. (2009). Performance analysis-based GA parameter selection and increase of μGA accuracy by gradual contraction of solution space. In IEEE International Conference on Industrial Technology. Gippsland, Australia, February 10–13. doi: 10.1109/ICIT.2009.4939483.
- Dwijayanti, K.,& Dawal, S. Z. (2010). A Proposed study on facility planning and design in manufacturing process. In Proceeding of the international multiconference of engineers and computer scientists 2010 (pp. 1640–1645). Hong Kong, March 17–19.Google Scholar
- Energy agent NRW. (2011). Energiekosten und leckagen: Das Druckluftnetz- ein Energiefresser im Betrieb. http://www.energieagentur.nrw.de/_database/_data/datainfopool/DruckluftBroschuere.pdf. Accessed 25 July 2011.
- Geldermann J., Treitz M., Schollenberger H., Ludwig J., Rentz O. (2007) Integrated process design for the inter-company plant layout planning of dynamic mass flow networks. Universitätsverlag Karlsruhe, KalrsruheGoogle Scholar
- Government of Alberta Energy. (2011). Alberta gas reference price History. http://www.energy.alberta.ca/NaturalGas/1322.asp. Accessed 21 July 2011.
- Ishibuchi, H., & Narukawa, K. (2004). Performance evaluation of simple multiobjective genetic local search algorithms on multiobjective 0/1 knapsack problems. In IEEE Congress on evolutionary computation (pp. 441–448). Portland, USA, June 19–23.Google Scholar
- Kemp I. C. (2007) Pinch analysis and process integration: A user guide on process integration for the efficient use of energy. Butterworth-Heinemann, OxfordGoogle Scholar
- Krenn, C., Fresner, J., & Meixner, E. (2008). Energieeffizienzsteigerung in Unternehmen der stahlverarbeitenden Industrie durch Abwärmenutzung im Niedertemperaturbereich. In: The 10. Symposium Energieinnovation. Austria: Graz.Google Scholar
- Van Veldhuizen, D. A., & Lamont, G. B. (1998). Evolutionary computation and convergence to a pareto front. In Proceedings of the 1998 Genetic Programming Conference (pp. 221–228). CA: Stanford University.Google Scholar
- Wang D. Y., Wang L. Y. (2005) The application of genetic algorithms in facility layout. Computer Engineering and Applications 14: 190–192Google Scholar
- Wu, G. Z., Li, D. Y., Qi, H. B., & Li, D. (2010). Analysis on thermal calculation model and influencing factors of steam pipeline. In 2010 International conference on intelligent system design and engineering application (pp. 314–317). Chang Sha, China.Google Scholar
- Yalcinoz, T., Altun, H., & Uzam, M. (2001) Economic dispatch solution using a genetic algorithm based on arithmetic crossover. In 2001 IEEE porto power tech conference. doi: 10.1109/PTC.2001.964734.
- Ying S. L., Pu Z. Y., Jie W. Y. (2006) Analysis and improve on system lose to process of compressed air transport. Coal Mine Machinery 27: 163–165Google Scholar