Abstract
The concept and mathematical models developed in the previous chapter have been implemented into a working software prototype to evaluate its applicability, usability, and contribution towards integration of variable electricity supply into manufacturing systems. Further, the prototype resembles a universally applicable, computerized calculation environment to support application according to proposed application cycle. The chapter starts with a brief discussion on implementation options and their potential applicability to develop a prototype. Simulation is chosen as a suitable method for implementation, and consequently additional background on simulation methods and validation is provided. The next section describes the developed software prototype, including its functionality, modules, inputs, and outputs as well as validation examples. The chapter closes with an intermediate conclusion.
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References
Afazov, S. M. (2013). Modelling and simulation of manufacturing process chains. CIRP Journal of Manufacturing Science and Technology, 6(1), 70–77. http://dx.doi.org/10.1016/j.cirpj.2012.10.005.
Balci, O. (1998a). Verification, validation, and accreditation. In D. J. Medeiros, E. F. Watson, J. Carson & M. S. Manivannan (Eds.), Proceedings of the 1998 winter simulation conference. Washington, DC.
Balci, O. (1998b). Handbook of simulation, verification, validation and testing (pp. 335–393). New York: Wiley.
Banerjee, S. (2014). Mathematical modeling: Models analysis and applications. Boca Raton: Taylor and Francis.
Banks, J. (1998). Principles of simulation. Handbook of simulation: Principles, methodology, advances, applications, and practice (pp. 3–30). New York: Wiley.
Chung, C. A. (2004). Simulation modeling handbook: A practical approach. Boca Raton: CRC Press.
Du, S., Xu, R., Huang, D., & Yao, X. (2015). Markov modeling and analysis of multi-stage manufacturing systems with remote quality information feedback. Computers & Industrial Engineering, 88, 13–25. http://linkinghub.elsevier.com/retrieve/pii/S0360835215002715.
Fiedler, T., Ott, S., & Metz, D. (2007). Künstliche Neuronale Netze (KNN) zur Verbrauchsprognose im Strom- und Gasbereich. Querschnitt, 21, 135–138.
Herrmann, C., Thiede, S., Kara, S., & Hesselbach, J. (2011). Energy oriented simulation of manufacturing systems - Concept and application. CIRP Annals - Manufacturing Technology, 60(1), 45–48. http://linkinghub.elsevier.com/retrieve/pii/S0007850611001284.
Hesselbach, J. (2012). Energie- und klimaeffiziente Produktion: Grundlagen, Leitlinien und Praxisbeispiele. Wiesbaden: Springer Vieweg.
Kuhn, A., Reinhardt, A., & Wiendahl, H.-P. (1993). Handbuch Simulationsanwendungen in Produktion und Logistik. Wiesbaden: Springer Fachmedien.
Li, L., & Sun, Z. (2013). Dynamic energy control for energy efficiency improvement of sustainable manufacturing systems using Markov decision process. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(5), 1195–1205. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6519950.
Li, L., Chang, Q., Ni, J., Xiao, G., & Biller, S. (2007). Bottleneck detection of manufacturing systems using data driven method. In IEEE International Symposium on Assembly and Manufacturing (pp. 76–81). Ann Arbor, MI. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4288452.
Li, L., Sun, Z., & Tang, Z. (2012a). Real time electricity demand response for sustainable manufacturing systems: Challenges and a case study. In 8th IEEE International Conference on Automation, Science and Engineering (pp. 353–357). Seoul. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6386437.
Li, L., Sun, Z., Yang, H., & Gu, F. (2012b). Simulation-based energy efficiency improvement for sustainable manufacturing systems. Proceedings of the ASME 2012 International Manufacturing Science and Engineering Conference MSEC2012 (pp. 1033–1039). IN: Notre Dame.
Liraviasl, K. K., Elmaraghy, H., Hanafy, M., & Samy, S. N. (2015). A framework for modelling reconfigurable manufacturing systems using hybridized discrete-event and agent-based simulation. IFAC-PapersOnLine, 48(3), 1535–1540. http://dx.doi.org/10.1016/j.ifacol.2015.06.297.
Mattern, F., & Mehl, H. (1989). Diskrete Simulation-Prinzipien und Probleme der Effizienzsteigerung durch Parallelisierung. Informatik-Spektrum, 12(4), 198–210.
Negahban, A., & Smith, J. S. (2014). Simulation for manufacturing system design and operation: Literature review and analysis. Journal of Manufacturing Systems, 33(2), 241–261. http://dx.doi.org/10.1016/j.jmsy.2013.12.007.
ProModel. (2015). ProModel. Retrieved October 22, 2015, from https://www.promodel.com/Products/ProModel.
Rabe, M., Spieckermann, S., & Wenzel, S. (2008). Verifikation und Validierung für die Simulation in Produktion und Logistik: Vorgehensmodelle und Techniken. Heidelberg: Springer.
Schönemann, M., Herrmann, C., Greschke, P., & Thiede, S. (2015). Simulation of matrix-structured manufacturing systems. Journal of Manufacturing Systems, 37(1), 104–112. http://dx.doi.org/10.1016/j.jmsy.2015.09.002.
Siemens. (2015). Logistics and Material Flow Simulation. Retrieved October 22, 2015, from http://www.plm.automation.siemens.com/en_us/products/tecnomatix/manufacturing-simulation/material-flow/index.shtml.
Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. Boston: The McGraw-Hill Companies Inc.
Sun, Z., & Li, L. (2014). Potential capability estimation for real time electricity demand response of sustainable manufacturing systems using Markov decision process. Journal of Cleaner Production, 65, 184–193. http://linkinghub.elsevier.com/retrieve/pii/S0959652613005738.
Tako, A. A., & Robinson, S. (2012). The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision Support Systems, 52(4), 802–815. http://dx.doi.org/10.1016/j.dss.2011.11.015.
The Anylogic Company. (2015). Anylogic. Retrieved October 22, 2015, from http://www.anylogic.com/.
Thiede, S. (2012). Energy efficiency in manufacturing systems. Heidelberg: Springer.
Thiede, S., Seow, Y., Andersson, J., & Johansson, B. (2013). Environmental aspects in manufacturing system modelling and simulation-state of the art and research perspectives. CIRP Journal of Manufacturing Science and Technology, 6(1), 78–87.
Yamin, H. Y., Shahidehpour, S. M., & Li, Z. (2004). Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets. International Journal of Electrical Power & Energy Systems, 26(8), 571–581.
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Beier, J. (2017). Prototypical Implementation. In: Simulation Approach Towards Energy Flexible Manufacturing Systems. Sustainable Production, Life Cycle Engineering and Management. Springer, Cham. https://doi.org/10.1007/978-3-319-46639-2_5
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DOI: https://doi.org/10.1007/978-3-319-46639-2_5
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