Abstract
This paper concerns the development of a design methodology and its demonstration through a prototype system for performance modeling and optimization of manufacturing processes. The design methodology uses a Modelica simulation tool serving as the graphical user interface for manufacturing domain users such as process engineers to formulate their problems. The Process Analytics Formalism, developed at the National Institute of Standards and Technology, serves as a bridge between the Modelica classes and a commercial optimization solver. The prototype system includes (1) manufacturing model components’ libraries created by using Modelica and the Process Analytics Formalism, and (2) a translator of the Modelica classes to Process Analytics Formalism, which are then compiled to mathematical programming models and solved using an optimization solver. This paper provides an experiment toward the goal of enabling manufacturing users to intuitively formulate process performance models, solve problems using optimization-based methods, and automatically get actionable recommendations.
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References
Åkesson, J. (2008). Optimica—An extension of modelica supporting dynamic optimization. https://www.modelica.org/events/modelica2008/Proceedings/sessions/session1b3.pdf, pp. 57–66.
Alrazgan, A., & Brodsky, A. (2014). Toward reusable models: System development for optimization analytics language (OAL). Department of Computer Science, George Mason University, Fairfax, VA, 22030, Tech. Rep. GMU-CS-TR-2014-4, 2014. [Online]. http://cs.gmu.edu/tr-admin/papers/GMU-CS-TR-2014-4.pdf. Accessed October 2015.
AnyLogic. (2014). Multimethod simulation software. http://www.anylogic.com/. Accessed October 2015.
Brodsky, A., & Nash, H. (2005). CoJava: A unified language for simulation and optimization. In The conference on object oriented programming systems languages and applications, pp. 194–195.
Brodsky, A., Egge, N., & Wang, X. (2012). Supporting agile organizations with a decision guidance query language. Journal of Management Information Systems, 28(4):39–68.
Brodsky, A., Shao, G., & Riddick, F. (2014). Process analytics formalism for decision guidance in sustainable manufacturing. Journal of Intelligent Manufacturing. doi:10.1007/s10845-014-0892-9.
CPLEX. (2014). http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/. Accessed July 2015.
Dietl, K., Yances, S. G., Johnsson, A., J. Åkesson, Link, K., & Velut, S. (2014). Industrial application of optimization with modelica and optimica uing intelligent Python scripting. In Proceedings of the 10th international modelica conference, pp. 778–786, Lund, Sweden.
Fritzon, P. (2011). Introduction to modeling and simulation of technical and physical systems with modelica (1st ed.). New York: Wiley-IEEE Press.
Gecode. (2015). Generic constraint development environment. http://www.gecode.org. Accessed October 2015.
Gurobi. (2015). An easier way to make better decisions—The state-of-the-art mathematical programming solver. http://www.gurobi.com/ Accessed October 2015.
IBM. (2014). Modeling with OPL. http://www-01.ibm.com/software/commerce/optimization/modeling/., Accessed October 2015.
Jacinto, J. (2015). Smart manufacturing? Industry 4.0? What’s it all about?. http://www.totallyintegratedautomation.com/2014/07/smart-manufacturing-industry-4-0-whats/. Accessed October 2015.
Jacop. (2015). JaCoP—Java Constraint Programming solver. http://jacop.osolpro.com/. Accessed October 2015.
Klemmt, A., Horn, S., Weigert, G., & Wolter, K.-J. (2009). Simulation-based optimization vs. mathematical programming: A hybrid approach for optimizing scheduling problems. Robotics and Computer-Integrated Manufacturing, 25(6):917–925.
Kumar, A., Veeranna, V., Durgaprasad, B., & Sarma, B. (2013). A MATLAB GUI tool for optimization of fms scheduling using conventional and evolutionary approach. International Journal of Current Engineering and Technology, 3(5):1739–1744.
Law, A., & Kelton, W. (2000). Simulation modeling and analysis. Boston: McGraw-Hill Higher Education.
MathWorks. (2014). Simulation and model-based design. http://www.mathworks.com/products/simulink/. Accessed October 2015.
McLean, C., & Shao, G. (2001). Simulation of shipbuilding operations. In Proceedings of the 2001 winter simulation conference, (pp. 870–876). Piscataway, NJ: Institute of Electrical and Electronics Engineers.
Modelica Association. (2012). Modelica \(\textregistered \) a unified object-oriented language for systems modeling language specification. https://www.modelica.org/documents/ModelicaSpec33.pdf. Accessed July 2014.
Modelica. (2014). Modelica and the Modelica Association . https://www.modelica.org/. Accessed October 2015.
NIST. (2014). Smart manufacturing program. http://www.nist.gov/el/msid/syseng/. Accessed July 2014.
OpenModelica. (2014). OpenModelica. https://www.openmodelica.org/. Accessed October 2015.
Optimizer. (2015). WITNESS optimizer. http://www.lanner.com/en/media/witness/optimiser.cfm. Accessed October 2015.
OptTek. (2015). Opt quest—Simulation optimization. http://www.opttek.com/OptQuest. Accessed October 2015.
Pathak, S. D., & Dilts, D. M. (2002). Simulation of supply chain networks using complex adaptive system theory. In IEEE international on engineering management conference, IEMC ’02, Vol. 2, pp. 655–660.
ProModel. (2015). ProModel—Better decisions—Faster. https://www.promodel.com/. Accessed October 2015.
Rockwell Automation. (2014). ARENA simulation software. https://www.arenasimulation.com/. Accessed October 2015.
Rockwell Automation. (2014). Capabilities: Sustainable production. http://www.rockwellautomation.com/solutions-services/capabilities/sustainable-production/overview.page. Accessed October 2015.
Shao, G. A. (2014). Decision guidance methodology for sustainable manufacturing using process analytics formalism. Journal of Intelligent Manufacturing. doi:10.1007/s10845-014-0995-3.
SimRunner. (2015). SimRunner user guide. ftp://www.web.ith.mx/pub/PROMODEL/Docs/SimRunner.pdf. Accessed October 2015.
SIMUL8. (2015). Process simulation software. http://www.simul8.com/. Accessed October 2015.
SMLC. (2011). Implementing 21st century smart manufacturing. https://smart-process-manufacturing.ucla.edu/about/news/Smart%20Manufacturing%206_24_11.pdf. Accessed October 2015.
Witness. (2015). Witness—The predictive simulation platform for business modelers. http://www.lanner.com/en/witness/. Accessed October 2015.
WolFram. (2014). Computation meets knowledge. http://www.wolfram.com/system-modeler/. Accessed October 2015.
Acknowledgments
The authors thank Abdullah Alrazgan, a graduate student from George Mason University, for his effort on SPAF compiler development. The work represented here was partially funded through cooperative agreement #70NANB12H277 between George Mason University and NIST.
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Shao, G., Brodsky, A. & Miller, R. Modeling and optimization of manufacturing process performance using Modelica graphical representation and process analytics formalism. J Intell Manuf 29, 1287–1301 (2018). https://doi.org/10.1007/s10845-015-1178-6
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DOI: https://doi.org/10.1007/s10845-015-1178-6