Multi-objective optimization of lean and resource efficient manufacturing systems

In the manufacturing industry, target-oriented and efficient use of resources is gaining importance, alongside economic optimization. The economic and organizational optimization of manufacturing systems according to the lean principles is only partly compatible with the goals of resource-efficient manufacturing. Therefore, an approach is sought to improve individual analyses of manufacturing systems. This paper proposes an approach for the multi-objective optimization of lean and resource-efficient manufacturing systems. To predict the dynamic effects of several configurations of manufacturing systems, material, energy, and information flows of a discrete event simulation are coupled with an assessment model, based on objectives of lean and resource-efficient manufacturing. Using design of experiments, Gaussian process meta-models are computed for the behavior of the simulation model. These meta-models allow the approximation of the system behavior to be computed in a short period of time and enable extensive multi-objective optimization and more adequate decision-making support systems. The proposed approach is tested in the metalworking industry.

[1]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[2]  Gisela Lanza,et al.  Simulation-based Methodology for the Application of Lean and Green Strategies Depending on External Change Driver Influence☆ , 2016 .

[3]  Konstantinos Salonitis,et al.  Forty Sixth CIRP Conference on Manufacturing Systems 2013 Energy efficient manufacturing from machine tools to manufacturing systems , 2013 .

[4]  Ben Richard Hughes,et al.  A review of energy simulation tools for the manufacturing sector , 2018 .

[5]  Timothy J. Larson,et al.  Perfect complements: Synergies between lean production and eco‐sustainability initiatives , 2004 .

[6]  Paul Young,et al.  Supply Chain Analysis Using Simulation, Gaussian Process Modelling and Optimisation , 2013 .

[7]  Kim Hua Tan,et al.  Green as the new Lean: how to use Lean practices as a catalyst to greening your supply chain , 2013 .

[8]  Paul R. McCright,et al.  Are Lean and Green Programs Synergistic , 2009 .

[9]  Jon C. Yingling,et al.  Quantifying benefits of conversion to lean manufacturing with discrete event simulation: A case study , 2000 .

[10]  Michael J. Lenox,et al.  LEAN AND GREEN? AN EMPIRICAL EXAMINATION OF THE RELATIONSHIP BETWEEN LEAN PRODUCTION AND ENVIRONMENTAL PERFORMANCE , 2001 .

[11]  Paul Schönsleben,et al.  A simulation-based decision support for eco-efficiency improvements in production systems , 2015 .

[12]  Holger Haag.  Eine Methodik zur modellbasierten Planung und Bewertung der Energieeffizienz in der Produktion , 2013 .

[13]  Jack P. C. Kleijnen,et al.  Kriging for interpolation in random simulation , 2003, J. Oper. Res. Soc..

[14]  Christoph Herrmann,et al.  Integrated Analysis of Energy, Material and Time Flows in Manufacturing Systems , 2016 .

[15]  Sebastian Thiede,et al.  Energy Efficiency in Manufacturing Systems , 2012 .

[16]  Leonardo Rivera,et al.  Measuring the impact of Lean tools on the cost-time investment of a product using cost-time profiles , 2007 .

[17]  Annabel Gabriele Jondral.  Simulationsgestützte Optimierung und Wirtschaftlichkeitsbewertung des Lean-Methodeneinsatzes , 2013 .

[18]  J. Gero,et al.  REDUCING THE PARETO OPTIMAL SET IN MULTICRITERIA OPTIMIZATION(With Applications to Pareto Optimal Dynamic Programming) , 1985 .

[19]  José Arnaldo Barra Montevechi,et al.  Application of design of experiments on the simulation of a process in automotive industry , 2007, 2007 Winter Simulation Conference.

[20]  Joel N. Morse,et al.  Reducing the size of the nondominated set: Pruning by clustering , 1980, Comput. Oper. Res..

[21]  Michael Zwicky Hauschild,et al.  Drivers and barriers for implementation of environmental strategies in manufacturing companies , 2013 .

[22]  C. Herrmann,et al.  An environmental perspective on Lean production , 2008 .

[23]  Richard Florida,et al.  Lean and Green: The Move to Environmentally Conscious Manufacturing , 1996 .

[24]  Klaus Erlach,et al.  Die CO2-Wertstrom-Methode zur Steigerung von Energie- und Materialeffizienz in der Produktion , 2014 .

[25]  G. Derringer,et al.  Simultaneous Optimization of Several Response Variables , 1980 .

[26]  Egon Müller,et al.  Energieeffiziente Fabriken planen und betreiben , 2009 .

[27]  Stefan Wrobel,et al.  A review of machine learning for the optimization of production processes , 2019, The International Journal of Advanced Manufacturing Technology.

[28]  David Dornfeld,et al.  Green Manufacturing and Sustainable Manufacturing Partnership Title Assessment of Lean and Green Strategies by Simulation of Manufacturing Systems in Discrete Production Environments , 2013 .

[29]  Christoph Herrmann,et al.  Sustainability Cockpit: An integrated tool for continuous assessment and improvement of sustainability in manufacturing , 2016 .

[30]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[31]  Jerry Banks,et al.  Discrete Event Simulation , 2002, Encyclopedia of Information Systems.

[32]  Jayant Rajgopal,et al.  Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study , 2007 .

[33]  Björn Johansson,et al.  Environmental aspects in manufacturing system modelling and simulation—State of the art and research perspectives , 2013 .

[34]  Douglas C. Montgomery,et al.  An empirical study of the prediction performance of space-filling designs , 2011 .

[35]  A. OHagan,et al.  Bayesian analysis of computer code outputs: A tutorial , 2006, Reliab. Eng. Syst. Saf..