Dynamic Energy Control for Energy Efficiency Improvement of Sustainable Manufacturing Systems Using Markov Decision Process

Greenhouse gas emissions and global warming have become vital problems to human society. About 40% of the carbon dioxide is emitted from electric power generation in the United States. Due to the lack of consideration in the system design and the lack of a real time systematic management method for energy consumption, the energy efficiency of industrial manufacturing systems is extremely low. Most of the existing research work related to energy efficiency improvement only focuses on a single-machine manufacturing system while little work has been done to achieve the optimal energy efficiency for a typical system with multiple machines and buffers. In this paper, an analytical model is developed to establish a systems (or holistic) view of energy efficiency in typical manufacturing systems with multiple machines and buffers that dynamically control energy consumption considering both energy states and production constraints. The complex interaction between the adopted energy control decisions and system state evolutions are modeled by Markov Decision Process. An approximate algorithm for the real time application is introduced to find a near-optimal solution. A numerical case study on a section of an assembly line is used to illustrate the effectiveness of the proposed approach.

[1]  Stephan Biller,et al.  Energy Consumption Reduction for Sustainable Manufacturing Systems Considering Machines With Multiple-Power States , 2011 .

[2]  Alexander Verl,et al.  A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing , 2009 .

[3]  Sami Kara,et al.  An empirical model for predicting energy consumption of manufacturing processes: a case of turning process , 2011 .

[4]  Eberhard Abele,et al.  Analyzing Energy Consumption of Machine Tool Spindle Units and Identification of Potential for Improvements of Efficiency , 2011 .

[5]  Yazhou Jia,et al.  Distribution of time between failures of machining center based on type I censored data , 2003, Reliab. Eng. Syst. Saf..

[6]  Jun Ni,et al.  Condition-Based Maintenance Decision-Making for Multiple Machine Systems , 2009 .

[7]  Mehmet Bayram Yildirim,et al.  A framework to minimise total energy consumption and total tardiness on a single machine , 2008 .

[8]  J. M. Norman,et al.  Control of Cash Reserves , 1965 .

[9]  Sami Kara,et al.  Impact of energy efficiency on computer numerically controlled machining , 2010 .

[10]  Ernst Worrell,et al.  Energy efficiency in the German pulp and paper industry – A model-based assessment of saving potentials , 2012 .

[11]  Dimitri P. Bertsekas,et al.  Approximate Dynamic Programming , 2017, Encyclopedia of Machine Learning and Data Mining.

[12]  M. Gheorghe,et al.  Models of machine tool efficiency and specific consumed energy , 2003 .

[13]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[14]  Kanako Tanaka,et al.  Review of policies and measures for energy efficiency in industry sector , 2011 .

[15]  Rolf A. Deininger,et al.  Generalization of White's Method of Successive Approximations to Periodic Markovian Decision Processes , 1972, Oper. Res..

[16]  T. Gutowski,et al.  Electrical Energy Requirements for Manufacturing Processes , 2006 .

[17]  E. Lawrence.  Energy Efficiency Improvement and Cost Saving Opportunities for the Vehicle Assembly Industry An ENERGY STAR ® Guide for Energy and Plant Managers , 2003 .

[18]  D. White.  Dynamic Programming and Systems of Uncertain Duration , 1965 .

[19]  Timothy G. Gutowski,et al.  An Environmental Analysis of Machining , 2004 .

[20]  G. J. Lieberman,et al.  A Stochastic Sequential Allocation Model , 1975, Oper. Res..

[21]  Ernst Worrell,et al.  Benchmarking the energy efficiency of Dutch industry: an assessment of the expected effect on energy consumption and CO2 emissions , 2002 .

[22]  Michael Gregory,et al.  Manufacturing Systems Engineering , 2015 .

[23]  Yi Qian,et al.  Proximity analysis on the life distribution functions of the high-speed rotating machine , 2009, 2009 8th International Conference on Reliability, Maintainability and Safety.

[24]  J. Bert Keats,et al.  Statistical Methods for Reliability Data , 1999 .

[25]  T. Gutowski,et al.  Environmentally benign manufacturing: Observations from Japan, Europe and the United States , 2005 .

[26]  D. Onstad,et al.  Dynamic programming and the computation of economic injury levels for crop disease control , 1985 .

[27]  Lin Li,et al.  Bottleneck detection of complex manufacturing systems using a data-driven method , 2009 .

[28]  Jie Ma,et al.  Solving the curse of dimensionality utilizing action-dependent heuristic dynamic programming , 2011, 2011 IEEE International Conference on Computer Science and Automation Engineering.

[29]  Krishnendu Chatterjee,et al.  Energy and Mean-Payoff Parity Markov Decision Processes , 2011, MFCS.

[30]  A. Geramifard.  Introduction to Markov Decision Processes , 2013 .

[31]  C. Park,et al.  Energy consumption reduction technology in manufacturing — A selective review of policies, standards, and research , 2009 .

[32]  John D. C. Little,et al.  The Use of Storage Water in a Hydroelectric System , 1955, Oper. Res..

[33]  Lin Li,et al.  Data driven bottleneck detection of manufacturing systems , 2009 .

[34]  Christoph Herrmann,et al.  Glocalized Solutions for Sustainability in Manufacturing , 2011 .