Simulation-based machine shop operations scheduling system for energy cost reduction

Abstract Owing to the ever increasing requirements in sustainability, manufacturing firms are trying to reduce their energy consumption and cost. In this paper, we propose a simulation-based machine shop operations scheduling system for minimizing the energy cost without sacrificing the productivity. The proposed system consists of two major functions: (1) real-time energy consumption monitoring (through power meters, a database server, and mobile applications) and (2) simulation-based machine shop operations scheduling (through a machine shop operations simulator). First, the real-time energy consumption monitoring function is developed to collect energy consumption data and provide real-time energy consumption status monitoring/electrical load abnormality warnings. Second, the simulation-based machine shop operations scheduling function is devised to estimate the energy consumptions and cost of CNC machines. In addition, an additive regression algorithm is developed to formulate energy consumption models for each individual machine as simulation inputs. The proposed system is implemented at a manufacturing company located in Tucson, Arizona state of USA. The experiment results reveal the effectiveness of the proposed system in achieving energy cost savings without sacrificing the productivity under various scenarios of machine shop operations.

[1]  Sanja Petrovic,et al.  An investigation into minimising total energy consumption and total weighted tardiness in job shops , 2014 .

[2]  Simon Rogers,et al.  A First Course in Machine Learning , 2011, Chapman and Hall / CRC machine learning and pattern recognition series.

[3]  J. Moon,et al.  Smart production scheduling with time-dependent and machine-dependent electricity cost by considering distributed energy resources and energy storage , 2014 .

[4]  David Dornfeld,et al.  Energy Consumption Characterization and Reduction Strategies for Milling Machine Tool Use , 2011 .

[5]  George Q. Huang,et al.  Hybrid flow shop scheduling considering machine electricity consumption cost , 2013 .

[6]  Kumar Abhishek,et al.  Simulation and optimization of machining parameters in drilling of titanium alloys , 2016, Simul. Model. Pract. Theory.

[7]  Thomas L. Saaty,et al.  DECISION MAKING WITH THE ANALYTIC HIERARCHY PROCESS , 2008 .

[8]  Toly Chen,et al.  A simulation analysis of part launching and order collection decisions for a flexible manufacturing system , 2016, Simul. Model. Pract. Theory.

[9]  Gabor Kecskemeti,et al.  DISSECT-CF: A simulator to foster energy-aware scheduling in infrastructure clouds , 2015, Simul. Model. Pract. Theory.

[10]  John Psarras,et al.  An integrated system for buildings’ energy-efficient automation: Application in the tertiary sector , 2013 .

[11]  M. S. Khalid,et al.  Optimization of process parameters for machining of AISI-1045 steel using Taguchi design and ANOVA , 2015, Simul. Model. Pract. Theory.

[12]  Christoph Herrmann,et al.  Energy oriented simulation of manufacturing systems - Concept and application , 2011 .

[13]  Elizabeth A. Peck,et al.  Introduction to Linear Regression Analysis , 2001 .

[14]  Jeffrey S. Smith,et al.  Simulation-based shop floor control , 2002 .

[15]  Chao Meng,et al.  A SysML-based simulation model aggregation framework for seedling propagation system , 2013, 2013 Winter Simulations Conference (WSC).

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

[17]  Peter Hoeller,et al.  Energy Prices, Taxes and Carbon Dioxide Emissions , 1991 .

[18]  Adriana Giret,et al.  Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm , 2013 .

[19]  Damien Trentesaux,et al.  Reactive and energy-aware scheduling of flexible manufacturing systems using potential fields , 2014, Comput. Ind..

[20]  Esfandyar Mazhari,et al.  Integrated analysis of high-penetration PV and PHEV with energy storage and demand response , 2013 .

[21]  Neil Brown,et al.  An advanced energy management framework to promote energy awareness , 2013 .

[22]  R. M. Shereef,et al.  Review of demand response under smart grid paradigm , 2011, ISGT2011-India.

[23]  Ioannis Mourtos,et al.  Towards a framework for energy-aware information systems in manufacturing , 2014, Comput. Ind..

[24]  Joaquín B. Ordieres Meré,et al.  Optimizing the production scheduling of a single machine to minimize total energy consumption costs , 2014 .