Developing a decision support system for improving sustainability performance of manufacturing processes

It is difficult to formulate and solve optimization problems for sustainability performance in manufacturing. The main reasons for this are: (1) optimization problems are typically complex and involve manufacturing and sustainability aspects, (2) these problems require diversity of manufacturing data, (3) optimization modeling and solving tasks require specialized expertise and programming skills, (4) the use of a different optimization application requires re-modeling of optimization problems even for the same problem, and (5) these optimization models are not decomposed nor reusable. This paper presents the development of a decision support system (DSS) that enables manufacturers to formulate optimization problems at multiple manufacturing levels, to represent various manufacturing data, to create compatible and reusable models and to derive easily optimal solutions for improving sustainability performance. We have implemented a DSS prototype system and applied this system to two case studies. The case studies demonstrate how to allocate resources at the production level and how to select process parameters at the unit-process level to achieve minimal energy consumption. The research of this paper will help reduce time and effort for enhancing sustainability performance without heavily relying on optimization expertise.

[1]  David Dornfeld,et al.  Green Manufacturing and Sustainable Manufacturing Partnership Title Design and Operation Strategies for Green Machine Tool Development , 2010 .

[2]  Shun Jia,et al.  Machining activity extraction and energy attributes inheritance method to support intelligent energy estimation of machining process , 2016, J. Intell. Manuf..

[3]  Bilgin Tolga Simsek,et al.  Optimization of cutting fluids and cutting parameters during end milling by using D-optimal design of experiments , 2013 .

[4]  Goran Šimunović,et al.  Intelligent predictive decision support system for maintenance , 2003 .

[5]  Lorenz T. Biegler,et al.  Nonlinear Waves in Integrable and Nonintegrable Systems , 2018 .

[6]  Christopher J. Roy,et al.  A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing , 2011 .

[7]  Ali M. S. Zalzala,et al.  Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons , 2000, IEEE Trans. Evol. Comput..

[8]  Wim Dewulf,et al.  Methodology for systematic analysis and improvement of manufacturing unit process life cycle inventory (UPLCI) CO2PE! initiative (cooperative effort on process emissions in manufacturing). Part 2: case studies , 2012, The International Journal of Life Cycle Assessment.

[9]  Li Li,et al.  Multi-objective optimization of cutting parameters in sculptured parts machining based on neural network , 2015, J. Intell. Manuf..

[10]  Duck Bong Kim,et al.  Decision guidance methodology for sustainable manufacturing using process analytics formalism , 2017, J. Intell. Manuf..

[11]  L. J. Zeballos,et al.  A constraint programming approach to tool allocation and production scheduling in flexible manufacturing systems , 2010 .

[12]  Pengyu Li,et al.  A quantitative approach to analyze carbon emissions of CNC-based machining systems , 2015, J. Intell. Manuf..

[13]  A. Hemanth.  THE HADOOP DISTRIBUTED FILE SYSTEM: BALANCING PORTABILTY , 2013 .

[14]  Mahesh Mani,et al.  Characterizing Energy Consumption of the Injection Molding Process , 2013 .

[15]  Alexandre Dolgui,et al.  A decision support system for design of mass production machining lines composed of stations with rotary or mobile table , 2012 .

[16]  Hong Wang,et al.  Hybrid knowledge model of process planning and its green extension , 2016, J. Intell. Manuf..

[17]  Wim Dewulf,et al.  Methodology for systematic analysis and improvement of manufacturing unit process life cycle inventory (UPLCI) Part 1: Methodology Description , 2011 .

[18]  Lei Qi,et al.  Hybrid knowledge model of process planning and its green extension , 2016 .

[19]  Chee Khiang Pang,et al.  An energy data-driven decision support system for high-performance manufacturing industries , 2013 .

[20]  Bülent Çatay,et al.  A decision support system for machine tool selection , 2004 .

[21]  Guodong Shao,et al.  Process analytics formalism for decision guidance in sustainable manufacturing , 2016, J. Intell. Manuf..

[22]  K. Jayakrishna,et al.  Development of decision support system for sustainability evaluation: a case study , 2013, Clean Technologies and Environmental Policy.

[23]  I. Hanafi,et al.  Optimization of cutting conditions for sustainable machining of PEEK-CF30 using TiN tools , 2012 .

[24]  Shamsuddin Ahmed,et al.  Uncertainty Factors in Real Manufacturing Environment , 2009 .

[25]  Mohammed A. Omar,et al.  Production energy optimization using low dynamic programming, a decision support tool for sustainable manufacturing , 2015 .

[26]  Dimitris Kanellopoulos,et al.  Data Preprocessing for Supervised Leaning , 2007 .

[27]  Ronald E. Giachetti,et al.  A decision support system for material and manufacturing process selection , 1998, J. Intell. Manuf..

[28]  C. Herrmann,et al.  Determining optimal process parameters to increase the eco-efficiency of grinding processes , 2014 .

[29]  Joaquim Ciurana,et al.  Design of a decision support system for machine tool selection based on machine characteristics and performance tests , 2011, J. Intell. Manuf..

[30]  Zahari Taha,et al.  A hybrid fuzzy AHP-PROMETHEE decision support system for machine tool selection in flexible manufacturing cell , 2011, Journal of Intelligent Manufacturing.

[31]  Yusuf Tansel İç,et al.  A decision support system for selection of net-shape primary manufacturing processes , 2014 .

[32]  Ron Kohavi,et al.  Emerging trends in business analytics , 2002, CACM.

[33]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[34]  Wim Dewulf,et al.  Methodology for systematic analysis and improvement of manufacturing unit process life cycle inventory (UPLCI) Part 2: Case Studies , 2012 .

[35]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[36]  Asif Iqbal,et al.  A rule-based system for trade-off among energy consumption, tool life, and productivity in machining process , 2015, J. Intell. Manuf..

[37]  Rajesh Kumar Bhushan,et al.  Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites , 2013 .

[38]  J. Euzenat,et al.  Ontology Matching , 2007, Springer Berlin Heidelberg.

[39]  J. Clausen,et al.  Branch and Bound Algorithms-Principles and Examples , 2003 .

[40]  Sudarsan Rachuri,et al.  Faceted classification of manufacturing processes for sustainability performance evaluation , 2014 .

[41]  David Dornfeld,et al.  Cost and Energy Consumption Optimization of Product Manufacture in a Flexible Manufacturing System , 2012 .

[42]  S. Feng,et al.  Categorization of indicators for sustainable manufacturing , 2013 .

[43]  Hari Singh,et al.  Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi's technique—A comparative analysis , 2008 .

[44]  Pingyu Jiang,et al.  Implementing of a three-phase integrated decision support model for parts machining outsourcing , 2014 .

[45]  Gabriela P. Henning,et al.  A Constraint Programming Approach to Tool Allocation and Resource Scheduling in FMS , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[46]  Benny Tjahjono,et al.  A decision support tool to facilitate the design of cellular manufacturing layouts , 2007, Comput. Ind. Eng..

[47]  Sudarsan Rachuri,et al.  Predictive Analytics Model for Power Consumption in Manufacturing , 2014 .

[48]  Singiresu S. Rao.  Engineering Optimization : Theory and Practice , 2010 .

[49]  John W. Sutherland,et al.  Flow shop scheduling with peak power consumption constraints , 2013, Ann. Oper. Res..