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A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making

by A. Mosavi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 1
Year of Publication: 2013
Authors: A. Mosavi
10.5120/11807-7457

A. Mosavi . A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making. International Journal of Computer Applications. 69, 1 ( May 2013), 26-38. DOI=10.5120/11807-7457

@article{ 10.5120/11807-7457,
author = { A. Mosavi },
title = { A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 1 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 26-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number1/11807-7457/ },
doi = { 10.5120/11807-7457 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:29:06.556107+05:30
%A A. Mosavi
%T A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 1
%P 26-38
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A novel environment for optimization, analytics and decision support in general engineering design problems is introduced. The utilized methodology is based on reactive search optimization (RSO) procedure and its recently implemented visualization software packages. The new set of powerful integrated data mining, modeling, visualiztion and learning tools via a handy procedure stretches beyond a decision-making task and attempts to discover new optimal designs relating to decision variables and objectives, so that a deeper understanding of the underlying problem can be obtained. In an optimal engineering design environment as such solving the multicriteria decision-making (MCDM) problem is considered as a combined task of optimization and decision-making. Yet in solving real-life MCDM problems often most of attention has been on finding the complete Pareto-optimal set of the associated multiobjective optimization (MOO) problem and less on decision-making. In this paper, along with presenting two case studies, the proposed interactive procedure which involves the decision-maker (DM) in the process addresses this issue effectively. Moreover the methodology delivers the capablity of handling the big data often associated with production decision-making as well as materials selection tasks in engineering design problems.

References
  1. Mosavi, A. 2010. Application of Multiobjective Optimization Packages in Design of an Evaporator Coil. World Academy of Science, Engineering and Technology, V61.
  2. Esmaeili, M. and Mosavi, A. 2010. Variable reduction for multi-objective optimization using data mining techniques; application to aerospace structures, International Conference on Computer Engineering and Technology, V5, pp. 333-337.
  3. Mosavi, A. 2010. The Multiobjective Optimization Package of IOSO; Applications and Future Trends, Conference of PhD students in Computer Science, CSCS, Szeged, Hungary.
  4. Mosavi, A. 2009. Computer Design and Simulation of Built Environment; Application to Forest. International Conference on Environmental and Computer Science
  5. Mosavi, A. 2009. Hydrodynamic Design and Optimization: Application to Design a General Case for Extra Equipments on the Submarine's Hull. International Conference on Computer Technology and Development, V2, pp. 139-143,
  6. Mosavi, A. 2009. Parametric modeling of trees and using integrated CAD/CFD and optimization tools: Application to creating the optimal planting patterns for new forests. 2nd International Conference Wind Effects on Trees, Albert-Ludwigs-University of Freiburg, Germany.
  7. Battiti, R. and Campigotto, P. 2010. Reactive search optimization: Learning while optimizing. an experiment in interactive multiobjective optimization. In S. Voss and M. Caserta, editors, Proceedings of MIC 2009, VIII Metaheuristic International Conference, Lecture Notes in Computer Science. Springer Verlag.
  8. Battiti, R. and Brunato, M. 2011. Reactive Business Intelligence. From Data to Models to Reactive Search", Srl, Italy.
  9. Mosavi, A. 2010. On Engineering Optimization the Splined Profiles", International modeFRONTIER Users' Meeting, Trieste, Italy.
  10. Mosavi, A. 2010. The large scale system of multiple criteria decision making; pre-processing. Large Scale Complex Systems Theory and Applications, V9, Part1.
  11. Mosavi, A. 2010. Applications of Interactive Methods of MOO in Chemical Engineering Problems. Global Journals of Engineering Research, V10.
  12. Miettinen, K. and Makela, M. 1995. Interactive bundle-based method for nondifferentiable multiobjective optimization: NIMBUS. Optimization V34, 1995, 231–246.
  13. Goldenthal, R. and Bercovier, M. 2004. Design of curves and surfaces using multi-objective optimization.
  14. Chaudhuri, S. and Deb, K. 2010. An interactive evolutionary multi-objective optimization and decision making procedure", Applied Soft Computing, V10, 496–511.
  15. Branke, J. 2008. Consideration of Partial User Preferences in Evolutionary Multiobjective Optimization, Springer, Berlin, pp. 157–178.
  16. Tan, K. C. , Lee, T. H. , Khoo, D. and Khor, E. F. 2001 A multiobjective evolutionay algorithm toolbox for computer-aided multiobjective optimization", IEEE Transactions on Systems, Man and Cybernetics—Part B: Cybernetics 31 (4) 537–556.
  17. Takagi, H. 2001. Interactive evolutionary computation: fusion of the capabilities of ec optimization and human evaluation", Proceedings of IEEE 89, 1275–1296.
  18. Miettinen, K. 1999. Nonlinear Multiobjective Optimization, Kluwer, Boston.
  19. V. Chankong, Y. Y. Haimes, "Multiobjective Decision Making Theory and Methodology", North-Holland, New York, 1983.
  20. Wierzbicki, A. P. 1980. The use of reference objectives in multiobjective optimization", in: G. Fandel, T. Gal (Eds. ), Multiple Criteria Decision Making Theory and Applications, Springer-Verlag, Berlin, 468–486.
  21. Kamalian, R. Takagi, H. and Agogino, A. 2004. Optimized design of mems by evolutionary multi-objective optimization with interactive evolutionary computation, in: Proceedings of the Genetic and Evolutionary Computing Conference (GECCO), 1030–1041.
  22. Deb, K. and Gupta, H. 2005. Searching for robust Pareto-optimal solutions in multi-objective optimization", in: Proceedings of the Third Evolutionary Multi-Criteria Optimization (EMO-05), 150–164.
  23. Deb, K. and Chaudhuri, S. 2007. I-MODE: an interactive multi-objective optimization and decision-making using evolutionary methods. in: Proceedings of Fourth International Conference on Evolutionary Multi-Criteria Optimization (EMO 2007), 788–802.
  24. Rekliatis, G. V. , Ravindrab A. , and Ragsdell, K. M. 1983. Engineering Optimisation Methods and Applications". New York: Wiley.
  25. Mosavi, A. , Milani, A. S. , Hoffmann, M. and Komeili, M. 2012. Multiple criteria decision making integrated with mechanical modeling of draping for material selection of textile composites. 15TH Eeuropean Conference on Composite Materials, Venice, Italy.
  26. Mosavi, A. Hoffmann, M. and Milani, A. S. 2012. Adapting the Reactive Search Optimization and Visualization Algorithms for Multiobjective Optimization Problems; Application to Geometry", Conference of PhD Students in Computer Science, Szeged, Hungary.
  27. Mosavi, A. , Hoffmann, M. and Milani, A. S. 2012 Optimal Design of the NURBS Curves and Surfaces Utilizing Multiobjective Optimization and Decision Making Algorithms of RSO", Conference of PhD Students in Mathematics, Szeged, Hungary.
  28. Foldi, E. , Delavar, A. , Mosavi, A. , Hewage, K. N. Milani, A. S. , Moussavi, A. A. and Yeheyis, M. Y, 2012. Reconsidering the Multiple Criteria Decision Making Problems of Construction Projects; Using Advanced Visualization and Data Mining Tools. Conference of PhD Students in Computer Science, Szeged, Hungary, 2012.
  29. Mosavi, A. , Azodinia, M. , Milani, A. S. , Hewage, K. N. and Yeheyis, M. 2011. Reconsidering the Multiple Criteria Decision Making Problems of Construction Workers With the aid of Grapheur", Newsletter of EnginSoft CAE Conference 2011, Year 8, No 4, Winter 2011.
  30. Mosavi, A. , Azodinia, M. , Milani, A. S. , Hewage, K. N. and Yeheyis, M. 2011. Reconsidering the Multiple Criteria Decision Making Problems of Construction Workers With the aid of Grapheur", International ANSYS and EnginSoft Conference, Italy.
  31. Jones, C. V. 1994 Feature Article–Visualization and Optimization". INFORMS Journal on Computing, 6, 221-229.
  32. Piero, P. Subbu, R. Lizzi, J. 2009. MCDM: A framework for research and applications". IEEE Computational Intelligence Magazine. 4, pp. 48–61.
  33. Battiti, R. , Brunato, M. and Mascia, F. 2008 Reactive Search and Intelligent Optimization". Operations research/Computer Science Interfaces. Springer Verlag.
  34. Mosavi, A. 2010 Multiple Criteria Decision-Making Preprocessing Using Data Mining tools. IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 2, No 1.
  35. Adejuwon, A and Mosavi, A. 2010. Domain Driven Data Mining; Application to Business", IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No 2.
  36. Mosavi, A. and Vaezipour, A. 2012. Reactive Search Optimization; Application to Multiobjective Optimization Problems. Applied Mathematics 3, no. 30 1572-1582.
  37. Mosavi, A. 2010 Multiobjective Optimization of Spline Curves using modeFRONTIER", International modeFRONTIER Users' Meeting, Trieste, Italy.
  38. Anzellotti, G. , Battiti, R. Lazzizzera, I. Lee, P. A. Sartori, G. Soncini, G. Tecchiolli, A. Zorat, 1995. Totem: a highly parallel chip for triggering applications with inductive learning based on the reactive tabu search", In: AIHENP95. Pisa, IT.
  39. Battiti, R. and Brunato, M. 2009. Reactive search optimization: learning while optimizing", In: Handbook of Metaheuristics, 2nd edn. Springer, Berlin.
  40. Battiti, R. , Brunato, M. and Mascia, F. 2008. Reactive Search and Intelligent Optimization", Operations research/ Computer Science Interfaces, vol. 45. Springer Verlag.
  41. Lenne, R. , Solnon, C. , Stutzle, T. , Tannier, E. and Birattari, M. 2008 Reactive Stochastic Local Search Algorithms for the Genomic Median Problem LECTURE NOTES IN COMPUTER SCIENCE 4972, 266.
  42. Genji, T. , Oomori, T. , Miyazato, K. and Hayashi, Y. N. Fukuyama, K. Co. 2003. Service Restoration in Distribution Systems Aiming Higher Utilization Rate of Feeders", In: Proc. of the Fifth Metaheuristics International Conference (MIC2003).
  43. R. Russell, W. Chiang, D. Zepeda, "Integrating multi-product production and distribution in newspaper logistics", Computers and Operations Research 35(5), 1576–1588, 2008.
  44. Nanry, W. and Wesley Barnes, J. 2000. Solving the pickup and delivery problem with time windows using reactive tabu search", Transportation Research Part B 34(2), 107–121.
  45. Login, A. and Areas, S. 2007. Reactive tabu adaptive memory programming search for the vehicle routing problem with backhauls", Journal of the Operational Research Society 58, 1630–1641.
  46. Chambers, J. and Barnes, J. 1996. New tabu search results for the job shop scheduling problem", The University of Texas, Austin, TX, Technical Report Series ORP96-06, Graduate Program in Operations Research and Industrial Engineering.
  47. Delmaire, H. D?az, J. Fernandez, E. and Ortega, M. 1999. Reactive GRASP and Tabu Search based heuristics for the single source capacitated plant location problem", INFOR 37, 194–225.
  48. Fink, A. and Voß, S. 2003. Solving the continuous flow-shop scheduling problem by metaheuristics. European Journal of Operational Research 151(2), 400–414, 2003.
  49. Potocnik, P. and Grabec, I. "Adaptive self-tuning neurocontrol", Mathematics and Computers in Simulation 51(3-4), 201–207.
  50. Winter, T. and Zimmermann, U. 2000. Real-time dispatch of trams in storage yards. Annals of Operations Research (96), 287–315.
  51. Magdon-Ismail, M. , Goldberg, M. , Wallace, W. and Siebecker, D. 2003, Locating hidden groups in communication networks using hidden markov models" LECTURE NOTES IN COMPUTER SCIENCE, 126–137.
  52. Hifi, M. Michrafy, M. Sbihi, A. 2006. A Reactive Local Search-Based Algorithm for the Multiple-Choice Multi-Dimensional Knapsack Problem. Computational Optimization and Applications 33(2), 271–285.
  53. Hu, B. and Raidl G. R. Variable neighborhood descent with self-adaptive neighborhoodordering", In: C. Cotta, A. J. Fernandez, J. E. Gallardo (eds. ) Proceedings of the 7th EU/MEeting on Adaptive, Self-Adaptive, and Multi-Level Metaheuristics, malaga, Spain, 2006.
  54. Ryan, J. , Bailey, T. , Moore, J. and Carlton, W. 1998. Reactive tabu search in unmanned aerial reconnaissance simulations. Proceedings of the 30th conference onWinter simulation. 873–880.
  55. Kincaid, R. and Laba, K. 1998. Reactive Tabu Search and Sensor Selection inActive Structural Acoustic Control Problems", Journal of Heuristics 4(3), 199–220.
  56. Hansen, P. Mladenovic, N. 2005. Variable neighborhood search. In: E. Burke, G. Kendall (eds. ) Search methodologies: introductory tutorials in optimization and decision support techniques. 211–238. Springer.
  57. Hamza, K. Mahmoud, H. Saitou, K. "Design optimization of N-shaped roof trusses using reactive taboo search", Applied Soft Computing Journal 3(3), 221–235, 2003.
  58. B?achut, J. 2007. Tabu search optimization of externally pressurized barrels and domes", Engineering Optimization 39(8), 899–918.
  59. Mosavi, A. 2013, Data mining for decision making in engineering optimal design, Journal of Artificial Intelligence & Data Mining, V1.
  60. Roberto, B. and Passerini, A. 2010. Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker. (PDF). IEEE Transactions on Evolutionary Computation 14 (15): 671–687.
  61. Ashby, M. 1999 Materials selection in mechanical design. Butterworth-Heinemann, Burlington.
  62. Jahan A. , Ismail M. Y. , Sapuan S. M. and Mustapha F. 2010 Material screening and choosing methods – A review. Materials and Design, 31, pp. 696–705.
  63. March, J. 1978. Bounded rationality, ambiguity, and the engineering of choice, The Bell Journal of Economics, pp. 587-608.
  64. Milani, A. S. , Eskicioglu C. , Robles K. , Bujun K. , and Hosseini-Nasab, H. 2011 Multiple criteria decision making with life cycle assessment for material selection of composites. eXPRESS Polymer Letters, 5, pp. 1062–1074.
  65. Geoffrion, A. M. 1976 The purpose of mathematical programming is insight, not numbers. Interfaces, 7, pp. 81–92.
  66. Jones, C. V. 1994 Feature Article–Visualization and Optimization. INFORMS Journal on Computing, 6, pp. 221-229.
  67. Piero P. , Subbu R. , Lizzi J. MCDM: A framework for research and applications. IEEE Computational Intelligence Magazine. 4, pp. 48–61(2009).
  68. Vassiliadis, S. , Kallivretaki, A. and Provatidis, C. , Mechanical modelling of multifilament twisted yarns. Fibers and Polymers, 11, pp. 89-96 (2010).
  69. Komeili M. , and Milani A. S. Finite Element Modeling of Woven Fabric Composites at Meso-Level Under Combined Loading Modes in "Advances in Modern Woven Fabrics Technology", edited by Vassiliadis S. INTECH, Croatia, pp. 66-77 (2011).
  70. Khabazi, Z. 2010 Generative algorithms concepts and experiments weaving. Morphogenesism, London
  71. Rakshit, S. and Ananthasuresh, GK. 2008 Simultaneous material selection and geometry design of statically determinate trusses using continuous optimization. Struct Multi Optim. 35:55-68.
  72. Ashby, M. F. , et al. Selection strategies for materials and processes. Materials & Design 25. 1 (2004): 51-67.
  73. Edwards KL. Selecting materials for optimum use in engineering components. Mater Des. 2005;26:469-473.
  74. Edwards KL. Linking materials and design: an assessment of purpose and progress. Mater Des. 2002;23:255-264.
  75. Shanian A, Milani AS. 2012 Combined finit element-multiple criteria optimization approach for materials selection of gas turbine component J Appl Mech. ;1:302.
  76. Sirisalee P, Ashby MF, Parks GT, John Clarkson P. Multi-criteria material selection of monolithic and multimaterials in engineering design. Adv Eng Mater. 2006;8:48-56.
  77. McDowell DL, Panchal JH, Choi H-J Seepersad CC, Allen JK, Misstree F. 2010. Critical path issues in materials design. Integrated design of mutiscale, multifunctional materials and products Boston, MA: Butterworth-Heinemann, 23-38.
  78. Jahan, A. and Edwards, K. L. 2013 Multi-criteria Decision Analysis for Supporting the Selection of Engineering Materials in Product Design. Elsevier Science.
  79. Mosavi, A. 2013. A MCDM Software Tool for the Automated Design Environments. 26th Europian Conference on Operational Research, Rome.
  80. Vaezipour, A. and Mosavi, A. 2012. Enterprise Decision Management With the Aid of Advanced Business Intelligence and Interactive Visualization Tools. International CAE Conference, Verona, Italy.
  81. Mosavi, A. 2010 Multiobjective optimization package of IOSO, 24th Mini EURO Conference on Continuous Optimization and Information-Based Technologies in the Financial Sector, Izmir, Turkey.
  82. Mosavi, A. 2013 A MCDM Software Tool for Automating the Optimal Design Environments with an Application in Shape Optimization, International Conference on Optimization and Analysis of Structures, Tartu, Estonia, 2013.
  83. Mosavi, A. 2013. On Developing a Decision-Making Tool for General Applications to Computer Vision. International Journal of Computer Applications.
  84. Vaezipour A. , et al. , 2013. Machine learning integrated optimization for decision making," In Proceedings of 26th Europian Conference on Operational Research, Rome.
  85. Mosavi, A. Hoffmann, M. and Peter, N. 2009 Automatic multi-objective surface design optimisation using modeFRONTIER's CAD/CAE integrated system: Application to military submarine sail EnginSoft International Conference and ANSYS Italian Conference, Bergamo, Italy.
  86. Hossaini, N. , and Hewage, K. 2013. Emergy accounting for regional studies: Case study of Canada and its provinces. Journal of environmental management, 118, 177-185.
  87. Yeheyis, M. , Hewage, K. , Alam, M. S. , Eskicioglu, C. , and Sadiq, R. 2013. An overview of construction and demolition waste management in Canada: a lifecycle analysis approach to sustainability. Clean Technologies and Environmental Policy, 15(1), 81-91.
  88. Hossaini, N. , and Hewage, K. 2011. Sustainable Materials Selection for Canadian Construction Industry: An Emergy-Based Life-Cycle Analysis (Em-LCA) of Conventional and LEED Suggested Construction Materials. Journal of Sustainable Development, 5(1), p2.
Index Terms

Computer Science
Information Sciences

Keywords

Opimal engineering design interactive multicriteria decision making reactive search optimization multiobjective optimization

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