A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects

Robotics and Computer-Integrated Manufacturing

Volumn 29, Issue 1, 2013, Pages 157-168

  Meysam Mousavi, S ^a   Tavakkoli Moghaddam, R ^a   Vahdani, Behnam ^a   Hashemi, H ^b   Sanjari, M J ^c  

^a UNIVERSITY OF TEHRAN   (Iran)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

^c AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Imperialist competitive algorithm; New product development projects; Support vector regression; Time estimation

Indexed keywords

BACK-PROPAGATION NEURAL NETWORKS; COMPETITION MECHANISM; COMPETITIVE ALGORITHMS; COMPLEX PROBLEMS; CONVENTIONAL TECHNIQUES; DATA PATTERNS; DATA SETS; ENGINEERING PROBLEMS; GENERAL REGRESSION NEURAL NETWORK; GENERALIZATION ABILITY; MANUFACTURING INDUSTRIES; NEURAL NETWORK TECHNIQUES; NEW PRODUCT DEVELOPMENT; NON-LINEAR REGRESSION; NON-LINEARITY; NONLINEAR REGRESSION PROBLEMS; NPD PROJECTS; OPTIMIZATION ALGORITHMS; OPTIMIZATION STRATEGY; STATISTICAL LEARNING THEORY; SUPPORT VECTOR; SUPPORT VECTOR REGRESSION (SVR); TIME ESTIMATION;

ALGORITHMS; ESTIMATION; NEURAL NETWORKS; PRODUCT DEVELOPMENT; REGRESSION ANALYSIS;

PROBLEM SOLVING;

EID: 84866373875     PISSN: 07365845     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rcim.2012.04.006     Document Type: Article

References (49)

1. R.J. Calantone, and C.A.D. Benedetto Performance and time to market: accelerating cycle time with overlapping stages IEEE Transaction on Engineering Management 47 2 2000 232 244
2. J. Kuar, J. Duhovnik, J. Grum, and M. Starbek How to reduce new product development time Robotics and Computer-Integrated Manufacturing 20 1 2004 1 15
3. D. Milosevic Managing new product development projects P.W.G. Morris, J.K. Pinto, The Wiley Guide to Managing Projects 2004 John Wiley & Sons Hoboken
4. C.H. Chen, S.F. Ling, and W. Chen Project scheduling for collaborative product development using DSM International Journal of Project Management 21 4 2003 291 299
5. S. Jónsdóttir, J. Vesterager, and T. Borresen Development of a product model for specifying new lines of seafood products Robotics and Computer-Integrated Manufacturing 16 6 2000 465 473
6. N. Karagozoglu, and W.B. Brown Time-based management of the new product development process The Journal of Product Innovation Management 10 3 1993 204 215
7. S.R. Rosenthal, and M.V. Tatikonda Time management in new product development: case study findings Journal of Manufacturing Systems 11 5 1995 359 368
8. D.W. LaBahn, A. Ali, and R. Krapfel New product development cycle time the influence of project and process factors in small manufacturing companies Journal of Business Research 36 2 1996 179 188
9. T.B. Clift, and M.B. Vandenbosch Project complexity and efforts to reduce product development cycle time Journal of Business Research 45 2 1999 187 198
10. A. Griffin Product development cycle time for business-to-business products Industrial Marketing Management 31 4 2002 291 304
11. M. Swink Completing projects on-time: how project acceleration affects new product development Journal of Engineering and Technology Management 20 4 2003 319 344
12. A.B. Dragut, and J.W.M. Bertrand A representation model for the solving-time distribution of a set of design tasks in new product development (NPD) European Journal of Operational Research 189 3 2008 1217 1233
13. F. Langerak, and E.J. Hultink The effect of new product development acceleration approaches on development speed: a case study Journal of Engineering and Technology Management 25 3 2008 157 167
14. J. Wang, and Y.I. Lin An overlapping process model to assess schedule risk for new product development Computers & Industrial Engineering 57 2 2009 460 474
15. M. Reboreda, M. Fernández-Delgado, and S. Barro Time estimation in injection molding production for automotive industry based on SVR and RBF Bioinspired Applications in Artificial and Natural Computation Lecture Notes in Computer Science Vol. 5602/2009 2009 509 518 10.1007/978-3-642-02267-8-54
16. V.N. Vapnik The nature of statistical learning theory 1999 Springer London
17. C.J.C. Burges A tutorial on support vector machines for pattern recognition Data Mining and Knowledge Discovery 2 2 1998 121 167
18. S. Deng, and T.H. Yeh Applying least squares support vector machines to airframe wing-box structural design cost estimation Expert Systems with Applications 37 2010 8417 8423
19. S.W. Fei, M.J. Wang, Y.B. Miao, J. Tu, and C.L. Liu Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil Energy Conversion and Management 50 6 2009 1604 1609
20. K.Y. Chen, and C.H. Wang Support vector regression with genetic algorithms in forecasting tourism demand Tourism Management 28 1 2007 215 226
21. S.H. Mina, J. Lee, and I. Han Hybrid genetic algorithms and support vector machines for bankruptcy prediction Expert Systems with Applications 31 3 2006 652 660
22. E. Avci Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm-support vector machines: HGASVM Expert Systems with Applications 36 2009 1391 1402
23. E. Atashpaz-Gargari, and C. Lucas Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition IEEE Congress on Evolutionary Computation 2007 4661 4667
24. E. Atashpaz-Gargari, F. Hashemzadeh, R. Rajabioun, and C. Lucas Colonial competitive algorithm: a novel approach for PID controller design in MIMO distillation column process International Journal of Intelligent Computing and Cybernetics 1 3 2008 337 355
25. A.J. Smola, and B. Scholkopf A tutorial on support vector regression, Statistics and Computing 14 2004 Kluwer Academic Publishers 199-222
26. V.N. Vapnik Statistic Learning Theory 1998 Wiley-Interscience Hoboken
27. K.Y. Chen, L.S. Chen, M.C. Chen, and C.L. Lee Using SVM based method for equipment fault detection in a thermal power plant Computers in Industry 62 2011 42 50
28. J.A.K. Suykens, T.V. Gestel, J.D. Brabanter, and J. Vandewalle Least Square Support Vector Machines 2002 World Scientific Singapore
29. Smola A.J., Schölkopf B. Learning with kernels - support vector machines, regularization, optimization, and beyond. MIT Press, 2002.
30. C.-S. Leung, K.-W. Wong, P.-F. Sum, and L.-W. Chan A pruning method for the recursive least squared algorithm Neural Networks 14 2 2001 147 174
31. B. Li, X. Li, and Z. Zhao Novel algorithm for constructing support vector machine regression ensemble Journal of Systems Engineering and Electronics 17 3 2006 541 545
32. M. Bortman, and M. Aladjem A growing and pruning method for radial basis function networks IEEE Transactions on Neural Networks 20 6 2009 1039 1045
33. N. Long, D. Gianola, G.J.M. Rosa, K.A. Weigel, A. Kranis, and O. Gonzalez-Recio Radial basis function regression methods for predicting quantitative traits using SNP markers Genetics Research (Camb) 92 2010 209 225
34. A. Kaveh, and S. Talatahari Optimum design of skeletal structures using imperialist competitive algorithm Computers and Structures 88 21-22 2010 1220 1229
35. S. Nazari-Shirkouhi, H. Eivazy, R. Ghodsi, K. Rezaie, and E. Atashpaz-Gargari Solving the integrated product mix-outsourcing problem using the imperialist competitive algorithm Expert Systems with Applications 37 12 2010 7615 7626
36. C. Lucas, Z. Nasiri-Gheidari, and F. Tootoonchian Application of an imperialist competitive algorithm to the design of a linear induction motor Energy Conversion and Management 51 7 2010 1407 1411
37. A. Biabangard-Oskouyi, E. Atashpaz-Gargari, N. Soltani, and C. Lucas Application of imperialist competitive algorithm for materials property characterization from sharp indentation test International Journal of Engineering Simulation 10 2010 1
38. R. Rajabioun, E. Atashpaz-Gargari, and C. Lucas Colonial competitive algorithm as a tool for Nash equilibrium point achievement Lecture Notes in Computer Science 5073 2 2008 680 695
39. C.-L. Huang, and J.-F. Dun A distributed PSO-SVM hybrid system with feature selection and parameter optimization Applied Soft Computing 8 2008 1381 1391
40. K. Duan, S. Keerthi, and A. Poo Evaluation of simple performance measures for tuning SVM hyper parameters (Technical report) 2001 National University of Singapore, Department of Mechanical Engineering Singapore
41. P. OConnor Implementing a stage-gate process: a multi-company perspective Journal of Product Innovation Management 11 3 1994 183 200
42. L. Dwyer, and R. Mellor Corporate environment and the proficiency of new product process activities Technovation 11 2 1991 63 78
43. L.-C. Chao, and C.-F. Chien A model for updating project S-curve by using neural networks and matching progress Automation in Construction 19 2010 84 91
44. W.-H. Chen, S.-H. Hsu, and H.-P. Shen Application of SVM and ANN for intrusion detection Computers & Operations Research 32 2005 2617 2634
45. W. Huang, Y. Nakamori, and S.-Y. Wang Forecasting stock market movement direction with support vector machine Computers & Operations Research 32 2005 2513 2522
46. A. Alenezi, S.A. Moses, and T.B. Trafalis Real-time prediction of order flow times using support vector regression Computers & Operations Research 35 2008 3489 3503
47. S. Haykin Neural networks: a comprehensive foundation 2nd ed. 1999 Prentice Hall
48. C. Goutte Note on free lunches and cross-validation Neural Computation 9 1997 1211 1215
49. L. Rutkowski Generalized regression neural networks in time-varying environment IEEE Transactions on Neural Networks 15 3 2004 576 596