Evaluación de Tecnología utilizando TOPSIS en Presencia de Multi-colinealidad en Atributos: ¿Por qué usar distancia de Mahalanobis?

Autores/as

  • Rodrigo Villanueva Ponce Universidad Autónoma de Ciudad Juárez
  • Jorge Luis García Alcaraz Universidad Autónoma de Ciudad Juárez https://orcid.org/0000-0002-7092-6963

DOI:

https://doi.org/10.17533/udea.redin.16308

Palabras clave:

técnicas multi-atributos, inversión en Tecnologías para la Manufactura Avanzada (TMA), TOPSIS, distancia Euclidiana (DE), distancia Mahalanobis (DM)

Resumen

Este artículo justifica matemáticamente el mejoramiento a la técnica multicriterio TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), la cual utiliza la distancia Euclidiana al evaluar un conjunto de alternativas, la cual asume independencia entre los atributos de dicha opción, algo que frecuentemente no se cumple. Se demuestra como matemáticamente la distancia Mahalanobis integra la dependencia lineal entre los atributos de las alternativas evaluadas, además se presentan dos casos de estudio en los que se realizan evaluaciones con ambas distancias, encontrándose diferencias en las soluciones obtenidas.
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Biografía del autor/a

Rodrigo Villanueva Ponce, Universidad Autónoma de Ciudad Juárez

Instituto de Ingeniería y Tecnología. Departamento de Ingeniería Industrial y Manufactura.

Jorge Luis García Alcaraz, Universidad Autónoma de Ciudad Juárez

Instituto de Ingeniería y Tecnología. Departamento de Ingeniería Industrial y Manufactura.

Citas

C. Parkan, L. Wu. “Decision-making and performance measurement models with applications to robot selection”. Computers & Industrial Engineering. Vol. 36. 1999. pp. 503-523. DOI: https://doi.org/10.1016/S0360-8352(99)00146-1

C. Hofmann, S. Orr. “Advanced manufacturing technology adoption-the German experience”. Technovation. Vol. 25. 2005. pp. 711-724. DOI: https://doi.org/10.1016/j.technovation.2003.12.002

F. Lefley, F. Wharton, L. Hajek, J. Hynek, V. Janecek. “Manufacturing investments in the Czech Republic: - An international comparison”. International Journal of Production Economics. Vol. 88. 2004. pp. 1-14. DOI: https://doi.org/10.1016/S0925-5273(03)00129-4

S. MacDougall, R. Pike. “Consider your options: changes to strategic value during implementation of advanced manufacturing technology”. Omega. Vol. 31. 2003. pp. 1-15. DOI: https://doi.org/10.1016/S0305-0483(02)00061-0

M. Small, I. Chen. “Economic and strategic justification of AMT inferences from industrial practices”. International Journal of Production Economic. Vol. 49. 1997. pp. 65-75. DOI: https://doi.org/10.1016/S0925-5273(96)00120-X

J. Meredith, N. Suresh. “Justification techniques for advanced manufacturing technologies”. International Journal of Production Research. Vol. 24. 1987. pp. 1043-1057. DOI: https://doi.org/10.1080/00207548608919787

R. Adler. “Strategic Investment Decision Appraisal Techniques: The Old and the New”. Business Horizons. Vol. 43. 2000. pp. 15-22. DOI: https://doi.org/10.1016/S0007-6813(00)80017-8

M. Wiecek, E. Matthias, G. Fadel, J. Ruiz. “Multiple criteria decision making for engineering”. Omega. Vol. 36. 2008. pp. 337-339. DOI: https://doi.org/10.1016/j.omega.2006.10.001

R. Yusuff, M. Hashmib. “A preliminary study on the potential use of the Analytical Hierarchical Process (AHP) to predict Advanced Manufacturing Technology (AMT) implementation”. Robotics and Computer-Integrated Manufacturing. Vol. 7. 2001. pp. 421-427. DOI: https://doi.org/10.1016/S0736-5845(01)00016-3

B. Ahn, K. Park. “Comparing methods for multi-attribute decision making with ordinal weights”. Computers and Operations Research. Vol. 35. 2008. pp. 1660-1670. DOI: https://doi.org/10.1016/j.cor.2006.09.026

S. Hajkowicz, A. Higgins. “A comparison of multiple criteria analysis techniques for water resource management”. European Journal of Operational Research. Vol. 184. 2008. pp. 255-265. DOI: https://doi.org/10.1016/j.ejor.2006.10.045

Y. Lai, T. Liu, C. Hwang. “TOPSIS for MODM”. European Journal of Operational Research. Vol. 76. 1994. pp. 486-500. DOI: https://doi.org/10.1016/0377-2217(94)90282-8

M. Chen, G. Tzeng. “Combining gray relation and TOPSIS concepts for selecting an expatriate host country”. Mathematical and Computer Modelling. Vol. 40. 2004. pp. 1473-1490. DOI: https://doi.org/10.1016/j.mcm.2005.01.006

K. Yoon, C. Hwang. “Multiple Attribute Decision Making. An introduction”. Quantitative Applications in the Social Sciences. Vol. 7-104. 1995. pp. 53-62.

M. Janic. “Multi-criteria evaluation of high-speed rail, trans-rapid maglev, and air passenger transport in Europe”. Transportation Planning and Technology. Vol. 26. 2003. pp. 491-512. DOI: https://doi.org/10.1080/0308106032000167373

C. Kwong, S. Tam. “Case-based reasoning approach to concurrent design of low power transformers”. Journal of Materials Processing Technology. Vol. 128. 2002. pp. 136-141. DOI: https://doi.org/10.1016/S0924-0136(02)00440-5

A. Milani, A. Shanian, R. Madoliat. “The effect of normalization norms in multiple attribute decision making models: A case study in gear material selection”. Structural Multidisciplinary Optimization. Vol. 29. 2005. pp. 312-318. DOI: https://doi.org/10.1007/s00158-004-0473-1

B. Srdjevic, Y. Medeiros, A. Faria. “An objective multi-criteria evaluation of water management scenarios”. Water Resources Management. Vol. 18. 2004. pp. 35- 54. DOI: https://doi.org/10.1023/B:WARM.0000015348.88832.52

K. Yoon, C. Hwang. “Manufacturing plant location analysis by multiple attribute decision making: Part I—single-plant strategy”. International Journal of Production Research. Vol. 23. 1985. pp. 345-359. DOI: https://doi.org/10.1080/00207548508904712

R. Rao. “Evaluation of environmentally conscious manufacturing programs using multiple attribute decision-making methods”. Proceedings of the Institution of Mechanical Engineers - Part B - Engineering Manufacture. Vol. 222. 2008. pp. 441- 451. DOI: https://doi.org/10.1243/09544054JEM981

R. Rao, J. Davim. “Decision-making framework model for material selection using a combined multiple attribute decision-making method”. International Journal of Advanced Manufacturing Technology. Vol. 35. 2007. pp. 751-760. DOI: https://doi.org/10.1007/s00170-006-0752-7

A. Bhattacharya, B. Sarkar, S. Mukherjee. “Distance-based consensus method for ABC analysis”. International Journal of Production Research. Vol. 45. 2007. pp. 3405-3420. DOI: https://doi.org/10.1080/00207540600847145

R. Prabhakaran, B. Babu, V. Agrawal. “Optimum selection of a composite product system using MADM”. Materials & Manufacturing Processes. Vol. 21. 2006. pp. 883-891. DOI: https://doi.org/10.1080/10426910600773472

Y. Deng. “Plant location selection based on fuzzy TOPSIS”. International Journal of Advanced Manufacturing Technology. Vol. 28. 2006. pp. 839- 844. DOI: https://doi.org/10.1007/s00170-004-2436-5

R. Rao. “Machinability evaluation of work materials using a combined multiple attribute decision-making method”. International Journal of Advanced Manufacturing Technology. Vol. 28. 2006. pp. 221- 227. DOI: https://doi.org/10.1007/s00170-004-2348-4

H. Byun, K. Lee. “A decision support system for the selection of a rapid prototyping process using the modified TOPSIS method”. International Journal of Advanced Manufacturing Technology. Vol. 26. 2006. pp. 1338-1347. DOI: https://doi.org/10.1007/s00170-004-2099-2

O. Ghrayeb, N. Phojanamongkolkij, R. Marcellus, W. Zhao. “A practical framework to evaluate and select robots for assembly operations”. Journal of Advanced Manufacturing Systems. Vol. 3. 2004. pp. 151-167. DOI: https://doi.org/10.1142/S0219686704000508

M. Yurdakul, C. Çogun. “Development of a multi-attribute selection procedure for non-traditional machining processes”. Proceedings of the Institution of Mechanical Engineers -- Part B -- Engineering Manufacture. Vol. 217. 2003. pp. 993-1009. DOI: https://doi.org/10.1243/09544050360686851

T. Saaty. “Time dependent decision-making; dynamic priorities in the AHP/ANP: Generalizing from points to functions and from real to complex variables”. Mathematical and Computer Modeling. Vol. 46. 2007. pp. 860-891. DOI: https://doi.org/10.1016/j.mcm.2007.03.028

M. Meloun, J. Militky. “Detection of single influential points in OLS regression model building”. Analytica Chimica Acta. Vol. 439. 2001. pp. 169-191. DOI: https://doi.org/10.1016/S0003-2670(01)01040-6

M. Kiang. “A comparative assessment of classification methods”. Decision Support Systems. Vol. 35. 2003. pp. 441-454. DOI: https://doi.org/10.1016/S0167-9236(02)00110-0

H. Nocairi, E. Mostafa, E. Vigneau, D. Bertrand. “Discrimination on latent components with respect to patterns. Application to multi-collinear data”. Computational Statistics and Data Analysis. Vol. 48. 2005. pp. 139-147. DOI: https://doi.org/10.1016/j.csda.2003.09.008

P. Kovács, T. Petres, L. Tóth. “A New Measure of Multi-collinearity in Linear Regression Models”. International Statistical Review. Vol. 73. 2005. pp. 405-412. DOI: https://doi.org/10.1111/j.1751-5823.2005.tb00156.x

W. Hoyt, Z. Imel, F. Chan. “Multiple Regression and Correlation Techniques: Recent Controversies and Best Practices”. Rehabilitation Psychology. Vol. 53. 2008. pp. 321-339. DOI: https://doi.org/10.1037/a0013021

S. Xiang, F. Nie, C. Zhang. “Learning a Mahalanobis distance metric for data clustering and classification”. Pattern Recognition. Vol. 41. 2008. pp. 3600-3612. DOI: https://doi.org/10.1016/j.patcog.2008.05.018

A. Pasini. “A bound for the collinearity graph of certain locally polar geometries”. Journal of Combinatorial Theory, Series A. Vol. 58. 1991. pp. 127-130. DOI: https://doi.org/10.1016/0097-3165(91)90077-T

S. Lipovetsky, W. Conklin. “Multi-objective regression modifications for collinearity”. Computers and Operations Research. Vol. 28. 2001. pp. 1333-1345. DOI: https://doi.org/10.1016/S0305-0548(00)00043-5

D. Montgomery, E. Peck, G. Vining. “Wiley Series in Probability and Statistics Series”. Introduction to linear regression analysis. 5a ed. New York. USA. 2012. pp. 167-193.

R. Johnson, D. Wichern. Applied Multivariate Statistical Analysis. 3rd ed. Ed. Prentice Hall. Englewood Cliffs, New Jersey, USA. 1992. pp. 512- 532.

R. Liu, J. Kuang, Q. Gong, X. Hou. “Principal component regression analysis with SPSS”. Computer Methods and Programs in Biomedicine. Vol. 71. 2003. pp. 141-147. DOI: https://doi.org/10.1016/S0169-2607(02)00058-5

B. Li, A. Morris, E. Martin. “Generalized partial least squares regression based on the penalized minimum norm projection”. Chemometrics and Intelligent Laboratory Systems. Vol. 72. 2004. pp. 21-26. DOI: https://doi.org/10.1016/j.chemolab.2004.01.026

M. Behzadiana, S. Khanmohammadi, M. Yazdanib, J. Ignatiusc. “A state-of the-art survey of TOPSIS applications”. Expert Systems with Applications. Vol. 39. 2012. pp 13051–13069. DOI: https://doi.org/10.1016/j.eswa.2012.05.056

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Publicado

2013-08-16

Cómo citar

Villanueva Ponce, R., & García Alcaraz, J. L. (2013). Evaluación de Tecnología utilizando TOPSIS en Presencia de Multi-colinealidad en Atributos: ¿Por qué usar distancia de Mahalanobis?. Revista Facultad De Ingeniería Universidad De Antioquia, (67), 31–42. https://doi.org/10.17533/udea.redin.16308