No CrossRef data available.
Article contents
On Vehicle Evaluation and Design Using Data Envelopment Analysis with Hierarchical Concepts
Part of:
Design Management
Published online by Cambridge University Press: 26 July 2019
Abstract
Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
In recent years, product complexity in terms of function and structure has been driven by technological development in complementary components. Designing unbiased product evaluation metrics being to grasp the complex relationships of product features, and able to capitalize on market needs has become a challenge in industrial practice.
In this paper, we propose a hybrid framework in which evaluation models are generated by integrating Interpretive Structural Modeling (ISM), Hierarchical Clustering and Data Envelopment Analysis (DEA). Whereas ISM constructs hierarchical digraphs (skeletons), Hierarchical Clustering reduces dimensionality of pairwise comparisons (correlations) of design variables, and suggests possible evaluation configurations, and DEA computes weights to provide optimal evaluation metrics. Our computational experiments using more than twenty thousand vehicles from 1982 to 2013 confirmed the feasibility and usefulness of DEA with hierarchical concepts to generate the optimal vehicle evaluation metric, and to suggest configurations for vehicle design layouts.
- Type
- Article
- Information
- Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 1225 - 1234
- Creative Commons
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.- Copyright
- © The Author(s) 2019
References
Barat, M., Tohidi, G. and Sanei, M., 2018, “DEA for nonhomogeneous mixed networks”, Asia Pacific Management Review.Google Scholar
Cantner, U., Krueger, J. J. and Soellnerz, R, “Product quality, product price, and share dynamics in the German compact car market”, Industrial and Corporate Change, Vol. 21 No. 5, pp. 1085–1115Google Scholar
Charnes, A., Cooper, W. and Rhodes, E., 1978, “Measuring the efficiency of decision making units”, European Journal of Operational Research, Vol. 2 No. 6, pp. 429 – 444.Google Scholar
Doyle, J. and Green, R, “Comparing Products using Data Envelopment Analysis”, Omega, Vol. 19 No. 6, pp. 631 – 638.Google Scholar
Fernandez-Castro, A. S. and Smith, P. C., 2002, “Lancaster's characteristics approach revisited: product selection using non-parametric methods”, Managerial and Decision Economics, Vol. 23 No. 2, pp. 83–91.Google Scholar
Førsund, F. R., 2018, “Economic interpretations of DEA”, Socio-Economic Planning Sciences, 61, pp. 9–15. Recent developments on the use of DEA in the public sector.Google Scholar
Gonzalez, E., Carcaba, A. and Ventura, J, “How car dealers adjust prices to reach the product efficiency frontier in the Spanish automobile market”, Omega, 51, pp. 38 – 48.Google Scholar
Han, Y., Geng, Z., Gu, X. and Zhu, Q, “Energy efficiency analysis based on DEA integrated ISM: A case study for Chinese ethylene industries”, Engineering Applications of Artificial Intelligence, Vol. 45, pp. 80–89.Google Scholar
Hsiao, S.-W., Ko, Y.-C., Lo, C.-H. and Chen, S.-H., “An ISM, DEI, and ANP based approach for product family development”, Advanced Engineering Informatics, Vol. 27 No. 1, pp. 131–148Google Scholar
Ishii, T., Parque, V., Miura, S., Miyashita, T, “Definition and support of differentiation and integration in mechanical structure using S-curve theory and wavelet transform. 21st International Conference on Engineering Design, Vol. 6 No. DS87-6, 2017, pp. 355–364Google Scholar
Papagapiou, A., Mingers, J. and Thanassoulis, E., 1997, “Would you buy a used car with DEA?”, OR Insight, Vol. 10 No. 1, Jan, pp. 13–19.Google Scholar
Papahristodoulou, C, “A DEA model to evaluate car efficiency”, Applied Economics, Vol. 29, pp. 1493–1508.Google Scholar
Parque, V., Kobayashi, M., Higashi, M. “Reinforced Explorit on Optimizing Vehicle Powertrains”, International Conference on Neural Information Processing, pp. 579–586Google Scholar
Parque, V. and Miyashita, T, “A method to learn high-performing and novel product layouts and its application to vehicle design”, Neurocomputing, Vol. 248, pp. 41–56 (2017)Google Scholar
Parque, V. and Miyashita, T., 2015, “Learning the Optimal Product Design Through History”, International Conference on Neural Information Processing, pp. 382–389Google Scholar
Parque, V. and Miyashita, T, “On Learning Fuel Consumption Prediction in Vehicle Clusters”, IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), pp. 116 - 121Google Scholar
Parque, V. and Miyashita, T, “On vehicle surrogate learning with genetic programming ensembles”, GECCO ’18 Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1704–1710Google Scholar
Parque, V. and Miyashita, T, “Towards the Succinct Representation of m Out of n”, Internet and Distributed Computing Systems - 11th International Conference, pp. 16–26Google Scholar
Parque, V. and Miyashita, T, “Unranking Combinations Using Gradient-Based Optimization”, IEEE 30th International Conference on Tools with Artificial Intelligence, pp. 579–586Google Scholar
Raut, R. D., Gardas, B. B., Jha, M. K. and Priyadarshinee, P., 2017, “Examining the critical success factors of cloud computing adoption in the msmes by using ism model”, The Journal of High Technology Management Research, Vol. 28 No. 2, pp. 125 – 141.Google Scholar
Seiford, L. M. and Zhu, J., 2003, “Context-dependent data envelopment analysis - measuring attractiveness and progress”, Omega, Vol. 31 No. 5, pp. 397 – 408.Google Scholar
Sueyoshi, T., Yuan, Y. and Goto, M., 2017, “A literature study for DEA applied to energy and environment”, Energy Economics, 62, pp. 104 – 124.Google Scholar
Tavana, M., Li, Z., Mobin, M., Komaki, M. and Teymourian, E., 2016, “Multi-objective control chart design optimization using NSGA-iii and MOPSO enhanced with DEA and topsis”, Expert Systems with Applications, 50, pp. 17 - 39.Google Scholar
Voltes-Dorta, A., Perdiguero, J. and Jiménez, J. L., 2013, “Are car manufacturers on the way to reduce CO2 emissions?”, A DEA approach. Energy Economics, 38, pp. 77 – 86.Google Scholar
Warfield, J. N., 1973a, “On arranging elements of a hierarchy in graphic form”. IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-3 No. 2, March, pp. 121–132.Google Scholar
Warfield, J. N., 1973b, “Binary matrices in system modeling”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-3 No. 5, Sept, pp. 441–449.Google Scholar
Warfield, J. N., 1974a, “Developing subsystem matrices in structural modeling”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-4 No. 1, Jan, pp. 74–80.Google Scholar
Warfield, J. N., 1974b, “Developing interconnection matrices in structural modeling”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-4 No. 1, Jan, pp. 81–87.Google Scholar
Warfield, J. N., 1973c, “Constructing operational value systems for proposed two-unit coalitions”, IEEE Conference on Decision and Control, pp. 204–213.Google Scholar
Warfield, J. N., 1974, “Toward interpretation of complex structural models”. IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-4 No. 5, Sept, pp. 405–417.Google Scholar
You have
Access
Open access