Choosing among weight-estimation methods for multi-criterion analysis: A case study for the design of multi-purpose offshore platforms |
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| Affiliation: | 1. McKetta Department of Chemical Engineering, University of Texas, 200 E. Dean Keeton St., Austin, TX 78712, USA;2. Energy Institute at the University of Texas, 2304 Whitis Avenue, Austin, TX 78712, USA;1. Department of Mathematics, Kohat University of Science & Technology, Kohat 26000, Khyber Pukhtunkhwa, Pakistan;2. Department of Mathematical Sciences, University of Essex, Wivenhoe Park, CO4 3SQ Colchester, UK;1. State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China;2. School of Nature Conservation, Beijing Forestry University, Beijing 100083, China;3. water@leeds, School of Civil Engineering, University of Leeds, Leeds LS2 9JT, United Kingdom;4. Swiss Federal Institute of Aquatic Science and Technology, Ueberlandstrasse 133, Duebendorf 8600, Switzerland;1. School of Information Science & Engineering, Central South University, Changsha, Hunan 410083, China;2. Collaborative Innovation Center of Resource-Conserving & Environment-Friendly Society and Ecological Civilization, Changsha, Hunan 410083, China;3. School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha, Hunan 410004, China;4. School of Business, Central South University, Changsha, Hunan 410083, China |
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| Abstract: | Application of the sustainability concept to environmental projects implies that at least three feature categories (i.e., economic, social, and environmental) must be taken into account by applying a participative multi-criterion analysis (MCA). However, MCA results depend crucially on the methodology applied to estimate the relative criterion weights. By using a logically consistent set of data and methods (i.e., linear regression LR], factor analysis FA], the revised Simos procedure RSP], and the analytical hierarchy process AHP]), the present study revealed that mistakes from using one weight-estimation method rather than an alternative are non-significant in terms of satisfaction of specified acceptable standards (i.e., a risk of up to 1% of erroneously rejecting an option), but significant for comparisons between options (i.e., a risk of up to 11% of choosing a worse option by rejecting a better option). In particular, the risks of these mistakes are larger if both differences in statistical or computational algorithms and in data sets are involved (e.g., LR vs. AHP). In addition, the present study revealed that the choice of weight-estimation methods should depend on the estimated and normalised score differences for the economic, social, and environmental features. However, on average, some pairs of weight-estimation methods are more similar (e.g., AHP vs. RSP and LR vs. AHP are the most and the least similar, respectively), and some single weight-estimation methods are more reliable (i.e., FA > RSP > AHP > LR). |
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| Keywords: | Multi-criterion analysis Weighting Linear regression Factor analysis Revised Simos procedure Analytical hierarchy process |
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