Quantifying uncertainty using robustness analysis in the application of ORESTE to sewer rehabilitation projects prioritization—Brussels case study

Sewer systems are considered extremely important components of the urban water infrastructure due to their function and capital-intensive nature. These systems, however, have been undergoing aging and deterioration, thus needing repair or rehabilitation. Historically, the budgets for sewer rehabilitation are often scarce and too limited to address the requirements, requiring utility managers to prioritize the competing projects. In this paper, the application of ORESTE to the prioritization of sewer rehabilitation projects for the Brussels, Belgium network was demonstrated. The 43 proposed projects were ranked based on a set of 16 criteria. In addition, a methodology was introduced to investigate the robustness of the ORESTE solution. The inclusion of the robustness analysis into the technique allowed for the quantification of the uncertainties associated with the priority rankings. This type of information is very important in developing confidence among decision makers as to their decision on the priority ranking of sewer rehabilitation projects. Copyright © 2010 John Wiley & Sons, Ltd.

[1]  E. Triantaphyllou,et al.  A Sensitivity Analysis Approach for Some Deterministic Multi-Criteria Decision-Making Methods* , 1997 .

[2]  Luis C. Dias,et al.  Resolving inconsistencies among constraints on the parameters of an MCDA model , 2003, Eur. J. Oper. Res..

[3]  Holger R. Maier,et al.  Reliability-Based Approach to Multicriteria Decision Analysis for Water Resources , 2004 .

[4]  H. Pastijn,et al.  Constructing an outranking relation with ORESTE , 1989 .

[5]  Richard N. Palmer,et al.  Expert System for Prioritizing the Inspection of Sewers: Knowledge Base Formulation and Evaluation , 2002 .

[6]  Samuel T. Ariaratnam,et al.  Financial Outlay Modeling for a Local Sewer Rehabilitation Strategy , 2002 .

[7]  Charles P. Schmidt,et al.  Sensitivity Analysis of Additive Multiattribute Value Models , 1988, Oper. Res..

[8]  Dulcy M. Abraham,et al.  CHALLENGING ISSUES IN MODELING DETERIORATION OF COMBINED SEWERS , 2001 .

[9]  Risto Lahdelma,et al.  SMAA-2: Stochastic Multicriteria Acceptability Analysis for Group Decision Making , 2001, Oper. Res..

[10]  Jeffrey L. Ringuest,et al.  LP-metric sensitivity analysis for single and multi-attribute decision analysis , 1997 .

[11]  Bernard Roy,et al.  Main sources of inaccurate determination, uncertainty and imprecision in decision models , 1989 .

[12]  Oleg I. Larichev,et al.  ZAPROS-LM — A method and system for ordering multiattribute alternatives , 1995 .

[13]  Bertrand Mareschal,et al.  Weight stability intervals in multicriteria decision aid , 1988 .

[14]  Daniel R. Dolk.  Data as models: an approach to implementing model management , 1986 .

[15]  Dana J. Vanier,et al.  Why industry needs asset management tools , 2001 .

[16]  Denis Bouyssou,et al.  Building Criteria: A Prerequisite for MCDA , 1990 .

[17]  Ehsan S. Soofi,et al.  Generalized Entropy-Based Weights for Multiattribute Value Models , 1990, Oper. Res..

[18]  Jon C. Helton,et al.  Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal , 1993 .

[19]  Wolfgang Schilling,et al.  Computer Aided Rehabilitation of Sewer and Storm Water Networks , 2002 .