摘要

This study describes the use of quadratically constrained linear programming with box and complementarity constraints, combined with a relative 1-norm distance measure, to determine the extent to which the relative weights (w) attached to three sustainability criteria (economic, social, and environmental features) could affect the choice of projects to be implemented. To do so, this paper analyzes alternative proportions of the total projects (m) that should be implemented (e.g., 50% and 25% of the total number of projects) as well as alternative standards (c) to be achieved, on average, for some indices (e.g., 100% and 150% of the average standard values, which represent the mean value of these indices for all projects). The overall analytical results are presented for both linearly and exponentially weighted constraints, using partial derivatives to perform local sensitivity analyses (i.e., for each selected or rejected project), and the results account for the effects of w, c, and m. Next, level curves are prepared over the whole domain for w to produce two-dimensional graphs that support a global sensitivity analysis (i.e., for all selected and rejected projects) and to account for the effects of w, c, and m for both linearly and exponentially weighted constraints. Application of this approach to the Iraqi national irrigation system as a case study showed that the results are less robust if a smaller proportion (25%) of the total projects is chosen, with a change of up to 30% in the projects selected. In this context, an increase in the smallest weights for sustainability criteria also affected project choices. If the average standard to be achieved is made stricter (150% of the average standard), the results become more robust, with a change of less than 5% in the selected projects. In this context, increases in the smallest weights for sustainability criteria did not affect the project choices. The results were less robust with linearly weighted constraints than with exponentially weighted constraints.

  • 出版日期2016-12