Comprehensive Methods for Dealing with Uncertainty in Assessing Sustainability Part 1

Abstract

Integrated Value Method for Sustainability Evaluation (MIVES) is a deterministic method based on requirement trees, value functions, and the Analytic Hierarchy Process. It allows integrating environmental, social, and economic sustainability indicators in a global index. The value functions make it possible to consider non-linearity in the assessment. MIVES takes into account the relative weight of the various model indicators. Deterministic models can cause significant problems in terms of adequately managing project sustainability. A method not only has to estimate the sustainability index at the end of the project. It also has to evaluate the degree of uncertainty that may make it difficult to achieve the sustainability objective. Uncertainty can affect indicators, weights, and value function shapes. This chapter presents a method for sustainability assessment, taking into account uncertainty. It is based on MIVES and the Monte Carlo simulation technique. An example of potential application is proposed, related to power plants.

Conceptual Framework for an Integrated Method to Optimize Sustainability of Engineering Systems

“…The MIVES method is a combination of techniques based on a requirement tree [8,9], value functions [10], and the AHP [11,12]. MIVES is used to transform different types of variables, measured with different …”

“…With more than four, one often loses the overall view and this can lead to inconsistencies, among other potential problems. It is a good idea to use AHP [9,11,12]. This technique helps to organize the process efficiently, reduce its complexity and subjectivity and decrease possible disagreements between the team members.…”

“…If possible, it is recommended to use analytical formulae for modelling correlations. When the correlations are not known in an analytical way, the technique called "correlation among samples" could be used [9].…”

“…Consequently, simple and easy to understand probability distributions should be used: open and closed triangular ones, uniform distributions, Bernoulli and general discrete functions [9], for the potential use of each type of distribution.…”

“…According to the previously defined distribution functions, pseudo-random values will be generated for every probabilistic variable (Phase P4). PRNGs (pseudo-random number generators) and complementary techniques will be used for this purpose, for instance, the inversion (inverse transform method) and acceptance-rejection techniques can be employed [9].…”