Multi-criteria method Sample Clauses

Multi-criteria method. In this step, the impact of alternatives on the stakeholders is evaluated through the use of an appropriate MCDA methodology. Numerous methodologies are suited to fit the MAMCA but within DIT4TraM, the methodology of TOPSIS (Xxxxx & Xxxx, 1981) which stands for ‘Technique of Order Preference Similarity to the Ideal Solution’ was selected. The criteria weights calculated in the previous step will be used within TOPSIS, along with the performances of the scenarios in the different KPIs, in order to create a complete ranking of the acceptance of the proposed use cases for every stakeholder group (for each pilot). The TOPSIS method requires minimal user input and its output is easily interpretable. The only subjective parameters are the weights associated with the criteria. It has been successfully performed multiple times in transport related decision-making problems in the past (Xxxxxxxxxx & Xxxxxxxx, 2020). A short description of the methodology by Xxxxxxxx & Nemery (2013) can be found next. “The fundamental idea of TOPSIS is that the best solution is the one which has the shortest distance to the ideal solution and the furthest distance from the anti-ideal solution. For example, in Figure 5, where both criteria are to be maximised, alternative B is closer to the ideal solution than A and further from the anti-ideal solution if the criteria weights are equivalent. As a result, TOPSIS presents alternative B as a better solution than alternative A. DIT4TraM_D8.1_Impact assessment framework_v3.0 30 Figure 5. TOPSIS methodology (Xxxxxxxx & Nemery, 2013) The TOPSIS method is based on five computation steps. The first step is the gathering of the performances of the alternatives on the different criteria. These performances need to be normalised in the second step. The normalised scores are then weighted and the distances to an ideal and anti-ideal point are calculated. Finally, the closeness is given by the ratio of these distances. These five steps are explained in more detail below. The performances of n alternatives a with respect to m criteria i are collected in a decision matrix X = (Xia) as in where i = 1, . . ., m and a = 1, . . ., n.
Sample 1
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