Problem

1. MCDA Summer School 2010Case Study -Student selection for last year of Industrial Engineering at Politecnico di Roma Nicolas Albarello, Akram Dehnokhalaji, Sanna Hanhikoski, Lounes Mohamed Mammeri, Mathieu Rivallain, Céline Verly

2. Problem • n applicants for the Industrial Engineering major (2009 n=71, 2010 n=51) • Selection of best students (max 50) • 50 students in 2009, 36 in 2010 • Dividing students in 4 Paths • A homogeneous distribution (gender, quality, also in Paths) • Ensure transparent and fair selection Case Study - Student Selection

3. Criteria • Grades • 3rd and 4th year • Rescaled to 1-5 • Motivation • Maturity/Personality • Professional Project • Knowledge of IE From interview, numbers 1-5 Case Study - Student Selection

4. Post analysis of the 2009 selection • In 2009 data, one main inconsistency : • Gender seems to be taken into account in the selection • An additive value model (without sex) is not able to solve this inconsistency Case Study - Student Selection

5. Preference model inference (1/2) • Monte-Carlo approach • Random weights in weighted sum + optimal selection threshold • Many models but always 1 inconsistency (the one previously presented) • Analysis of inferred models • Jobs are a quite important criteria • Sex is not an important criteria (considering individuals) • DMs give more weight to interviews results (in average) Presentation title – file name

6. Preference model inference (2/2) • Dominance-based Rough Sets Approach • With sex: A set of 10 rules (sometimes discriminatory) permit to fully describe the decision • Without sex: A set of 8 rules permit to describe the decision at 96,7% Sex should not be taken as a student value criteria but as a collective value criteria (at the Major/Paths level) Presentation title – file name

7. Approach • Step 0 • Analyse the current applicants • Step 1 • Pre-selection of students with RPM (Robust Portfolio Modelling) • Step 2 • Ranking the pre-selected students with PROMETHEE • Selecting the required number of students • Step 3 • Assigning students to different paths Case Study - Student Selection

8. Approach - Step 1 • Selecting a portfolio of m students out of n applicants (in 2009 m=50, n=69) • Criteria equally weighted • Number of women between 7 and 10 • Results • Several non-dominated portfolios • 8 students red → eliminated in this phase • Green and yellow students to next step Case Study - Student Selection

9. Approach - Step 1 Case Study - Student Selection

10. Approach - Step 2 • Use of PROMETHEE to rank the selected students from RPM • Equal weights • Usual functions for interview criteria • Linear function for grades (q=1, p=2) • m first students selected Case Study - Student Selection

11. Stochastic method for Paths formation • Random attribution of students to Paths • Evaluation of an objective function (weighted sum) • Minimize the normalized ECART in number of students/value/sex ratio between Paths • Maximize the overall satisfaction of the group (sum of students satisfaction) • Alternative : evolutionary algorithm (better) Path allocation results in 2009 Presentation title – file name

12. Results- Selection 2009 • 49 students selected with our approach were really selected in 2009 • Exception: Difina really selected, our approach would select Quagliata instead Case Study - Student Selection

13. Results – Selection 2010 Case Study - Student Selection

14. Conclusions • Transparent and fair approach • The homogenity of gender taken into account • Students’ wishes taken into account as much as possible Case Study - Student Selection