The data set for this problem is provided separately in the Excel file

EXCEL FILE:

Aston Business School has one of the most culturally diverse students in the UK. Students often differ from each other in their ethnic background. While this could help to enrich students' learning experiences, in particular during their group work, students often fail harnessing cultural diversity for their learning in such groups. The research in this area has shown that a proper support and a proper environment needs to be established in order for students to reap all the potential benefits of cultural diversity. While at Aston, students are often allocated into syndicate groups to work together on various projects, which are subsequently assessed as a group work. Ideally, an allocation of students into groups should support and encourage harnessing cultural diversity. To achieve this, the groups formed should maximise the diversity of members within groups and minimise the average differences between groups. Groups formed on this basis are called "balanced groups". Creating balanced groups for a large class can be a very tedious and time-consuming administrative task if that process is carried out manually using a trial-and-error approach. For this reason, it would be good to construct a system that would produce balanced groups at a "touch of a button". Your task for this case study will be to prepare the model that will be able to perform automatic allocation of students into groups while satisfying as much as possible these ideal targets: 1. Groups should be of the same size if possible. The ideal size is 5 members per group. 2. Groups should be balanced with respect to gender (G). 3. Groups should be balanced with respect to quantitative background (0). 4. Groups should be balanced with respect to ethnic background but in a very specific way. According to the most up-to-date research on harnessing cultural diversity: Ideally, each international (non-British) student should be paired in the same group with another international student of the same nationality (N). If the above is not possible to achieve, then the next best thing is to pair international students based on the same cultural background (C). Pairing based on the nationality or cultural background is not important to achieve only for home (British) students. The data provided to you contains the records for 45 students with the relevant characteristics for each one of them given through the five columns: Student ID (S), Nationality (N), Cultural Background (C), Gender (G) and Quantitative Background (0). To solve the problem, feel free to use any of the techniques (or any mix of the techniques) we learned in Decision Models. Also, feel free to transform any of the raw data provided to you before you use them in your model, if you think that any such transformation will have more positive than negative effects to the overall quality of your solution. You will need to produce: a model, the final solution obtained from the model, a brief justification of any assumptions you made in the process, and a brief explanation as for what you have managed to achieve with your solution. Your model should be flexible enough to be feasible and to produce a reasonable solution for any other set of 45 students that may have very different characteristics from the ones given), but it does not have to flexible to work well for any other number of students. Student ID (S) Nationality (N) Cultural Background (C) Gender (G) Quantitative Background (e) 101 Saudi Arabian Middle East Female 7.6 102 Saudi Arabian Middle East Male 4.2 103 Romanian Eastern Europe Female 6.2 104 British British Female 9.3 105 British British Male 5.4 106 Chinese East Asia Male 7.8 107 British British Female 8.5 108 Chinese East Asia Female 109 British British Female 110 British British Male 111 Chinese East Asia Female 7.9 112 Chinese East Asia Male 7.9 113 Spanish Western Europe Female 7.6 114 Chinese East Asia Male 8.6 115 Saudi Arabian Middle East Male 116 German Western Europe Female 9.5 117 British British Female 9.1 118 Lithuanian Eastern Europe Male 9.4 119 Russian Eastern Europe Malc 8.5 120 Chinese East Asia Female 7.5 121 Chinese East Asia Female 5.7 122 Mongolian East Asia Male 4.7 123 Chinese East Asia Female 6.1 124 British British Female 9.0 125 Indian South Asia Male 9.0 126 VietnamNet East Asia Male 7.9 127 Iranian Middle East Female 7.7 128 Turkish Middle East Female 4.7 129 Indian South Asia Female 5.5 130 Vietnamise East Asia Female 8.2 131 Vietnamise East Asia Male 8.9 132 Chinese East Asia Female 9.0 133 Chinese East Asia Male 5.9 134 Indian South Asia Female 5.7 135 Chinese East Asia Female 6.2 136 British British Male 9.5 137 Bulgarian Eastern Europe Female 9.0 138 Japanese East Asia Male 5.2 139 Russian Eastern Europe Malc 8.5 140 British British Female 4.8 141 Chinese East Asia Female 7.8 142 Chinese East Asia Female 7.6 143 Chinese East Asia Female 8.1 144 Chinese East Asia Female 6.2 145 Russian Eastern Europe Female 6.5