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objective function, given in expression 1, maximizes the total utility of the students, assignments may lead to an imbalanced workload for the members of staff as some topics tend to be significantly more popular than others and therefore receive higher weight assignments wij .

In the studied case of the real world problem, four members of staff B = {B1, . . . , B4} **are involved,**
with three persons supporting three topics each (B1 = {1, 2, 3}, B1 = {4, 5, 6}, B1 = {7, 8, 9}**) and**
one person being responsible for six topics (B4 = {10, . . . , 15}**). In order to balance the assignment**
of students, the difference between the lecturer with the most **load and the member of staff with the**
lowest load is minimized. The load is here expressed as the ratio between the number of assigned students to the number of available spaces of the specific lecturer, given in expression (6). The problem is then solved by identifying all optimal alternatives in the sense of Pareto-optimality.

min $\text{IMBALANCE}=\left(\max_{B_{k}\in\mathcal{B}}\left(\frac{\sum_{i=1}^{n}\sum_{j\in B_{k}}x_{ij}}{\sum_{j\in B_{k}}b_{j}}\right)-\min_{B_{k}\in\mathcal{B}}\left(\frac{\sum_{i=1}^{n}\sum_{j\in B_{k}}x_{ij}}{\sum_{j\in B_{k}}b_{j}}\right)\right)$ (6)

The efficient solutions are plotted in outcome space in figure 2. It can be shown that in addition to the optimal alternatives for the optimality criterion of **maximizing the total utility, other efficient** points exist that are better balanced while being slightly worse in terms of their utility. A total of 139 distinct alternatives possessing values of UTILITY = 279 and IMBALANCE = 0.055 have been identified, 29 alternatives for the outcome of 281/0.222, and 13 for the values 282/0.333.