# Maximising Preference

# Model introduction

Consider a situation where an instructor wishes to divide students into groups. Each group will be allocated a topic \(t\), from a pool of topics \(1,\ldots,T\). However, each project team assigned to topic would comprise \(B\) sub-groups or sub-teams. Thus, in essence, there would be \(BT\) topics to be assigned to student groups. It is also possible that each topic \(t\) is repeated \(R_t\) times across the class. Note that the more common case, where there is only one sub-group per topic, can be easily attained by setting \(B=1\).

In total, there are \(N\) students in the class. Suppose that students form their own groups, which they submit through a survey form. In total there are \(G\) groups; each student appears in exactly 1 group. We let \(n_g\) represent the number of students in group \(g\), where \(g\) runs from \(1,\ldots,G\).

Finally, suppose we also have the preference that each self-formed group has for a particular topic \(t\), where \(t \in \{1, \ldots, BT \}\).

This model allows you to maximise the preference scores for each group.

# Objective function

\[ \max \sum_{g=1}^G \sum_{t=1}^{BT} \sum_{r=1}^{R_t} x_{gtr} \cdot n_g \cdot p_{tg} \]

where \(p_{tg}\) corresponds to the preference score that group \(g\) has for topic \(t\). Since our objective function is formulated as a maximum, the preference scores should be coded such that higher scores indicate stronger preference for a topic.

The decision variable \(x_{gtr}\) is a binary variable.

\[\begin{equation} x_{gtr} = \begin{cases} 1 & \text{if group $g$ is assigned to repetition $r$ of topic $t$} \\ 0 & \text{otherwise} \end{cases} \end{equation}\]

# Constraints

## Group to topic-repetition combination

The first constraint ensures that each group is assigned to exactly one topic \(t\), where \(t \in \{1, \; \ldots, \; BT \}\).

\[ \sum_{t=1}^{BT} \sum_{r=1}^{R_t} x_{gtr} = 1, \quad \forall g \]

## Number of repetitions per topic

This set of constraints serve to regulate the total number of repetitions for each topic. \(r_{min}\) and \(r_{max} = R_t\) are input variables that the instructor needs to set.

\(a_{tr}\) is a binary decision variable which indicates if repetition \(r\) of topic \(t\) is “live”, where \(r \in \{1 , \ldots, R_t\}\).

\[\begin{eqnarray} a_{tr} &\ge& x_{gtr}, \quad \forall t \in \{1,2,\ldots,BT \},\;r \\ a_{tr} &\le& \sum_{g=1}^G x_{gtr}, \quad \forall t \in \{1,2,\ldots,BT \},\;r \\ a_{tr} &\ge& r_{min}, \quad \forall t \in \{1,2,\ldots,T \} \end{eqnarray}\]

## Balanced number of subgroups

The next constraint ensures that there is an equal number of subgroups for each “live” repetition of a topic. \[ \sum_{r=1}^{R} a_{tr} = \sum_{r=1}^R a_{(bT+t)r}, \quad \forall t \in \{1,2,\ldots,T \},\; \min(1,B-1)\le b \le \max(0, B-1) \]

This is where we can see that the ordering of all sub-groups in the preference matrix should be as follows:

\[ T_1S_1,\; T_2S_1,\; \ldots, \; T_1S_2, T_2S_2, \; \ldots T_TS_B \]

## Number of students per subgroup

A similar set of constraints are used to bound the number of students in each eventually assigned group.

\[\begin{eqnarray} \sum_{i=1}^N \sum_{g=1}^G m_{ig} \cdot x_{gtr} &\ge& a_{tr} \cdot n_{tr}^{min}, \quad \forall t \in \{1,2,\ldots,BT \},\;r \\ \sum_{i=1}^N \sum_{g=1}^G m_{ig} \cdot x_{gtr} &\le& a_{tr} \cdot n_{tr}^{max}, \quad \forall t \in \{1,2,\ldots,BT \},\;r \end{eqnarray}\]

## Binary and non-negativity constraints

Finally, as stated above, we have the following constraints on the decision variables.

\[\begin{eqnarray} x_{gtr} &\in& \{0,1\} \\ a_{tr} &\in& \{0,1\} \end{eqnarray}\]