# A New Exam Scheduling Algorithm Using Graph Coloring

Exam scheduling is a challenging task that universities and colleges face several times every year. The challenge is to schedule so many exams of courses in a limited, and usually short, period of time. An Exam schedule should avoid conflicts, in the sense that no two or more exams for the same student are scheduled at the same time. Part of the challenge is to achieve fairness for the students.

A fair schedule does not schedule more than two exams, for example for a student on one day. In the meantime, a fair schedule does not leave a big gap between exams for the students.

## Overview

An undirected graph $G$ is an ordered pair $(V, E)$ where $V$ is a set of nodes and $E$ is a set of non-directed edges between nodes. Two nodes are said to be adjacent if there is an edge between them. The graph coloring is a well-known problem. Node coloring assigns colors to the nodes of the graph such that no two adjacent nodes have the same color. Edge coloring assigns colors to the edges of the graph such that no two adjacent edges have the same color. Two edges are said to be adjacent if they both share a node in common. General graph coloring algorithms are well known and have been extensively studied by researchers.

Exam scheduling is a challenging task that universities and colleges face several times every year. The challenge is to schedule so many exams of courses in a limited, and usually short, period of time. An Exam schedule should avoid conflicts, in the sense that no two or more exams for the same student are scheduled at the same time. Part of the challenge is to achieve fairness for the students. A fair schedule does not schedule more than two exams, for example for a student on one day. In the meantime, a fair schedule does not leave a big gap between exams for the students. The exam scheduling problem is defined as follows: "We first represent the courses by nodes of a graph, where 2 nodes are adjacent if the 2 corresponding courses are registered by at least one student. Then, it is required to assign each course represented by a node a time slot, such that no two adjacent nodes have the same slot, in condition that a set of constraints imposed on the problem are also met." We solve this problem by using node graph coloring technique.

This study provides a mechanism for automatic exam-schedule generation that achieves fairness, and minimizes the exam period. As a result, this paper presents a graph-coloring-based algorithm for the exam scheduling application which achieves the objectives of fairness, accuracy, and optimal exam time period. Numerous studies have considered the problem of exam scheduling. The main difference between various studies is the set of assumptions and constraints taken into consideration. Burke, Elliman and Weare, for example, followed a similar approach using graph coloring. However, in their algorithm, they addressed only the conflicts without any constraints. Moreover, the algorithm presented in [9] does not eliminate conflicts, and only aims at minimizing conflicts. In this paper, we consider few but important assumptions and constraints, closely related to the general exam scheduling, and mainly driven from the real life requirements collected through the experience at various universities. Such assumptions and constraints are distinct from those present in more general graph coloring problems. We summarize the main assumptions and constraints as follows:

1. The number of exam periods per day (Time Slots ($TS$)) can be set by the user. TS depend on college/department specific constraints. For example, a university that uses a 2-hours exam period and begins the exam day at 8:00 am and finish at 8:00 pm, may set $TS$ to 5.
2. The number of concurrent exam sessions or concurrency level ($N_p$) depends on the number of available halls, and the availability of faculty to conduct the exams. Np is determined by the registrar's office. This paper assumes that $N_p$ is a system parameter and the scheduling algorithm has been examined with several $N_p$ values.
3. A student shall not have more than ($y$) exams per day (fairness requirement), and is treated as a system tunable parameter.
4. A student shall not have a gap of more than ($x$) days between two successive exams, and this factor is to be determined by the college or department (another fairness requirement).
5. The schedule shall be done in the minimal possible period of time, i.e., minimize the number of exam slots and/or number of exam days. The exam time period is an outcome of the scheduling algorithm.
6. Next, we give some more definitions that are relevant to the underlined problem. Let $C$ be a list of all courses to be scheduled. The length of this list is $n$. In other words, $n$ is the number of courses in the list. A course at position $i$ in the list $C$ is referred to using an index $c_i$. Let $G$ be the graph that represents the list $C$ of courses. We impose a weight $w_{ij}$ to each edge of $G$, where $w_{ij}$ is defined as the number of students present in both courses $c_i$ and $c_j$. An edge $e_{ij}$ exists between nodes $c_i$ and $c_j$ iff $w_{ij}$ is not $0$. We define a weight matrix $W$ to be an $nxn$ matrix, where $n$ is the number of courses to be scheduled for the exams, and $wij$ equals the weight of the edge $e_{ij}$ that joins the courses $c_i$ and $c_j$. Such a weight imposed on the edges of $G$ represents the exam conflict complexity present in courses $c_i$ and $c_j$. A multi-section course is considered as one course. However, the number of sections per course is taken into consideration in the process of hall assignment.

The degree $d_i$ of a node $c_i$ is defined as the number of edges connected to a node. A large degree of a node $c_i$ indicates that there is a large number of students registered in this course and $d_i$ other courses. The degree $d_i$ is also a measure of conflict complexity. An example of a weighted graph $G$ and the corresponding weight matrix $W$ is given in Figure 1 and , respectively. In Figure 1, $c_2$ and $c_5$ both have degree $3$. In Table 1, the weight of the edge $e_{15}$ is $4$.

$C1$$C2$$C3$$C4$$C5$
$C1$$0$$2$$0$$0$$4 C2$$2$$0$$1$$0$$3$
$C3$$0$$1$$0$$4$$0 C4$$0$$0$$4$$0$$3$

## Algorithm Color Schedule

The algorithm consists of two major steps. The first step builds the weight matrix and graph. The second step assigns colors to the nodes of the graph.

## Conclusion and Future Work

As discussed above, the number of concurrent exam sessions or concurrency level (Np) depends on the number of available halls, and the availability of faculty to conduct the exams. The value of Np is usually determined by the registrar's office, and the paper assumes that Np is a system parameter, and we will run the scheduling algorithm with several Np values. In a later work, the actual distribution of exam sessions to halls will be included. Also, the algorithm presented in this paper is claimed to achieve near optimal performance (close to minimal number of colors) in polynomial time. We are currently investigating a modification of the algorithm, which will achieve the absolute minimal for a certain set of graphs.

Love,
Ahmed Jazzar