Timetable Generator

Timetable generator for university schedule implemented in Python using genetic algorithms.

Abstract

This project implements one of possible solutions for generating university schedule. The proposed solution is based on methods of evolutionary computing, uses (1+1) evolutionary strategy and simulated hardening. The success of solution is estimated on fulfillment of given constraints and criteria. Results of testing the algorithm show that all hard constraints are satisfied, while additional criteria are optimized to a certain extent.

Problem

The assignment is to find generic solution that will facilitate generating schedule for university (this specific problem is adjusted to Faculty of Computing in Belgrade).

Each class on faculty is represented as block (lasts arbitrary number of hours, mostly form 1 to 4). For conducting every class required are: teacher, classroom, start time, duration and groups which attend the class. It is also know in advance which groups attend which class and all classrooms are the same size (each group can fit to a classroom).
Teaching is done on faculty from 9AM until 9PM on each work day.

Input data for each class are teachers’ name, subject, type, duration and list of allowed classrooms.
Output data are classroom and time for each class. Time is determined by day (Monday to Friday) and start hour of the class.

Constraints

1. Resources can not overlap timewise
   • No teacher can hold two classes at the same time
   • No group can listen for two classes at the same time
   • No classroom can receive two classes at the same time

   Note: under the term "same time" is not meant only at the beginning of the class, it should be taken into account the duration of the class. If the resource is busy at the moment T1 and the class lasts t1, then the resource can only be re-occupied at the moment T2 = T1 + t1.

2. Class should take place in one of the allowed classrooms
3. If the subject has several forms of teaching, the preferred order for each group is the lectures, exercises, and laboratory exercises.

Constraints 1 and 2 must be met, while the 3rd limit is "soft" and allowed to be violated.
Additional criteria for estimating solution (used also for cost function):

• Fulfill hard constraints (1 & 2)
• Fulfill soft constraints (3)
• Minimize total "idle" for each group (eliminating pause between classes)
• Minimize total "idle time" for each teacher (elimination of pause between classes)
• Provide one hour on a teaching week where no one has classes

Solution

The algorithm for the timetable is represented as table with its columns as classrooms and rows allowed times for classes, while the elements of the table are the specific class held in proper classroom and given time. Table is a matrix with dimensions 60 x number of classrooms, where 60 corresponds to total number of possible times during the week (5 workdays, 12 hours a day).

Element of matrix can be empty if there is no teaching held in given time and classroom or it can contain appropriate class index.
Index of the class is determined by: teachers’ name, subject, type (lectures, exercises or laboratory exercises), duration, allowed classrooms and groups. Because of different duration of classes, the same indexes appear in the table multiple times and are always in vertical blocks (they take consecutive fields in the same column).

The representation of the schedule is on the following picture.

Basic idea of this approach is simplicity in changing terms and classrooms by just changing the block in the matrix taken by the class. Schedule matrix allows that one of the hard constraints is always satisfied, while calculating the cost is easily computed. Because of the way of placing classes in matrix it is ensured that at most one class is at one classroom at any given time, the algorithm will never put a class in non-empty field.

Also, the checkout if the class is in the proper classroom is just examining if the class can be in that column. Overlaps of teachers and groups are examined by checking the rest of the classes which are in the same row. Soft constraint and additional criteria, such as idle for groups are calculated based on additional structures in linear time.

Besides general idea to solving the problem, for optimisation and fulfillment of constraints the two methods of evolutionary computing are used, (1+1) evolutionary strategy and simulated hardening.

Algorithm

The algorithm consists of the following steps:

1. Loading and processing data
   The classes are loaded from the JSON file and are allocated to the appropriate structures. Also, random data mixing is done.
2. Model setting
   Creation of schedule matrix and dictionaries representing empty fields, filled fields in a matrix, order of classes for each type of class and group, an empty group idle, and an empty teacher's idle.
3. Generating the initial population
   Classes are arranged in vertically consecutive blank spaces of the matrix so that they are in the appropriate classroom.
4. (1 + 1) evolution strategy for hard constraints
   The selection of classes that change the place in the schedule is based on the cost of the classes calculating only the hard constraints. Classes with the highest cost are chosen and with a certain probability mutated. Mutation is changing the field in the matrix by looking for a free field that meets all the rigid constraints. Schwefel's notation is also used.
5. Simulated hardening for additional criteria
   Optimizes the previously generated schedule by minimizing the idle time for groups for all days and the existence of free hour in which there is no teaching. In each iteration, a random number of classes are mututed, i.e. they change the place in the matrix so they continue to meet hard constraints. For the cooling schedule, a geometric temperature drop is used.
6. Representing schedule and statistics

Testing & Results

The algorithm was tested on 3 different schedules (input1.txt, input2.txt, input3.txt) and for each ran 10 times.

Class allocation is first performed by an evolutionary algorithm, where the sigma parameter takes the value 2, the number of runs 5, while the maximum stagnation is 200. After that, the schedule is optimized by the simulated hardening algorithm where the initial temperature is 0.5, and the number of iterations is 2500.

The results of all runs are shown in Table 1. Also, for each input file, one schedule has been selected and detailed statistics is shown in Table 2. This schedule was selected based on the lowest average idle time for groups and professors, subjected to the availability of existence of free hour in the week.

Table 1: Statistics obtained by average of 10 test runs.

Table 2: Statistics of best schedules out of 10 test runs.

Use

Running scheduler.py runs the code executing the algorithm for the data that is loaded from the file whose path is in the main function in the same file.