The purpose of this tool is to generate examination timetables. It was developped to assist people who makes examination timetables for the University of Mons. The examination timetables generation is a well-known NP-Complete problem in computer sciences. In order to solve this problem we will use heuristic algorithms. Some references are presents at the bottom of this document.

T, set of time periods (a day has two time periods, morning and afternoon) E, set of exams C, set of classes (a class is a group of students writing an exam e) P, set of professors R, set of rooms

An examination timetable generation problem consists of allocate a time period t for each exam e in E in an appropriate room r in R (in terms of capacity or features for example). To do this, we introduce some constraints. We differentiate hard and soft constraints. The first ones can not be violated to have a feasible solution. Soft constraints can be violated, but in this case a penalty is applied. The best solution is the solution with the lowest penalty. An optimal solution is a solution with no penalty.

Constraints & feasible timetable construction are described in the lib/ directory. It also contains the code source for the feasible timetable construction.

You can compile the code using the Makefile in the root of this directory. This Makefile compiles the source included in lib/ directory.

[1] Asmuni, Hishammudin and Burke, Edmund K and Garibaldi, Jonathan M and McCollum, Barry and Parkes, Andrew J, An investigation of fuzzy multiple heuristic orderings in the construction of university examination timetables, Computers & Operations Research 2009

[2] Qu, Rong and Burke, EK and McCollum, Barry and Merlot, Liam TG and Lee, Sau Y, A survey of search methodologies and automated approaches for examination timetabling, Computer Science Technical Report No. NOTTCS-TR-2006-4, UK, 2006

[3] Gogos, Christos and Alefragis, Panayiotis and Housos, Efthymios, An improved multi-staged algorithmic process for the solution of the examination timetabling problem, Annals of Operations Research 194, 2012