University course timetabling using constraint handling rules

Abstract

T imetabling the courses o †ered at the Computer Science Department of the University ofMunich requires the processing of hard and soft constraints. Hard constraints are conditions that must be satisÐed soft constraints, however, may be violated, but should be satisÐed as much as possible. T his paper shows how to model the timetabling problem as a partial constraint satisfaction problem and gives a concise Ðnite domain solver implemented with constraint handling rules that, by performing soft constraint propagation, allows for making soft constraints an active part of the problem ± solving process. Furthermore, efficiency is improved by reusing parts of the timetable of the previous year. T his prototype needs only a few minutes to create a timetable while manual timetabling usually takes a few days. T his was presented at the SystemsÏ98 Computer Fair in Munich and several universities have inquired about it.

As time goes by: Constraint Handling Rules

“…Abdennadher and Marte (2000) have successfully used CHR for scheduling courses at the university of Munich. Their approach is based on a form of soft constraints, implemented in CHR, to deal with teacher's preferences.…”

Extended basic integer programming models for multiple scheduling problems

“…Numerous developments of the mathematical models and algorithms have been done in different areas of application. For instance, according to [4], there are exact and heuristic methods to solve scheduling problems that have been proposed since the 1960s by several authors such as [5], [6], [7] and [8]. Further explanation of exact and heuristic methods can be explained by [5] whereby exact method guarantee on an optimum solution of the problem whereas heuristic method does not promise an optimum solution.…”

Tabu search heuristic for university course timetabling problem

“…We address the course timetabling problem at the University of Dar es salaam (UDSM). Many approaches have been suggested in tackling this problem in other institutions including, Mathematical Programming (Werra 1985, Daskalaki et al 2004, Constraint logic programming (Abdennadher andMichael 1999, Panagiotis 1998), Graph Coloring (Miner et al, 1995), and many heuristic algorithms such as genetic algorithms (Corne et , applied Tabu Search with longer term memory to a course timetabling problem, however their application did not consider room allocation in the optimization strategy. In our application, rooms are also a scarce resource and will have to be included in the optimization strategy.…”