University Course Timetabling with Soft Constraints Hana Rudova, Keith Murray Presented by: Marlien Edward

Introducing the Authors Hana Rudova : Faculty of Informatics, Masaryk University Czech Republic Keith Murray: Space Management and Academic Scheduling, Purdue University, USA

Introduction  Using CLP to construct a huge automated timetabling system at Purdue university  Declarative nature of problem description  Constraint propagation technique for reducing search space  Features of the problem  Demand driven  Different variables have different preferences  Over constrained

Problem Description  Class combination meeting student’s needs  Classroom allocation respecting both instructional requirements and faculty preferences (e.g. time requirements or class preference)  Students sectioning to be easy to measure the desirability and undesirability of classes overlap

Managing Soft Constraints Preference constraint solver Handles preference values for preference variables  Zero preference: complete satisfaction  Higher preferences: expresses the degree of violation  Values not present in the preference variable domain: infinite preference **The preference solver maintains a cost variable for each preference variable having the best preference variable as its lower bound

Managing Soft Constraints (contd) Types of Soft Constraints: 1.Unary on time variables  faculty time preference 2.Unary on classroom variables  faculty preferences on the classroom selection 3.Binary for joint enrollment between two classes

Searching Aim: is to search for a complete assignment of preference variables to achieve the best satisfaction of all constraints Technique: initial partial assignment of the variables then repair it such that most of the variables are assigned values

Managing Hard Constraints Some useful constraints:  disjoint2 (+Rectangles) To ensure that each class is assigned to just one suitable classroom  cumulative (+Starts, +Durations, +Resources, ?Limit) To ensure that a resource can run several tasks in parallel

Conclusion Solution included:  New solver for soft constraints  Repair search for finding a solution  Some constraints for stronger constraint propagation Future work:  Extension of problem solution  Improving the preference solver  New approach for initial student sectioning