This paper introduces the multiphase course timetabling problem and presents mathematical formulations effective solution algorithms to solve it in a real case study. Consider pool of lessons number students who are required take subset these graduate. Each lesson consists predetermined consecutive sequence phases with possibly different resource requirements duration. Students must be present ...

We describe the General Examination/Lecture Timetabling Problem (GELTP), which covers a very broad range of real problems faced continually in educational institutions, and we describe how Evolutionary Algorithms (EAs) can be employed to eeectively address arbitrary instances of the GELTP. Some benchmark GELTPs are described, including real and randomly generated problems. Results are presented...

The university course timetabling problem is hard and time-consuming to solve. Profits from full automatisation of this process can be invaluable. This paper describes architecture and operation of two automatic timetabling systems. Both are based on evolutionary algorithms, with specialised genetic operators and penalty-based evaluation function. The paper covers two problem variations (theore...

This paper presents a genetic algorithmic approach to the solution of the problem of personnel timetabling in laboratories in which the objective is to assign tasks to employees and nurse scheduling in medical centre where the objectives are to assign staff to particular day in planning period and minimization of personnel cost by avoiding overtime pay. The personnel scheduling and timetabling ...

This paper extends semidefinite programming relaxations of graph colouring to bounded graph colouring and extensions encountered in timetabling, where room sizes, room features, room stability, and pre-allocated assignments are considered. A matrix-free implementation of an augmented Lagrangian method is presented. Encouraging computational results are reported for conflict graphs from all thre...

The timetabling problem is concerned with the allocation, subject to constraints, of given resources to objects in space and time in such way as to satisfy as nearly as possible a set of desirable objectives. This problem is known to be NP-complete and as such only combinatorial optimization methods can guarantee an optimal timetable. In this paper we propose a sector-based genetic algorithm fo...