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A hybrid method for solving timetabling problems based on the evolutionary approach

Norberciak M.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2007

Vol. 11, No 1-2

137-149

Timetabling problems are often difficult and time-consuming to solve. Most of the methods of solving these problems are limited to one problem instance or class. This paper describes a universal method for solving large, highly constrained timetabling problems in various domains. The solution is based on an evolutionary algorithm framework and employs tabu search to quicken the solution finding process. Hyper-heuristics are used to establish the algorithm's operating parameters. The method has been used to solve three timetabling problems with promising results of extensive experiments.

Artificial intelligence technique for planning duties in hospital - preliminary results

Norberciak M.

Journal of Medical Informatics & Technologies

2004

Vol. 7

KB83--90

Scheduling doctors duties in a hospital are complicated and time-consuming tasks. The person responsible for creating a duty timetable is facing one major problem when allocating doctors to time periods: the agreement between several constraining (and often mutually excluding) requirements must be found. In this paper a solution methodology for the monthly duty assignment of doctors is presented. The typical problem is described in detail, along with specific hospital environment, from which datasets for experiments have been taken. A hybrid approach that utilizes strengths of a few artificial intelligence techniques was used to solve the problem. In particular, a population of initial solutions is generated heuristically and then improved using evolutionary algorithm. Experimental results are presented along with a discussion on the computational efficiency, operational acceptability and quality of the solutions.