Genetic Algorithmic Approach for Personnel Timetabling

This paper presents a genetic algorithmic approach to the solution of the problem of personnel timetabling in which the objective is to assign tasks to employees. The problem is multi-constrained and having huge search space which makes it NP hard. The problem considered is that of the timetabling of laboratory personnel. Genetic algorithm is applied to a problem instance with 14 employees and 9 tasks. Canonical genetic algorithm demonstrates very slow convergence to optimal solution. Hence, a knowledge augmented operator is introduced in genetic algorithm framework. This helps to get the near-optimal solution quickly.

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