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A Genetic Algorithm To Solve The Timetable Problem

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Scheduling and Timetabling Solutions Metaheuristic Optimization Algorithms Research Constraint Satisfaction and Optimization

Abstract

In this paper we present the results of an investigation of the possibilities offered by genetic algorithms to solve the timetable problem. This problem has been chosen since it is representative of the class of multi-constrained, NP-hard, combinatorial optimization problems with real-world application. First we present our model, including the definition of a hierarchical structure for the objective function and the generalized genetic operators which can be applied to matrices representing timetables. Then we report about the outcomes of the utilization of the implemented system to the specific case of the generation of a school timetable. We compare two versions of the genetic algorithm (GA), with and without local search, both to a handmade timetable and to two other approaches based on simulated annealing and tabu search. Our results show that GA with local search and tabu search with relaxation both outperform simulated annealing and handmade timetables. (Introduction) Evolutio...

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References

1985An introduction to timetabling 1991Constructing School Timetables Using Simulated Annealing: Sequential and Parallel Algorithms 1986OR Practice—A Survey of Practical Applications of Examination Timetabling Algorithms 1969An integer linear programming model of a school timetabling problem 1982A classroom/time assignment model

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2007Finding Feasible Timetables Using Group-Based Operators 2016A Utilization-based Genetic Algorithm for Solving the University Timetabling Problem (UGA)2013A greedy gradient-simulated annealing selection hyper-heuristic 2016A Hidden Markov Model Approach to the Problem of Heuristic Selection in Hyper-Heuristics with a Case Study in High School Timetabling Problems 2003Constructing nurse schedules at large hospitals

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