Greedy Genetic Algorithms, Optimizing Mutations and Bus Driver Scheduling

Abstract

Genetic algorithms have been applied to bus driver scheduling and compared to other approaches such as simulated annealing. Bus driver scheduling is a more difficult domain than most genetic algorithm applications. Special purpose genetic algorithms have been developed that search constrained versions of the initial search space. Greedy algorithms are used for crossovers, though these had to be randomized to give good results. Special purpose optimizing mutation improves search in domains too large for traditional mutation to be useful. The greedy genetic algorithm produces schedules typically within a few duties of the optimum solution. Further theoretical analyses are expected to result in new methods that will improve results. The technology developed may also have applications in other areas of transport scheduling.