Computer-Aided Scheduling of Public Transport
Computer-Aided Scheduling of Public Transport
-Opt Population Training for Minimization of Open Stack Problem
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Multiobjective Metaheuristics for the Bus Driver Scheduling Problem
Transportation Science
A flexible system for scheduling drivers
Journal of Scheduling
Constructive Genetic Algorithm for Clustering Problems
Evolutionary Computation
Population training heuristics
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
A hybrid column generation approach for the berth allocation problem
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
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This paper describes a new hybrid method based on the application of the Population Training Algorithm (PTA) and linear programming (LP) for generation of schedules for drivers in a public transport system. These methods are applied in an iterative way, where PTA is responsible for the generation of good columns (low cost and good covering of the tasks), and LP for solving a set partitioning problem formed by these columns. The PTA employs heuristics in fitness definition, guiding the population to settle down in search areas where the individuals cannot be improved by such heuristics. The driver schedules are represented by columns in a large-scale set partitioning problem, which are formed when solving the linear programming relaxation. The computational results are compared against a Simulated Annealing metaheuristic using randomly formed instances based on real problems.