Exploiting Fitness Distance Correlation of Set Covering Problems
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
On complexity of optimal recombination for binary representations of solutions
Evolutionary Computation
A new hybrid heuristic for driver scheduling
International Journal of Hybrid Intelligent Systems - VIII Brazilian Symposium On Neural Networks
International Journal of Computer Integrated Manufacturing - Industrial Engineering and Systems Management
Experimental analysis of optimization techniques on the road passenger transportation problem
Engineering Applications of Artificial Intelligence
An efficient estimation function for the crew scheduling problem
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
A multiobjective GRASP for rule selection
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Very large-scale neighborhood search techniques in timetabling problems
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
GRASP strategies for a bi-objective commercial territory design problem
Journal of Heuristics
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We present new multiobjective metaheuristics for solving real-world crew scheduling problems in public bus transport companies. Since the crews of these companies are drivers, we will designate the problem as bus-driver scheduling. Crew scheduling problems are well known, and several mathematical programming-based techniques have been proposed to solve them, in particular, using the single-objective set-covering formulation. However, in practice, there exists the need to consider multiple objectives, some of them in conflict with each other; for example, the cost and service quality, implying also that alternative solution methods have to be developed. We propose multiobjective metaheuristics based on the tabu search and genetic algorithms. These metaheuristics also present some innovation features related with the structure of the crew scheduling problem that guide the search efficiently and enable them to find good solutions. Some of these new features can also be applied to the development of heuristics to other combinatorial optimization problems. A summary of computational results with real-data problems is presented. The methods have been successfully incorporated in the GIST Planning Transportation Systems and are actually used by several companies.