dc.contributor |
Universitat Pompeu Fabra. Departament d'Economia i Empresa |
dc.contributor.author |
Ramalhinho-Lourenço, Helena |
dc.contributor.author |
Pinto, José Luis |
dc.contributor.author |
Portugal, Rita |
dc.date |
1998-07-01 |
dc.identifier.citation |
https://econ-papers.upf.edu/ca/paper.php?id=304 |
dc.identifier.citation |
Transportation Science, 3, 35, (2001), pp. 331-343 |
dc.identifier.uri |
http://hdl.handle.net/10230/1066 |
dc.format |
application/pdf |
dc.language.iso |
eng |
dc.relation |
Economics and Business Working Papers Series; 304 |
dc.rights |
L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.rights |
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject |
metaheuristics |
dc.subject |
bus-driver |
dc.subject |
crew-scheduling |
dc.subject |
tabu-search |
dc.subject |
grasp |
dc.subject |
genetic algorithms |
dc.subject |
Operations Management |
dc.title |
Metaheuristics for the bus-driver scheduling problem |
dc.type |
info:eu-repo/semantics/workingPaper |
dc.description.abstract |
We present new metaheuristics for solving real crew scheduling problems
in a public transportation bus company. Since the crews of these
companies are drivers, we will designate the problem by the bus-driver
scheduling problem. Crew scheduling problems are well known and several
mathematical programming based techniques have been proposed to solve
them, in particular using the set-covering formulation. However, in
practice, there exists the need for improvement in terms of computational
efficiency and capacity of solving large-scale instances. Moreover, the
real bus-driver scheduling problems that we consider can present variant
aspects of the set covering, as for example a different objective
function, implying that alternative solutions methods have to be
developed. We propose metaheuristics based on the following approaches:
GRASP (greedy randomized adaptive search procedure), tabu search and
genetic algorithms. These metaheuristics also present some innovation
features based on and genetic algorithms. These metaheuristics also
present some innovation features based on the structure of the crew
scheduling problem, that guide the search efficiently and able them to
find good solutions. Some of these new features can also be applied in
the development of heuristics to other combinatorial optimization
problems. A summary of computational results with real-data problems is
presented. |