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**Harvard**

Curry, E. (2018) *Alternative Pricing in Column Generation for Airline Crew Rostering*. Göteborg : Chalmers University of Technology

** BibTeX **

@mastersthesis{

Curry2018,

author={Curry, Emily},

title={Alternative Pricing in Column Generation for Airline Crew Rostering},

abstract={In airline crew rostering, the objective is to create personalized schedules, i.e., rosters,
for a set of crew members. Because of the large number of possible rosters that
could be formed, the problem is solved using column generation, where each column
corresponds to a specific roster. The pricing problem, which is the problem studied
in this thesis, is then defined as to find legal rosters with the potential of improving
the current solution. Since the rules and regulations regarding rosters vary between
airlines, we have chosen to treat the pricing problem as a black-box optimization
problem.
Three different methods for solving the black-box pricing problem have been implemented.
The first method uses binary particle swarm optimization (BPSO) to
search for new rosters. The other two methods use surrogate modeling to fit a nonlinear
surrogate function to a set of sampled rosters using radial basis functions. The
surrogate function was then either linearly approximated, so that a shortest path
problem could be set up and solved, or solved heuristically by a BPSO method.
The three methods have been evaluated on five real-world test cases. For each test
case, a large number of different pricing problems are solved. Our comparison of
the methods’ performance shows that the method using BPSO performed the best,
followed by the surrogate modeling approach without the linear approximation.},

publisher={Institutionen för matematiska vetenskaper, Chalmers tekniska högskola},

place={Göteborg},

year={2018},

}

** RefWorks **

RT Generic

SR Electronic

ID 255713

A1 Curry, Emily

T1 Alternative Pricing in Column Generation for Airline Crew Rostering

YR 2018

AB In airline crew rostering, the objective is to create personalized schedules, i.e., rosters,
for a set of crew members. Because of the large number of possible rosters that
could be formed, the problem is solved using column generation, where each column
corresponds to a specific roster. The pricing problem, which is the problem studied
in this thesis, is then defined as to find legal rosters with the potential of improving
the current solution. Since the rules and regulations regarding rosters vary between
airlines, we have chosen to treat the pricing problem as a black-box optimization
problem.
Three different methods for solving the black-box pricing problem have been implemented.
The first method uses binary particle swarm optimization (BPSO) to
search for new rosters. The other two methods use surrogate modeling to fit a nonlinear
surrogate function to a set of sampled rosters using radial basis functions. The
surrogate function was then either linearly approximated, so that a shortest path
problem could be set up and solved, or solved heuristically by a BPSO method.
The three methods have been evaluated on five real-world test cases. For each test
case, a large number of different pricing problems are solved. Our comparison of
the methods’ performance shows that the method using BPSO performed the best,
followed by the surrogate modeling approach without the linear approximation.

PB Institutionen för matematiska vetenskaper, Chalmers tekniska högskola,

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/255713/255713.pdf

OL 30