# Large neighborhood improvements for solving car sequencing problems

Bertrand Estellon; Frédéric Gardi; Karim Nouioua

RAIRO - Operations Research (2007)

- Volume: 40, Issue: 4, page 355-379
- ISSN: 0399-0559

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topEstellon, Bertrand, Gardi, Frédéric, and Nouioua, Karim. "Large neighborhood improvements for solving car sequencing problems ." RAIRO - Operations Research 40.4 (2007): 355-379. <http://eudml.org/doc/105354>.

@article{Estellon2007,

abstract = {
The NP-hard problem of car sequencing has received a lot of attention these last years. Whereas a direct
approach based on integer programming or constraint programming is generally fruitless when the number of vehicles to
sequence exceeds the hundred, several heuristics have shown their efficiency. In this paper, very large-scale
neighborhood improvement techniques based on integer programming and linear assignment are presented for solving car
sequencing problems. The effectiveness of this approach is demonstrated through an experimental study made on seminal
CSPlib's benchmarks.
},

author = {Estellon, Bertrand, Gardi, Frédéric, Nouioua, Karim},

journal = {RAIRO - Operations Research},

keywords = {Combinatorial optimization; car sequencing/scheduling; very large-scale neighborhood search; integer
programming; assignment.; assignment},

language = {eng},

month = {2},

number = {4},

pages = {355-379},

publisher = {EDP Sciences},

title = {Large neighborhood improvements for solving car sequencing problems },

url = {http://eudml.org/doc/105354},

volume = {40},

year = {2007},

}

TY - JOUR

AU - Estellon, Bertrand

AU - Gardi, Frédéric

AU - Nouioua, Karim

TI - Large neighborhood improvements for solving car sequencing problems

JO - RAIRO - Operations Research

DA - 2007/2//

PB - EDP Sciences

VL - 40

IS - 4

SP - 355

EP - 379

AB -
The NP-hard problem of car sequencing has received a lot of attention these last years. Whereas a direct
approach based on integer programming or constraint programming is generally fruitless when the number of vehicles to
sequence exceeds the hundred, several heuristics have shown their efficiency. In this paper, very large-scale
neighborhood improvement techniques based on integer programming and linear assignment are presented for solving car
sequencing problems. The effectiveness of this approach is demonstrated through an experimental study made on seminal
CSPlib's benchmarks.

LA - eng

KW - Combinatorial optimization; car sequencing/scheduling; very large-scale neighborhood search; integer
programming; assignment.; assignment

UR - http://eudml.org/doc/105354

ER -

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