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Machine-Learning–Based Column Selection for Column Generation

Column generation (CG) is widely used for solving large-scale optimization problems. This article presents a new approach based on a machine learning (ML) technique to accelerate CG. This approach, called column selection, applies a learned model to select a subset of the variables (columns) generated at each iteration of CG. The goal is to reduce the computing time spent reoptimizing the restricted master problem at each iteration by selecting the most promising columns. The effectiveness of the approach is demonstrated on two problems: the vehicle and crew scheduling problem and the vehicle routing problem with time windows. The ML model was able to generalize to instances of different sizes, yielding a gain in computing time of up to 30%.

Ketersediaan

Informasi Detail

Judul Seri

TRANSPORTATION SCIENCE; Vol.55 No.3 May-June 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 815 - 831

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MACHINE LEARNING
COLUMN GENERATION
COLUMN SELECTION

Info Detail Spesifik

https://doi.org/10.1287/trsc.2021.1045

Pernyataan Tanggungjawab

Mouad Morabit, Guy Desaulniers, Andrea Lodi

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