Andrea Lodi

2024-05-01

Computers & Operations Research (publié)

Increasing schedule reliability in the multiple depot vehicle scheduling problem with stochastic travel time

L'ea Ricard

Guy Desaulniers

Louis-Martin Rousseau

2024-04-01

Omega (publié)

Reinforcement learning for freight booking control problems

Justin Dumouchelle

Emma Frejinger

2024-03-16

Journal of Revenue and Pricing Management (publié)

An Exact Method for (Constrained) Assortment Optimization Problems with Product Costs

Markus Leitner

Roberto Roberti

Claudio Sole

2024-03-01

INFORMS Journal on Computing (publié)

Recovering Dantzig–Wolfe Bounds by Cutting Planes

Rui Chen

Oktay Günlük

Leveraging Dantzig–Wolfe Decomposition in the Original Variable Space for Mixed-Integer Programming Dantzig–Wolfe decomposition has been… (voir plus) extensively applied to solve large-scale mixed-integer programs with decomposable structures, leading to exact solution approaches, such as branch and price. However, these approaches would require solving the problem in an extended variable space and are not readily present in off-the-shelf solvers. In “Recovering Dantzig–Wolfe Bounds by Cutting Planes,” Chen, Günlük, and Lodi propose a computational effective approach for generating cutting planes from Dantzig–Wolfe decomposition to enhance branch and cut in the space of original variables. The proposed approach requires a relatively small number of cutting planes to recover the strength of the Dantzig–Wolfe dual bound and should be easy to implement in general-purpose mixed-integer programming solvers. The authors show that these cutting planes typically lead to a formulation with lower dual degeneracy and hence, a better computational performance than naïve approaches, such as the objective function cut.

2024-01-03

Operations Research (publié)

Operational Research: methods and applications

Fotios Petropoulos

Gilbert Laporte

Emel Aktas

Sibel A. Alumur

Claudia Archetti

Hayriye Ayhan

Maria Battarra

Julia A. Bennell

Jean-Marie Bourjolly

John E. Boylan

Michèle Breton

David Canca

Laurent Charlin

Bo Chen

Cihan Tugrul Cicek

Louis Anthony Cox

Christine S.M. Currie

Erik Demeulemeester

Li Ding

Stephen M. Disney … (voir 62 de plus)

Matthias Ehrgott

Martin J. Eppler

Güneş Erdoğan

Bernard Fortz

L. Alberto Franco

Jens Frische

Salvatore Greco

Amanda J. Gregory

Raimo P. Hämäläinen

Willy Herroelen

Mike Hewitt

Jan Holmström

John N. Hooker

Tuğçe Işık

Jill Johnes

Bahar Y. Kara

Özlem Karsu

Katherine Kent

Charlotte Köhler

Martin Kunc

Yong-Hong Kuo

Judit Lienert

Adam N. Letchford

Janny Leung

Dong Li

Haitao Li

Ivana Ljubić

Sebastián Lozano

Virginie Lurkin

Silvano Martello

Ian G. McHale

Gerald Midgley

John D.W. Morecroft

Akshay Mutha

Ceyda Oğuz

Sanja Petrovic

Ulrich Pferschy

Harilaos N. Psaraftis

Sam Rose

Lauri Saarinen

Said Salhi

Jing-Sheng Song

Dimitrios Sotiros

Kathryn E. Stecke

Arne K. Strauss

İstenç Tarhan

Clemens Thielen

Paolo Toth

Greet Vanden Berghe

Christos Vasilakis

Vikrant Vaze

Daniele Vigo

Kai Virtanen

Xun Wang

Rafał Weron

Leroy White

Tom Van Woensel

Mike Yearworth

E. Alper Yıldırım

Georges Zaccour

Xuying Zhao

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a … (voir plus)diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes.

2023-12-27

Journal of the Operational Research Society (publié)

When Nash Meets Stackelberg

Margarida Carvalho

Gabriele Dragotto

Felipe Feijoo

Sriram Sankaranarayanan

2023-12-22

Management Science (publié)

Deep Neural Networks pruning via the Structured Perspective Regularization

Matteo Cacciola

Antonio Frangioni

Xinlin Li

2023-12-05

SIAM Journal on Mathematics of Data Science (publié)

A framework for fair decision-making over time with time-invariant utilities

Sriram Sankaranarayanan

Guanyi Wang

2023-12-01

European Journal of Operational Research (publié)

A machine learning framework for neighbor generation in metaheuristic search

De-You Liu

Defeng Liu

Vincent Perreault

Alain Hertz

This paper presents a methodology for integrating machine learning techniques into metaheuristics for solving combinatorial optimization pro… (voir plus)blems. Namely, we propose a general machine learning framework for neighbor generation in metaheuristic search. We first define an efficient neighborhood structure constructed by applying a transformation to a selected subset of variables from the current solution. Then, the key of the proposed methodology is to generate promising neighbors by selecting a proper subset of variables that contains a descent of the objective in the solution space. To learn a good variable selection strategy, we formulate the problem as a classification task that exploits structural information from the characteristics of the problem and from high-quality solutions. We validate our methodology on two metaheuristic applications: a Tabu Search scheme for solving a Wireless Network Optimization problem and a Large Neighborhood Search heuristic for solving Mixed-Integer Programs. The experimental results show that our approach is able to achieve a satisfactory trade-offs between the exploration of a larger solution space and the exploitation of high-quality solution regions on both applications.

2023-07-26

Frontiers in Applied Mathematics and Statistics (publié)

Structured Pruning of Neural Networks for Constraints Learning

Matteo Cacciola

Antonio Frangioni

In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity across diverse app… (voir plus)lications, including cancer treatment, algorithmic configuration, and chemical process optimization. In this domain, the combination of ML and OR often relies on representing the ML model output using Mixed Integer Programming (MIP) formulations. Numerous studies in the literature have developed such formulations for many ML predictors, with a particular emphasis on Artificial Neural Networks (ANNs) due to their significant interest in many applications. However, ANNs frequently contain a large number of parameters, resulting in MIP formulations that are impractical to solve, thereby impeding scalability. In fact, the ML community has already introduced several techniques to reduce the parameter count of ANNs without compromising their performance, since the substantial size of modern ANNs presents challenges for ML applications as it significantly impacts computational efforts during training and necessitates significant memory resources for storage. In this paper, we showcase the effectiveness of pruning, one of these techniques, when applied to ANNs prior to their integration into MIPs. By pruning the ANN, we achieve significant improvements in the speed of the solution process. We discuss why pruning is more suitable in this context compared to other ML compression techniques, and we identify the most appropriate pruning strategies. To highlight the potential of this approach, we conduct experiments using feed-forward neural networks with multiple layers to construct adversarial examples. Our results demonstrate that pruning offers remarkable reductions in solution times without hindering the quality of the final decision, enabling the resolution of previously unsolvable instances.

2023-07-14

ArXiv (preprint)

Structured Pruning of Neural Networks for Constraints Learning

Matteo Cacciola

Antonio Frangioni

In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity across diverse app… (voir plus)lications, including cancer treatment, algorithmic configuration, and chemical process optimization. In this domain, the combination of ML and OR often relies on representing the ML model output using Mixed Integer Programming (MIP) formulations. Numerous studies in the literature have developed such formulations for many ML predictors, with a particular emphasis on Artificial Neural Networks (ANNs) due to their significant interest in many applications. However, ANNs frequently contain a large number of parameters, resulting in MIP formulations that are impractical to solve, thereby impeding scalability. In fact, the ML community has already introduced several techniques to reduce the parameter count of ANNs without compromising their performance, since the substantial size of modern ANNs presents challenges for ML applications as it significantly impacts computational efforts during training and necessitates significant memory resources for storage. In this paper, we showcase the effectiveness of pruning, one of these techniques, when applied to ANNs prior to their integration into MIPs. By pruning the ANN, we achieve significant improvements in the speed of the solution process. We discuss why pruning is more suitable in this context compared to other ML compression techniques, and we identify the most appropriate pruning strategies. To highlight the potential of this approach, we conduct experiments using feed-forward neural networks with multiple layers to construct adversarial examples. Our results demonstrate that pruning offers remarkable reductions in solution times without hindering the quality of the final decision, enabling the resolution of previously unsolvable instances.

2023-07-14

ArXiv (prépublication)