Andrea Lodi

. Metaheuristics are widely recognized as efficient approaches for many combinatorial problems. Studies to improve the performance of metahe… (voir plus)uristics have increasingly relied on the use of various methods either combining different metaheuristics or methods originating outside of the metaheuristic field. This paper presents a learning algorithm to improve tabu search by reducing its search space and the evaluation effort. We study the performance of a learning tabu search algorithm using classification methods in an attempt to select moves through the search space more wisely. The experimental results demonstrate the benefit of using a learning mechanism under deterministic and stochastic conditions.

2024-01-01

Comput. Oper. Res. (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.

2024-01-01

J. Oper. Res. Soc. (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 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)

Capacity Planning in Stable Matching: An Application to School Choice

Federico Bobbio

Margarida Carvalho

Ignacio Rios

Alfredo Torrico

Centralized mechanisms are becoming the standard approach to solve several assignment problems. Examples include the allocation of students … (voir plus)to schools (school choice), high-school graduates to colleges, residents to hospitals and refugees to cities. In most of these markets, a desirable property of the assignment is stability, which guarantees that no pair of agents has incentive to circumvent the matching. Using school choice as our matching market application, we introduce the problem of jointly allocating a school capacity expansion and finding the best stable matching for the students in the expanded market. We analyze theoretically the problem, focusing on the trade-off behind the multiplicity of student-optimal assignments, and the problem complexity. Since the theoretical intractability of the problem precludes the adaptation of classical approaches to solve it efficiently, we generalize existent mathematical programming formulations of stability constraints to our setting. These generalizations result in integer quadratically-constrained programs, which are computationally hard to solve. In addition, we propose a novel mixed-integer linear programming formulation that is exponentially-large on the problem size. We show that the stability constraints can be separated in linear time, leading to an effective cutting-plane method. We evaluate the performance of our approaches in a detailed computational study, and we find that our cutting-plane method outperforms mixed-integer programming solvers applied to existent formulations extended to our problem setting. We also propose two heuristics that are effective for large instances of the problem. Finally, we use the Chilean school choice system data to demonstrate the impact of capacity planning under stability conditions. Our results show that each additional school seat can benefit multiple students. On the one hand, we can focus on access by prioritizing extra seats that benefit previously unassigned students; on the other hand, we can focus on merit by allocating extra seats that benefit several students via chains of improvement. These insights empower the decision-maker in tuning the matching algorithm to provide a fair application-oriented solution.

2023-07-07

Proceedings of the 24th ACM Conference on Economics and Computation (publié)

Continuous cutting plane algorithms in integer programming

Didier Chételat

2023-07-01

Operations Research Letters (publié)

A solution algorithm for chance-constrained problems with integer second-stage recourse decisions

Enrico Malaguti

Michele Monaci

Giacomo Nannicini

Paolo

Paronuzzi

2023-06-15

Mathematical programming (publié)

Integer Programming Games: A Gentle Computational Overview

Margarida Carvalho

Gabriele Dragotto

Sriram Sankaranarayan

2023-06-05

ArXiv (prépublication)

Predicting Time to and Average Quality of Future Offers for Kidney Transplant Candidates Declining a Current Deceased Donor Kidney Offer: A Retrospective Cohort Study

Jonathan Jalbert

Jean-Noel Weller

Pierre-Luc Boivin

Sylvain Lavigne

Mehdi Taobane

Mike Pieper

Heloise Cardinal

2023-06-02

Canadian Journal of Kidney Health and Disease (publié)

Machine-learning-based arc selection for constrained shortest path problems in column generation

Mouad Morabit

Guy Desaulniers

Column generation is an iterative method used to solve a variety of optimization problems. It decomposes the problem into two parts: a maste… (voir plus)r problem and one or more pricing problems (PP). The total computing time taken by the method is divided between these two parts. In routing or scheduling applications, the problems are mostly defined on a network, and the PP is usually an NP-hard shortest path problem with resource constraints. In this work, we propose a new heuristic pricing algorithm based on machine learning. By taking advantage of the data collected during previous executions, the objective is to reduce the size of the network and accelerate the PP, keeping only the arcs that have a high chance to be part of the linear relaxation solution. The method has been applied to two specific problems: the vehicle and crew scheduling problem in public transit and the vehicle routing problem with time windows. Reductions in computational time of up to 40% can be obtained.

2023-04-01

INFORMS Journal on Optimization (publié)