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About

Elsa Silva is a researcher at INESC TEC's Industrial Engineering and Management Center and an invited Assistant Professor at the University of Minho's Production and Systems Department.  

She has a PhD in Industrial and Systems Engineering since 2012 from the University of Minho. Her main research interests include the ability to solve hard and large-scale combinatorial optimization problems that arise in various fields, such as cutting and packing problems and retail operations, using hybrid linear and meta-heuristic programming approaches.   

Elsa Silva's main research contributions have been in cutting and packing (C&P) problems. Pioneering algorithms have been developed combining mathematical models, decomposition methods and heuristics to solve practical applications that so far have not been studied realistically. This was an important contribution to the advancement of C&P knowledge.   

The practical applications addressed were Shelf Space Allocation problem, Strip Packing Problem in textile industry, Container Loading Problem with stability, weight limit, load balance and multi-drop constraints. Another important contribution in C&P area was the problem generator for every type of 2D and 3D rectangular C&P problems. 

Interest
Topics
Details

Details

004
Publications

2021

Three-dimensional guillotine cutting problems with constrained patterns: MILP formulations and a bottom-up algorithm

Authors
Martin, M; Oliveira, JF; Silva, E; Morabito, R; Munari, P;

Publication
Expert Systems with Applications

Abstract

2021

Solving the grocery backroom layout problem

Authors
Pires, M; Silva, E; Amorim, P;

Publication
International Journal of Production Research

Abstract

2019

Data mining based framework to assess solution quality for the rectangular 2D strip-packing problem

Authors
Neuenfeldt Junior, A; Silva, E; Gomes, M; Soares, C; Oliveira, JF;

Publication
Expert Systems with Applications

Abstract
In this paper, we explore the use of reference values (predictors) for the optimal objective function value of hard combinatorial optimization problems, instead of bounds, obtained by data mining techniques, and that may be used to assess the quality of heuristic solutions for the problem. With this purpose, we resort to the rectangular two-dimensional strip-packing problem (2D-SPP), which can be found in many industrial contexts. Mostly this problem is solved by heuristic methods, which provide good solutions. However, heuristic approaches do not guarantee optimality, and lower bounds are generally used to give information on the solution quality, in particular, the area lower bound. But this bound has a severe accuracy problem. Therefore, we propose a data mining-based framework capable of assessing the quality of heuristic solutions for the 2D-SPP. A regression model was fitted by comparing the strip height solutions obtained with the bottom-left-fill heuristic and 19 predictors provided by problem characteristics. Random forest was selected as the data mining technique with the best level of generalisation for the problem, and 30,000 problem instances were generated to represent different 2D-SPP variations found in real-world applications. Height predictions for new problem instances can be found in the regression model fitted. In the computational experimentation, we demonstrate that the data mining-based framework proposed is consistent, opening the doors for its application to finding predictions for other combinatorial optimisation problems, in particular, other cutting and packing problems. However, how to use a reference value instead of a bound, has still a large room for discussion and innovative ideas. Some directions for the use of reference values as a stopping criterion in search algorithms are also provided. © 2018 Elsevier Ltd

2018

A new Load Balance Methodology for Container Loading Problem in Road Transportation

Authors
Ramos, AG; Silva, E; Oliveira, JF;

Publication
European Journal of Operational Research

Abstract

2018

Allocating products on shelves under merchandising rules: Multi-level product families with display directions

Authors
Bianchi Aguiar, T; Silva, E; Guimardes, L; Carravilla, MA; Oliveira, JF;

Publication
Omega (United Kingdom)

Abstract
Retailers’ individual products are categorized as part of product families. Merchandising rules specify how the products should be arranged on the shelves using product families, creating more structured displays capable of increasing the viewers’ attention. This paper presents a novel mixed integer programming formulation for the Shelf Space Allocation Problem considering two innovative features emerging from merchandising rules: hierarchical product families and display directions. The formulation uses single commodity flow constraints to model product sequencing and explores the product families’ hierarchy to reduce the combinatorial nature of the problem. Based on the formulation, a mathematical programming-based heuristic was also developed that uses product families to decompose the problem into a sequence of sub-problems. To improve performance, its original design was adapted following two directions: recovery from infeasible solutions and reduction of solution times. A new set of real case benchmark instances is also provided, which was used to assess the formulation and the matheuristic. This approach will allow retailers to efficiently create planograms capable of following merchandising rules and optimizing shelf space revenue. © 2017 Elsevier Ltd

Supervised
thesis

2020

OTIMIZAÇÃO DA ENTREGA DE ENCOMENDAS POR DRONES

Author
JOÃO PEDRO MOUTINHO ALVES BARBOSA

Institution
IPP-ISEP

2019

Conceção e desenvolvimento de um sistema de apoio à decisão para gestão de inventários no retalho

Author
Edgar Filipe dos Anjos Couto

Institution
UP-FEUP

2019

Efficient Heuristics for Two-Dimensional Cutting and Packing Problems

Author
Óscar António Maia de Oliveira

Institution
INESCTEC

2017

The Two-Dimensional Rectangular Strip Packing Problem

Author
Álvaro Luiz Neuenfeldt Júnior

Institution
UP-FEUP