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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

002
Publications

2019

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

Authors
Júnior, AN; Silva, E; Gomes, AM; Soares, C; Oliveira, JF;

Publication
Expert Syst. Appl.

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

2016

A surveyonheuristics for the two-dimensional rectangular strip packing problem

Authors
Oliveira, JF; Neuenfeldt, A; Silva, E; Carravilla, MA;

Publication
Pesquisa Operacional

Abstract
Two-dimensional rectangular strip packing problems belong to the broader class of Cutting and Packing (C&P) problems, in which small items are required to be cut from or packed on a larger object, so that the waste (unused regions of the large object) is minimized. C&P problems differ from other combinatorial optimization problems by the intrinsic geometric constraints: items may not overlap and have to be fully contained in the large object. This survey approaches the specific C&P problem in which all items are rectangles, therefore fully characterized by a width and a height, and the large object is a strip, i.e. a rectangle with a fixed width but an infinite height, being the problem’s goal to place all rectangles on the strip so that the height is minimized. These problems have been intensively and extensively tackled in the literature and this paper will focus on heuristic resolution methods. Both the seminal and the most recent approaches (from the last decade) will be reviewed, in a rather tutorial flavor, and classified according to their type: constructive heuristics, improvement heuristics with search over sequences and improvement heuristics with search over layouts. Building on this review, research gaps are identified and the most interesting research directions pointed out. © 2016 Brazilian Operations Research Society.

2016

Using Analytics to Enhance a Food Retailer's Shelf-Space Management

Authors
Bianchi Aguiar, T; Silva, E; Guimaraes, L; Carravilla, MA; Oliveira, JF; Amaral, JG; Liz, J; Lapela, S;

Publication
INTERFACES

Abstract
This paper describes the results of our collaboration with the leading Portuguese food retailer to address the shelf-space planning problem of allocating products to shop-floor shelves. Our challenge was to introduce analytical methods into the shelf-space planning process to improve the return on space and automate a process heavily dependent on the experience of the retailer's space managers. This led to the creation of GAP, a decision support system that the company's space-management team uses daily. We developed a modular operations research approach that systematically applies mathematical programming models and heuristics to determine the best layout of products on the shelves. GAP combines its analytical strength with an ability to incorporate different types of merchandising rules to balance the tradeoff between optimization and customization.

2016

The pallet loading problem: a review of solution methods and computational experiments

Authors
Silva, E; Oliveira, JF; Waescher, G;

Publication
ITOR

Abstract
The manufacturer's pallet loading problem (MPLP) has been widely studied during the past 50 years. It consists of placing a maximum number of identical rectangular boxes onto a single rectangular pallet. In this paper, we have reviewed the methods that have been proposed for the solution of this problem. Furthermore, the various problem instances and data sets are analyzed that have been used in computational experiments for the evaluation of these methods. The most challenging and yet unsolved methods are identified. By doing so, areas of future research concerning the MPLP can be highlighted. © 2015 The Authors.

Supervised
thesis

2017

The Two-Dimensional Rectangular Strip Packing Problem

Author
Álvaro Luiz Neuenfeldt Júnior

Institution
UP-FEUP