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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

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

2018

An intercontinental replenishment problem: A hybrid approach

Authors
Silva, E; Ramos, AG; Lopes, M; Magalhaes, P; Oliveira, JF;

Publication
Springer Proceedings in Mathematics and Statistics

Abstract
This work addresses a case study in an intercontinental supply chain. The problem emerges in a company in Angola dedicated to the trade of consumable goods for construction building and industrial maintenance. The company in Angola sends the replenishment needs to a Portuguese company, which takes the decision of which products and in which quantities will be sent by shipping container to the company in Angola. The replenishment needs include the list of products that reached the corresponding reorder point. The decision of which products and in which quantity should take into consideration a set of practical constraints: the maximum weight of the cargo, the maximum volume the cargo and financial constraints related with the minimum value that guarantees the profitability of the business and a maximum value associated with shipping insurance. A 2-stage hybrid method is proposed. In the first stage, an integer linear programming model is used to select the products that maximise the sales potential. In the second stage, a Container Loading Algorithm is used to effectively pack the selected products in the shipping container ensuring the geometrical constraints, and safety constraints such as weight limit and stability. A new set of problem instances was generated with the 2DCPackGen problem generator, using as inputs the data collected in the company. Computational results for the algorithm are presented and discussed. Good results were obtained with the solution approach proposed, with an average occupation ratio of 92% of the container and an average gap of 4% for the solution of the integer linear programming model. © Springer International Publishing AG 2018.

2018

The two-dimensional strip packing problem: What matters?

Authors
Neuenfeldt Junior, A; Silva, E; Miguel Gomes, AM; Oliveira, JF;

Publication
Springer Proceedings in Mathematics and Statistics

Abstract
This paper presents an exploratory approach to study and identify the main characteristics of the two-dimensional strip packing problem (2D-SPP). A large number of variables was defined to represent the main problem characteristics, aggregated in six groups, established through qualitative knowledge about the context of the problem. Coefficient correlation are used as a quantitative measure to validate the assignment of variables to groups. A principal component analysis (PCA) is used to reduce the dimensions of each group, taking advantage of the relations between variables from the same group. Our analysis indicates that the problem can be reduced to 19 characteristics, retaining most part of the total variance. These characteristics can be used to fit regression models to estimate the strip height necessary to position all items inside the strip. © Springer International Publishing AG 2018.

Supervised
thesis

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
UP-FEUP

2017

The Two-Dimensional Rectangular Strip Packing Problem

Author
Álvaro Luiz Neuenfeldt Júnior

Institution
UP-FEUP