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Sobre

Elsa Silva é investigadora no Centro de Engenharia e Gestão Industrial do INESC TEC e Professora Auxiliar convidada do Departamento de Produção e Sistemas da Universidade do Minho.

É doutorada em Engenharia Industrial e de Sistemas desde 2012 pela Universidade do Minho. Os seus principais interesses de investigação incluem a capacidade de resolver problemas de optimização combinatória difíceis e de grande escala que surgem em várias áreas, tais como problemas de corte e empacotamento e operações de retalho, utilizando abordagens híbridas de programação linear e meta-heurísticas.

As principais contribuições da investigação da Elsa Silva têm sido em problemas de corte e empacotamento (C&P). Algoritmos pioneiros foram desenvolvidos combinando modelos matemáticos, métodos de decomposição e heurísticas para resolver aplicações práticas que até agora não foram estudadas realisticamente. Esta foi uma contribuição importante para o avanço do conhecimento na área dos C&P.

As aplicações práticas abordadas foram: Problema de Alocação de Espaço em Prateleira, Problema de Empacotamento de Tiras na indústria têxtil, Problema de Carregamento de Contentores com estabilidade, limite de peso, balanceamento de carga e restrições de entregas múltiplas. Outra contribuição importante na área de C&P foi o gerador de instâncias para todos os tipos de problemas de C&P retangulares 2D e 3D.

Tópicos
de interesse
Detalhes

Detalhes

004
Publicações

2020

Solving the grocery backroom layout problem

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

Publicação
International Journal of Production Research

Abstract

2019

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

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

Publicação
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

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

Publicação
European Journal of Operational Research

Abstract

2018

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

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

Publicação
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

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

Publicação
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.

Teses
supervisionadas

2019

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

Autor
Edgar Filipe dos Anjos Couto

Instituição
UP-FEUP

2019

Efficient Heuristics for Two-Dimensional Cutting and Packing Problems

Autor
Óscar António Maia de Oliveira

Instituição
UP-FEUP

2017

The Two-Dimensional Rectangular Strip Packing Problem

Autor
Álvaro Luiz Neuenfeldt Júnior

Instituição
UP-FEUP