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About

I am a researcher in the Center for Industrial Engineering and Management from INESC TEC, and an invited professor in the Department of Industrial Engineering and Management at FEUP. I hold M.Sc. and Ph.D. degrees in Industrial Engineering and Management from FEUP.

My research interests include supply chain management, operations research and decision support systems. I have published in international journals such as Computers and OR, Computers and Chemical Engineering, Decision Support Systems, and OR Perspectives - Google citation profile.

I have also been a researcher/consultant in several R&D projects, funded by different types of entities, in the areas of production planning, supply chain design, scheduling, disturbance management and inventory replenishment.

Interest
Topics
Details

Details

006
Publications

2020

Cooperative coevolution of expressions for (r,Q) inventory management policies using genetic programming

Authors
Lopes, RL; Figueira, G; Amorim, P; Almada Lobo, B;

Publication
International Journal of Production Research

Abstract
There are extensive studies in the literature about the reorder point/order quantity policies for inventory management, also known as (r,Q) policies. Over time different algorithms have been proposed to calculate the optimal parameters given the demand characteristics and a fixed cost structure, as well as several heuristics and meta-heuristics that calculate approximations with varying accuracy. This work proposes a new meta-heuristic that evolves closed-form expressions for both policy parameters simultaneously - Cooperative Coevolutionary Genetic Programming. The implementation used for the experimental work is verified with published results from the optimal algorithm, and a well-known hybrid heuristic. The evolved expressions are compared to those algorithms, and to the expressions of previous Genetic Programming approaches available in the literature. The results outperform the previous closed-form expressions and demonstrate competitiveness against numerical methods, reaching an optimality gap of less than (Formula presented.), while being two orders of magnitude faster. Moreover, the evolved expressions are compact, have good generalisation capabilities, and present an interesting structure resembling previous heuristics. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

2020

Production scheduling in the context of Industry 4.0: review and trends

Authors
Parente, M; Figueira, G; Amorim, P; Marques, A;

Publication
International Journal of Production Research

Abstract

2020

Trustability in Algorithmic Systems Based on Artificial Intelligence in the Public and Private Sectors

Authors
Teixeira, S; Gama, J; Amorim, P; Figueira, G;

Publication
ERCIM News

Abstract

2019

Integration of Supplier Selection and Inventory Management under Supply Disruptions

Authors
Saputro, TE; Figueira, G; Almada Lobo, B;

Publication
IFAC PAPERSONLINE

Abstract
Procurement plays an essential role in the supply of materials for the production of goods or products. The success of procurement management to fulfill demand with high service levels and on-time delivery relies on the suppliers' performance. Suppliers should be appropriately selected to source materials with the right quality, in the right quantity, at the right time, and for the right price. The scope of this problem, as well as other aspects such as the sourcing strategy, will depend on the type of items. Critical items, which represent high-profit impacts and high supply risks, should be approached comprehensively by considering all the main activities of the procurement process. This study focuses on a supplier selection problem integrated with inventory management under a multi-sourcing strategy, by taking into account stochastic demand and supply disruptions. This problem is approached by a simulation-optimization method, composed of discrete-event simulation and a genetic algorithm (GA). Finally, a numerical example is provided to illustrate the solution procedure.

2018

Designing new heuristics for the capacitated lot sizing problem by genetic programming

Authors
Hein, F; Almeder, C; Figueira, G; Almada Lobo, B;

Publication
Computers and Operations Research

Abstract
This work addresses the well-known capacitated lot sizing problem (CLSP) which is proven to be an NP-hard optimization problem. Simple period-by-period heuristics are popular solution approaches due to the extremely low computational effort and their suitability for rolling planning horizons. The aim of this work is to apply genetic programming (GP) to automatically generate specialized heuristics specific to the instance class. Experiments show that we are able to obtain better solutions when using GP evolved lot sizing rules compared to state-of-the-art constructive heuristics. © 2018 Elsevier Ltd

Supervised
thesis

2020

Leveraging Supplier Selection Within Supply Chain Managememt Under Uncertainty

Author
Thomy Eko Saputro

Institution
UP-FEUP

2020

Product line selection in fast-moving consumer goods industries

Author
Xavier António Reis Andrade

Institution
UP-FEUP

2020

Previsão do potencial de vendas na cadeia de pescado com base na procura censurada

Author
Mariana Teixeira de Jesus

Institution
UP-FEUP

2020

Políticas de gestão de inventário para farmácias comunitárias

Author
Luís Miguel Varajão Gonçalves

Institution
INESCTEC

2020

A Comparison on Statistical Methods and Long Short Term Memory Network Forecasting the Demand of Fresh Fish Products

Author
Rúben Alexandre da Fonseca Marques

Institution
UP-FEUP