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About

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

003
Publications

2019

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.

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

2017

Decentralized Vs. Centralized Sequencing in a Complex Job-Shop Scheduling

Authors
Mehrsai, A; Figueira, G; Santos, N; Amorim, P; Almada Lobo, B;

Publication
IFIP Advances in Information and Communication Technology

Abstract
Allocation of jobs to machines and subsequent sequencing each machine is known as job scheduling problem. Classically, both operations are done in a centralized and static/offline structure, considering some assumptions about the jobs and machining environment. Today, with the advent of Industry 4.0, the need to incorporate real-time data in the scheduling decision process is clear and facilitated. Recently, several studies have been conducted on the collection and application of distributed data in real-time of operations, e.g., job scheduling and control. In practice, pure distribution and decentralization is not yet fully realizable because of e.g., transformation complexity and classical resistance to change. This paper studies a combination of decentralized sequencing and central optimum allocation in a lithography job-shop problem. It compares the level of applicability of two decentralized algorithms against the central scheduling. The results show better relative performance of sequencing in stochastic cases. © IFIP International Federation for Information Processing 2017.

2017

An optimization-simulation approach to the network redesign problem of pharmaceutical wholesalers

Authors
Martins, S; Amorim, P; Figueira, G; Almada Lobo, B;

Publication
COMPUTERS & INDUSTRIAL ENGINEERING

Abstract
The pharmaceutical industry operates in a very competitive and regulated market The increased pressure of pharmacies to order fewer products and to receive them more frequently is overcharging the pharmaceutical's distribution network Furthermore, the tight margins and the continuous growth of generic drugs consumption are pressing wholesalers to optimize their supply chains. In order to survive, wholesalers are rethinking their strategies to increase competitiveness. This paper proposes an optimization-simulation approach to address the wholesalers network redesign problem, trading off the operational costs and customer service level. Firstly, at a strategic-tactical level, the supply chain network redesign decisions are optimized via a mixed integer programming model. Here, the number, location, function and capacity of the warehouses, the allocation of customers to the warehouses and the capacity and function of the distribution channels are defined. Secondly, at an operation level, the solution found is evaluated by means of a discrete event simulation model to assess the impact of the redesign in the wholesaler's daily activities. Computational results on a pharmaceutical wholesaler case-study are discussed and the benefits of this solution approach exposed.

2017

A data mining based system for credit-card fraud detection in e-tail

Authors
Carneiro, N; Figueira, G; Costa, M;

Publication
DECISION SUPPORT SYSTEMS

Abstract
Credit-card fraud leads to billions of dollars in losses for online merchants. With the development of machine learning algorithms, researchers have been finding increasingly sophisticated ways to detect fraud, but practical implementations are rarely reported. We describe the development and deployment of a fraud detection system in a large e-tail merchant. The paper explores the combination of manual and automatic classification, gives insights into the complete development process and compares different machine learning methods. The paper can thus help researchers and practitioners to design and implement data mining based systems for fraud detection or similar problems. This project has contributed not only with an automatic system, but also with insights to the fraud analysts for improving their manual revision process, which resulted in an overall superior performance.

Supervised
thesis

2018

Controlo das trajetórias de um robô móvel de alto desempenho

Author
Sandro Augusto Costa Magalhães

Institution
UP-FEUP

2018

Altitude Control of an Underwatervehicle Based on Computer Vision

Author
Pedro Miguel Flores Rodrigues

Institution
UP-FEUP

2018

Odometria Visual em Robôs para a Agricultura com Câmara com Lentes "Olho de Peixe"

Author
Sérgio Miguel Vieira Pinto

Institution
UP-FEUP

2018

Estratégias de evolução da Automação de Próxima Geração para a Rede Nacional de Distribuição

Author
Ricardo Alberto Ferreira Puga

Institution
UP-FEUP

2016

Improvement of customs calculation model in a cross-border e-tail business

Author
Ana Isabel de Castro Arrepia Ferreira

Institution
UP-FEUP