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About

About

Master of Science and Doctor of Philosophy in Industrial Engineering and Management by FEUP.

Head of the Research Center for Industrial Engineering and Management from INESC TEC Laboratório Associado.

Assistant Professor at the Department of Industrial Engineering and Management at FEUP.

Co Founder of LTPlabs - consultancy company that applies advanced analytical methods to help make better complex decisions.

Specialist in supply chain planning with an emphasis on food products. He was Supply Chain Analyst at Total Raffinage Marketing (França). Researcher/Consultant in several projects related to Operations Management and supported by different types of entities.

Author of several publications in international journals in the field of Operations Research (for example, International Journal of Production Economics, Industrial Engineering and Chemistry Research, Computers and Chemical Engineering, Interfaces) - Google citation profile.

Interest
Topics
Details

Details

016
Publications

2022

Fostering Customer Bargaining and E-Procurement Through a Decentralised Marketplace on the Blockchain

Authors
Martins, J; Parente, M; Amorim Lopes, M; Amaral, L; Figueira, G; Rocha, P; Amorim, P;

Publication
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT

Abstract

2022

Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning

Authors
Ferreira, C; Figueira, G; Amorim, P;

Publication
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE

Abstract
The emergence of Industry 4.0 is making production systems more flexible and also more dynamic. In these settings, schedules often need to be adapted in real-time by dispatching rules. Although substantial progress was made until the ???90s, the performance of these rules is still rather limited. The machine learning literature is developing a variety of methods to improve them. However, the resulting rules are difficult to interpret and do not generalise well for a wide range of settings. This paper is the first major attempt at combining machine learning with domain problem reasoning for scheduling. The idea consists of using the insights obtained with the latter to guide the empirical search of the former. We hypothesise that this guided empirical learning process should result in effective and interpretable dispatching rules that generalise well to different scenarios. We test our approach in the classical dynamic job shop scheduling problem minimising tardiness, one of the most well-studied scheduling problems. The simulation experiments include a wide spectrum of scenarios for the first time, from highly loose to tight due dates and from low utilisation conditions to severely congested shops. Nonetheless, results show that our approach can find new state-of-the-art rules, which significantly outperform the existing literature in the vast majority of settings. Overall, the average improvement over the best combination of benchmark rules is 19%. Moreover, the rules are compact, interpretable, and generalise well to extreme, unseen scenarios. Therefore, we believe that this methodology could be a new paradigm for applying machine learning to dynamic optimisation problems.

2022

The multi-product inventory-routing problem with pickups and deliveries: Mitigating fluctuating demand via rolling horizon heuristics

Authors
Neves Moreira, F; Almada Lobo, B; Guimaraes, L; Amorim, P;

Publication
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW

Abstract

2022

On the impact of adjusting the minimum life on receipt (MLOR) criterion in food supply chains

Authors
Santos, MJ; Martins, S; Amorim, P; Almada Lobo, B;

Publication
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE

Abstract
The Minimum Life on Receipt (MLOR) is a widely used rule that imposes the minimum remaining age a food product must be delivered by the producer to the retailer. In practice, this rule is set by retailers and it is fixed, around 2/3 of the age of products regardless their shelf life. In this work, we study single and two echelon make-to-stock production-inventory problems for fixed-lifetime perishables. Mixed-integer linear optimization models are developed considering the MLOR rule both as decision variable and fixed parameter. When the MLOR rule is a variable, it is considered either a sole decision of the producer or a collaborative decision between retailer and producer. The goal of this work is to compare the supply chain performance considering this innovative setting of optimal MLOR (as a variable) against the traditional setting of fixed MLOR rule. The computational results suggest that allowing flexible MLOR rules according to the shelf life of products and the operational requirements of the producer benefit both entities in the supply chain. In particular, reducing the MLOR requirement in up to 12% does not interfere substantially with the average freshness of products arriving to the retailer, but reduces extensively surplus/waste generation at the producer while keeping a small amount of waste at the retailer.

2022

Performance evaluation of problematic samples: a robust nonparametric approach for wastewater treatment plants

Authors
Henriques, AA; Fontes, M; Camanho, AS; D'Inverno, G; Amorim, P; Silva, JG;

Publication
ANNALS OF OPERATIONS RESEARCH

Abstract
This paper explores robust unconditional and conditional nonparametric approaches to support performance evaluation in problematic samples. Real-world assessments often face critical problems regarding available data, as samples may be relatively small, with high variability in the magnitude of the observed indicators and contextual conditions. This paper explores the possibility of mitigating the impact of potential outlier observations and variability in small samples using a robust nonparametric approach. This approach has the advantage of avoiding unnecessary loss of relevant information, retaining all the decision-making units of the original sample. We devote particular attention to identifying peers and targets in the robust nonparametric approach to guide improvements for underperforming units. The results are compared with a traditional deterministic approach to highlight the proposed method's benefits for problematic samples. This framework's applicability in internal benchmarking studies is illustrated with a case study within the wastewater treatment industry in Portugal.

Supervised
thesis

2021

Organização, Classificação e Análise de Reviews Online Direcionadas ao Retalho do Município do Porto

Author
Pedro Miguel Baldaia Braga da Costa

Institution
UP-FEP

2021

Predictive Models for Shop Floor Optimization in the Agrofood Industry

Author
Inês Maria de Macedo Pinto Ferreira

Institution
UP-FEUP

2021

Gestão de Armazéns em Emergência Humanitária

Author
Filipe de Sousa Gonçalves

Institution
UP-FEUP

2021

O Impacto da Melhoria Contínua: Análise e Restruturação de Processos Logísticos numa indústria de reparações técnicas

Author
Maria Francisca de Azevedo e Silva

Institution
UP-FEUP

2021

Essays at the intersection of wildfire management and environmental policy: state of the art in mediterranean-climate regions, and impacts and synergies in Portugal

Author
Renata Martins Pacheco

Institution
UP-FEUP