Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
About
Download Photo HD

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

015
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

The Impact of Committing to Customer Orders in Online Retail

Authors
Figueira, G; van Jaarsveld, W; Amorim, P; Fransoo, JC;

Publication
Manufacturing & Service Operations Management

Abstract
Problem definition: Online retailers are on a consistent drive to increase on-time delivery and reduce customer lead time. However, in reality, an increasing share of consumers places orders early. Academic/practical relevance: Such advance demand information can be deployed strategically to reduce costs and improve the customer service experience. This requires inventory and allocation policies that make optimal use of this information and that induce consumers to place their orders early. An increasing number of online retailers not only offer customers a choice of lead time but also, actively back-order missing items from a consumer basket. Methodology: We develop new allocation policies that commit to a customer order upon arrival of the order rather than at the moment the order is due. We provide analytical results for the performance of these allocation policies and evaluate their behavior with real data from a large food retailer. Results: Our policy leads to a higher fill rate at the expense of a slight increase in average delay. The analysis based on real-life data suggests a sizeable impact that should impact current best practices in online retail. Managerial implications: With the changing landscape in online retail, customers increasingly place baskets of orders that they would like to receive at a planned and confirmed moment in time. Especially in grocery, this has grown fast. This fundamentally changes the strategic management of inventory. We demonstrate that online retailers should commit early to customer orders to enhance the customer service experience and eventually, to also create opportunities for reducing the cost of operations.

Supervised
thesis

2021

Impact of "Minimum Life on Receipt" on the production of food consumer goods

Author
Gonçalo Bacelar Xavier Moreno de Sousa

Institution
UP-FEUP

2021

SkyNet: Towards a Dynamic and Adaptive Intrusion Tolerant System

Author
Tadeu Augusto Leite Freitas

Institution
UP-FCUP

2021

Resolução do problema logístico de receção de matérias-primas recorrendo a princípios de revenue management

Author
Bruno Filipe Carvalhais Rebelo

Institution
UP-FEUP

2021

Improving Manufacturing Execution Systems Modelling: a pattern collection and classification approach

Author
Moisés Vasconcelos Rodrigues Torres Coelho

Institution
UP-FEUP

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