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
Facts & Numbers
000
Presentation

Industrial Engineering and Management

The centre is an international reference in business analytics through decision support systems for service and operations management, contributing also in service design, performance assessment and asset management.

Our core areas of application include Mobility/Transports, Retail/Industry and Healthcare, also with significant contributions in the Energy Sector and a strengthened collaboration with the Centre for Power and Energy Systems.

In the latest years, CEGI substantially contribute to Industry 4.0 initiatives (improving scheduling rules based on the additional information available in manufacturing systems).

Latest News
Systems Engineering and Management

TestBed 5G: INESC TEC in half of the pilots that aim to boost the manufacturing industry

INESC TEC’s Industry and Innovation Laboratory (iiLab) has already received six of the 12 planned pilots. The demonstrations aim to provide industrial solutions with different operating and coordination features.  

29th October 2024

Systems Engineering and Management

Sorting, organising, palletising: INESC TEC technology paving the way to an optimised supply chain

The Institute contributed to a solution that reduces manual efforts and ensures a more flexible supply chain. This involvement was “fundamental”, stemming from the ongoing progress of the PRODUTECH R3 mobilising agenda.  

29th October 2024

Drones, automation and sensing: here are INESC TEC’s solutions to the challenges of the wine sector

INESC TEC researchers led discussions on innovative solutions for vineyards at an event that brought together companies, universities and players in the sector.  

24th October 2024

Systems Engineering and Management

INESC TEC researcher warns companies about the quality of data generated through AI in a paper published by MIT management journal

Could the increasing interest in language models, like ChatGPT, be diverting resources away from companies to adopt advanced analytics practices that truly support smart decisions? Pedro Amorim, INESC TEC researcher, and João Alves (from INESC TEC LTPLabs spin-off) believe so. In a paper published in MIT Sloan Management Review, they warn about the quality and unpredictability of data generated solely from generative language models - despite advocating for more investment in Artificial Intelligence (AI) that incorporates these models with advanced analysis (with concrete reasons provided).

25th June 2024

This technology aims to make cities more accessible for everyone - and earned a recognition for an INESC TEC researcher

INESC TEC researcher Marta Campos Ferreira participated in the development of a prototype that seeks to improve the experiences of people with reduced mobility in cities – and make them more inclusive. The new solution featured at an international conference, and the researcher's work was acknowledged. Now, the goal is for the solution to reach everyone through an application.

17th May 2024

Team
Publications

CEGI Publications

View all Publications

2025

Local stability in kidney exchange programs

Authors
Baratto, M; Crama, Y; Pedroso, JP; Viana, A;

Publication
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Abstract
When each patient of a kidney exchange program has a preference ranking over its set of compatible donors, questions naturally arise surrounding the stability of the proposed exchanges. We extend recent work on stable exchanges by introducing and underlining the relevance of a new concept of locally stable, or L-stable, exchanges. We show that locally stable exchanges in a compatibility digraph are exactly the so-called local kernels (L-kernels) of an associated blocking digraph (whereas the stable exchanges are the kernels of the blocking digraph), and we prove that finding a nonempty L-kernel in an arbitrary digraph is NP-complete. Based on these insights, we propose several integer programming formulations for computing an L-stable exchange of maximum size. We conduct numerical experiments to assess the quality of our formulations and to compare the size of maximum L-stable exchanges with the size of maximum stable exchanges. It turns out that nonempty L-stable exchanges frequently exist in digraphs which do not have any stable exchange. All the above results and observations carry over when the concept of (locally) stable exchanges is extended to the concept of (locally) strongly stable exchanges.

2025

Predicting demand for new products in fashion retailing using censored data

Authors
Sousa, MS; Loureiro, ALD; Miguéis, VL;

Publication
EXPERT SYSTEMS WITH APPLICATIONS

Abstract
In today's highly competitive fashion retail market, it is crucial to have accurate demand forecasting systems, namely for new products. Many experts have used machine learning techniques to forecast product sales. However, sales that do not happen due to lack of product availability are often ignored, resulting in censored demand and service levels that are lower than expected. Motivated by the relevance of this issue, we developed a two-stage approach to forecast the demand for new products in the fashion retail industry. In the first stage, we compared four methods of transforming historical sales into historical demand for products already commercialized. Three methods used sales-weighted averages to estimate demand on the days with stock-outs, while the fourth method employed an Expectation-Maximization (EM) algorithm to account for potential substitute products affected by stock-outs of preferred products. We then evaluated the performance of these methods and selected the most accurate one for calculating the primary demand for these historical products. In the second stage, we predicted the demand for the products of the following collection using Random Forest, Deep Neural Networks, and Support Vector Regression algorithms. In addition, we applied a model that consisted of weighting the demands previously calculated for the products of past collections that were most similar to the new products. We validated the proposed methodology using a European fashion retailer case study. The results revealed that the method using the Expectation-Maximization algorithm had the highest potential, followed by the Random Forest algorithm. We believe that this approach will lead to more assertive and better-aligned decisions in production management.

2025

Emerging technologies for supporting patients during Hemodialysis: A scoping review

Authors
Martins, AR; Ferreira, MC; Fernandes, CS;

Publication
International Journal of Medical Informatics

Abstract

2025

Emerging technologies for supporting patients during Hemodialysis: A scoping review

Authors
Martins, AR; Ferreira, MC; Fernandes, CS;

Publication
International Journal of Medical Informatics

Abstract

2024

Heuristics for online three-dimensional packing problems and algorithm selection framework for semi-online with full look-ahead

Authors
Ali, S; Ramos, AG; Carravilla, MA; Oliveira, JF;

Publication
APPLIED SOFT COMPUTING

Abstract
In online three-dimensional packing problems (3D-PPs), unlike offline problems, items arrive sequentially and require immediate packing decisions without any information about the quantities and sizes of the items to come. Heuristic methods are of great importance in solving online problems to find good solutions in a reasonable amount of time. However, the literature on heuristics for online problems is sparse. As our first contribution, we developed a pool of heuristics applicable to online 3D-PPs with complementary performance on different sets of instances. Computational results showed that in terms of the number of used bins, in all problem instances, at least one of our heuristics had a better or equal performance compared to existing heuristics in the literature. The developed heuristics are also fully applicable to an intermediate class between offline and online problems, referred to in this paper as a specific type of semi-online with full look-ahead, which has several practical applications. In this class, as in offline problems, complete information about all items is known in advance (i.e., full look-ahead); however, due to time or space constraints, as in online problems, items should be packed immediately in the order of their arrival. As our second contribution, we presented an algorithm selection framework, building on developed heuristics and utilizing prior information about items in this specific class of problems. We used supervised machine learning techniques to find the relationship between the features of problem instances and the performance of heuristics and to build a prediction model. The results indicate an 88% accuracy in predicting (identifying) the most promising heuristic(s) for solving any new instance from this class of problems.

Facts & Figures

0R&D Employees

2020

9Proceedings in indexed conferences

2020

22Papers in indexed journals

2020