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About

I am a researcher at INESC TEC since 2010 where I have participated in both european and national projects such as FIT4U or KEP.
My research interests include Optimisation, Simulation and Machine Learning.
I hold a degree in Mathematics and a Master in Mathematical Engineering from Faculdade de Ciências da Universidade do Porto-Portugal. I am currently a PhD candidate in Computer Science at the referred university.

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001
Publications

2017

Kidney exchange simulation and optimization

Authors
Santos, N; Tubertini, P; Viana, A; Pedroso, JP;

Publication
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY

Abstract
One of the challenges in a kidney exchange program (KEP) is to choose policies that ensure an effective and fair management of all participating patients. In order to understand the implications of different policies of patient allocation and pool management, decision makers should be supported by a simulation tool capable of tackling realistic exchange pools and modeling their dynamic behavior. In this paper, we propose a KEP simulator that takes into consideration the wide typology of actors found in practice (incompatible pairs, altruistic donors, and compatible pairs) and handles different matching policies. Additionally, it includes the possibility of evaluating the impact of positive crossmatch of a selected transplant, and of dropouts, in a dynamic environment. Results are compared to those obtained with a complete information model, with knowledge of future events, which provides an upper bound to the objective values. Final results show that shorter time intervals between matches lead to higher number of effective transplants and to shorter waiting times for patients. Furthermore, the inclusion of compatible pairs is essential to match pairs of specific patient-donor blood type. In particular, O-blood type patients benefit greatly from this inclusion.

2015

Performance of state space and ARIMA models for consumer retail sales forecasting

Authors
Ramos, P; Santos, N; Rebelo, R;

Publication
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

Abstract
Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.

2014

A tabu search for the permutation flow shop problem with sequence dependent setup times

Authors
Santos, N; Rebelo, R; Pedroso, JP;

Publication
International Journal of Data Analysis Techniques and Strategies

Abstract
In this work we present a tabu search metaheuristic method for solving the permutation flow shop scheduling problem with sequence dependent setup times and the objective of minimising total weighted tardiness. The problem is well known for its practical applications and for the difficulty in obtaining good solutions. The tabu search method proposed is based on the insertion neighbourhood, and is characterised by the selection and evaluation of a small subset of this neighbourhood at each iteration; this has consequences both on diversification and intensification of the search. We also propose a speed-up technique based on book keeping information of the current solution, used for the evaluation of its neighbours. © 2014 Inderscience Enterprises Ltd.

2011

A Tabu Search Approach for the Hybrid Flow Shop

Authors
Nicolau Filipe Santos; João Pedro Pedroso

Publication
VII ALIO/EURO - VII ALIO/EURO Workshop on Applied Combinatorial Optimization, Porto, Portugal

Abstract
In this work we present a metaheuristic based on tabu search, designed with the objective of minimizing makespan in a hybrid flow shop problem. In order to assess the performance of the proposed method we performed tests using both well known benchmarks and randomly generated instances; preliminary results indicate that the approach is valid.