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About

Rui Rebelo has a degree in Electrical and Computer Engineering from the Faculty Lusíada, Famalicão (1994). His research interests include balancing, scheduling and development of new production systems.

His work comprehends different cases from development of decision support tools to industrial robotics. Since May 1995 until now he is a Senior Researcher in the Manufacturing Systems Engineering Unit (UESP) of INESC-Porto, Project Leader, actively participating in the institutions’ Research and Development (R&D) activities. He has participated in several R&D projects, including: “CEC-made-shoe: Custom, Environment and Comfort made shoe”, “EUROShoE – extended user oriented shoe enterprise“, “CICLOP - Computerised and integrated closing operations”, “FIT4U - Framework of Integrated Technologies for User Centred Products (2 European patents) 

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Details

Details

038
Publications

2017

Balancing a Mixed-Model Assembly System in the Footwear Industry

Authors
Sadeghi, P; Rebelo, RD; Soeiro Ferreira, J;

Publication
IFIP Advances in Information and Communication Technology

Abstract
Portuguese footwear industry has improved dramatically to become one of the main world players. This work is part of a project in cooperation with a large footwear company, operating a new automated assembly equipment, integrating various lines. Balancing such lines implies going from an almost manual preparation executed by experienced operators, to a planning supported by optimisation systems. These complex mixed-model lines have distinctive characteristics, which make balancing a unique problem. The paper proposes the ASBsm – Assembly System Balancing Solution Method, a new method that integrates a constructive heuristic and an improvement heuristic, which takes inspiration from Tabu Search. The solutions obtained, based on real instances, are quite encouraging when compared with other effected factory solutions. Consequently, the balances obtained by ASBsm are now being implemented and articulated with sequencing methods. © IFIP International Federation for Information Processing 2017.

2016

Evaluating the Forecasting Accuracy of Pure Time Series Models on Retail Data

Authors
Ramos, P; Oliveira, JM; Rebelo, R;

Publication
ADVANCES IN MANUFACTURING TECHNOLOGY XXX

Abstract
Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail supply chains. For profitable retail businesses, accurate sales forecasting is crucial in organizing and planning purchasing, production, transportation and labor force. Retail sales series belong to a special type of time series that typically contain strong trend and seasonal patterns, presenting challenges in developing effective forecasting models. This paper 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. An approach based on cross-validation is used to identify automatically appropriate state space and ARIMA models. The forecasting performance of these models is also compared by examining the out-of-sample forecasts. The results indicate that the overall out-of-sample forecasting performance of ARIMA models evaluated via RMSE, MAE and MAPE is better than state space models. The performance of both forecasting methodologies in producing forecast intervals was also evaluated and the results indicate that ARIMA produces slightly better coverage probabilities than state space models for the nominal 95% forecast intervals. For the nominal 80% forecast intervals the performance of state space models is slightly better.

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

Previsão de vendas de calçado usando redes neuronais artificiais

Authors
João Abel Sousa; Patrícia Ramos; Rui Diogo Rebelo

Publication
Proceedings of ACIM 2011 - XIII Congresso of Accounting and Auditing, Porto, Portugal

Abstract