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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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059
Publications

2020

Exploring the Linkages Between the Internet of Things and Planning and Control Systems in Industrial Applications

Authors
Soares, R; Marques, A; Gomes, R; Guardão, L; Hernández, E; Rebelo, R;

Publication
Lecture Notes in Mechanical Engineering - Progress in Digital and Physical Manufacturing

Abstract

2020

Implementing RAMI4.0 in Production - A Multi-case Study

Authors
Hernández, E; Senna, P; Silva, D; Rebelo, R; Barros, AC; Toscano, C;

Publication
Lecture Notes in Mechanical Engineering - Progress in Digital and Physical Manufacturing

Abstract

2018

Balancing mixed-model assembly systems in the footwear industry with a variable neighbourhood descent method

Authors
Sadeghi, P; Rebelo, RD; Ferreira, JS;

Publication
COMPUTERS & INDUSTRIAL ENGINEERING

Abstract
This paper addresses new Mixed-model Assembly Line Balancing Problems (MALBP) in a real industrial context, the stitching systems of a footwear company. The work is part of large ongoing projects with this industry, and the main purposes are minimising the number of required workstations and smoothing the operators' workload. The company has invested in new flexible automated assembly systems, which accommodate dozens of workstations and many moving boxes. Footwear components are inside boxes (with various quantities) which can move from the warehouses to a convenient workstation or between any workstations (in any order). This is a significant and distinct feature of the MALBP, together with the fact that the assignment of different skilled operators and machines is achieved simultaneously. An optimisation model is developed, in part to facilitate the understanding of the situation and to solve small-size instances. Due to the complexity of the problems, we had to devise an approximate method, based on the Variable Neighbourhood Descent (VND) metaheuristic and integrating an adaptation of the Ranked Positional Weighted (RPW) method. The adapted RPW method is used to create initial feasible solutions, while preassigning special operators and machines. After choosing good initial solutions, VND is applied to improve their quality. The new contributed method, named as RPW-VNDbal, is tested with medium and large instances, in two distinct stitching systems. A Lower Bound of the objective function and Simulation contribute to evaluate the solutions and their practicability. The results implemented by the project team, show that the RPW-VNDbal method is fast enough and offers better solutions than those implemented by the experienced operation managers of the company.

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.