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About

Tatiana M. Pinho graduated in Energy Engineering from the University of Trás-os-Montes e Alto Douro (UTAD), Portugal, in 2011 and received the MSc degree in Energy Engineering from UTAD, in 2013. Presently she is a Ph.D. student of Electrical and Computer Engineering in UTAD and INESC-TEC Technology and Science, supported by the FCT (Fundação para a Ciência e a Tecnologia). Her research interests include modeling and adaptive control, in particular Model Predictive Control, applied to agri-forestry systems. 

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Publications

2019

Workload control and optimised order release: an assessment by simulation

Authors
Fernandes, NO; Thürer, M; Pinho, TM; Torres, P; Carmo Silva, S;

Publication
International Journal of Production Research

Abstract
An important scheduling function of manufacturing systems is controlled order release. While there exists a broad literature on order release, reported release procedures typically use simple sequencing rules and greedy heuristics to determine which jobs to select for release. While this is appealing due to its simplicity, its adequateness has recently been questioned. In response, this study uses an integer linear programming model to select orders for release to the shop floor. Using simulation, we show that optimisation has the potential to improve performance compared to ‘classical’ release based on pool sequencing rules. However, in order to also outperform more powerful pool sequencing rules, load balancing and timing must be considered at release. Existing optimisation-based release methods emphasise load balancing in periods when jobs are on time. In line with recent advances in Workload Control theory, we show that a better percentage tardy performance can be achieved by only emphasising load balancing when many jobs are urgent. However, counterintuitively, emphasising urgency in underload periods leads to higher mean tardiness. Compared to previous literature we further highlight that continuous optimisation-based release outperforms periodic optimisation-based release. This has important implications on how optimised-based release should be designed. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

2018

PID Posicast Control for Uncertain Oscillatory Systems: A Practical Experiment

Authors
Oliveira, J; Oliveira, PM; Pinho, TM; Cunha, JB;

Publication
IFAC-PapersOnLine

Abstract

2018

A Sliding Mode-Based Predictive Strategy for Irrigation Canal Pools

Authors
Oliveira, J; Pinho, TM; Coelho, J; Boaventura-Cunha, J; Moura Oliveira, P;

Publication

Abstract
This paper evaluates a robust Model Predictive Controller (MPC) based on Sliding Modes (SMPC) for the downstream level control in irrigation canal pools. Its features are compared with the conventional Generalized Predictive Controller (GPC), regarding set point tracking (water level) and output disturbances (offtake discharges). Simulation results suggest feasibility of applying SMPC for gate manipulation, with suitable command signals and robustness.

2018

Digital Technologies for Forest Supply Chain Optimization: Existing Solutions and Future Trends

Authors
Scholz, J; De Meyer, A; Marques, AS; Pinho, TM; Boaventura Cunha, J; Van Orshoven, J; Rosset, C; Kunzi, J; Kaarle, J; Nummila, K;

Publication
Environmental Management

Abstract

2018

Soft computing optimization for the biomass supply chain operational planning

Authors
Pinho, TM; Coelho, JP; Veiga, G; Moreira, AP; Boaventura Cunha, J;

Publication
13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 - Proceedings

Abstract
Supply chains are complex interdependent structures in which tasks' accomplishment is the result of a compromise between all the entities involved. This complexity is particularly pronounced when dealing with chipping and transportation tasks within a forest-based biomass energy production supply chain. The logistic costs involved are significant and the number of network nodes are usually in a considerable number. For this reason, efficient optimization tools should be used in order to derive cost effective scheduling. In this work, soft computing optimization tools, namely genetic algorithms (GA) and particle swarm optimization (PSO), are integrated within a discrete event simulation model to define the vehicles operational schedule in a typical forest biomass supply chain. The presented simulation results show the proposed methodology effectiveness in dealing with the addressed systems. © 2018 IEEE.