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Volume 2017, Article ID 5402896, 10 pages
Research Article

A Multilayer Model Predictive Control Methodology Applied to a Biomass Supply Chain Operational Level

1Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
2Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência (INESC TEC), Campus da FEUP, 4200-465 Porto, Portugal
3Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Bragança, Campus de Sta. Apolónia, 5300-253 Bragança, Portugal
4Faculty of Engineering, University of Porto, Porto, Portugal

Correspondence should be addressed to Tatiana M. Pinho; tp.datu@panaitat

Received 23 February 2017; Accepted 5 June 2017; Published 11 July 2017

Academic Editor: Petri T. Helo

Copyright © 2017 Tatiana M. Pinho et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Forest biomass has gained increasing interest in the recent years as a renewable source of energy in the context of climate changes and continuous rising of fossil fuels prices. However, due to its characteristics such as seasonality, low density, and high cost, the biomass supply chain needs further optimization to become more competitive in the current energetic market. In this sense and taking into consideration the fact that the transportation is the process that accounts for the higher parcel in the biomass supply chain costs, this work proposes a multilayer model predictive control based strategy to improve the performance of this process at the operational level. The proposed strategy aims to improve the overall supply chain performance by forecasting the system evolution using behavioural dynamic models. In this way, it is possible to react beforehand and avoid expensive impacts in the tasks execution. The methodology is composed of two interconnected levels that closely monitor the system state update, in the operational level, and delineate a new routing and scheduling plan in case of an expected deviation from the original one. By applying this approach to an experimental case study, the concept of the proposed methodology was proven. This novel strategy enables the online scheduling of the supply chain transport operation using a predictive approach.