Model Predictive Control in Semiconductor Supply Chain Operations

2011 ◽  
pp. 403-428 ◽  
Author(s):  
Karl Kempf ◽  
Kirk Smith ◽  
Jay Schwartz ◽  
Martin Braun
Keyword(s):  
Supply Chain ◽  
Model Predictive Control ◽  
Predictive Control

scholarly journals A Centralized Model Predictive Control Strategy for Dynamic Supply Chain Management

2013 ◽  
Vol 46 (9) ◽  
pp. 1608-1613 ◽  
Author(s):  
Dongfei Fu ◽  
Clara Mihaela Ionescu ◽  
El-Houssaine Aghezzaf ◽  
Robin De Keyser
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Chain Management ◽  
Centralized Model

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

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Tatiana M. Pinho ◽  
João Paulo Coelho ◽  
Germano Veiga ◽  
A. Paulo Moreira ◽  
José Boaventura-Cunha
Keyword(s):  
Supply Chain ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Fossil Fuels ◽  
Dynamic Models ◽  
Forest Biomass ◽  
Routing And Scheduling ◽  
Multilayer Model ◽  
Operational Level ◽  
Biomass Supply

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.

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