CONTROLO 2016 pp 167-177 | Cite as

Model Predictive Control Applied to a Supply Chain Management Problem

  • Tatiana M. PinhoEmail author
  • João Paulo Coelho
  • António Paulo Moreira
  • José Boaventura-Cunha
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 402)


Supply chains are ubiquitous in any commercial delivery systems. The exchange of goods and services, from different supply points to distinct destinations scattered along a given geographical area, requires the management of stocks and vehicles fleets in order to minimize costs while maintaining good quality services. Even if the operating conditions remain constant over a given time horizon, managing a supply chain is a very complex task. Its complexity increases exponentially with both the number of network nodes and the dynamical operational changes. Moreover, the management system must be adaptive in order to easily cope with several disturbances such as machinery and vehicles breakdowns or changes in demand. This work proposes the use of a model predictive control paradigm in order to tackle the above referred issues. The obtained simulation results suggest that this strategy promotes an easy tasks rescheduling in case of disturbances or anticipated changes in operating conditions.


Model predictive control Supply chain modelling Integer programming problems Transportation scheduling 



This work was supported by the FCT—Fundação para a Ciência e Tecnologia through the PhD Studentship SFRH/BD/98032/2013, program POPH—Programa Operacional Potencial Humano and FSE—Fundo Social Europeu.


  1. 1.
    Eshlaghy, A.T., Razavi, M.: Modeling and simulating supply chain management. Appl. Math. Sci. 5(17), 817–828 (2011)MathSciNetGoogle Scholar
  2. 2.
    Janvier-James, A.M.: A new introduction to supply chains and supply chain management: definitions and theories perspective. Int. Bus. Res. 5(1), 194–207 (2012)Google Scholar
  3. 3.
    Mestan, E., Trkay, M., Arkun, Y.: Optimization of operations in supply chain systems using hybrid systems approach and model predictive control. Ind. Eng. Chem. Res. 45, 6493–9503 (2006)CrossRefGoogle Scholar
  4. 4.
    Park, B.C., Jeong, S.: A modeling framework of supply chain simulation. J. Supply Chain Oper. Manage. 12(2), 91–106 (2014)Google Scholar
  5. 5.
    Perea, E., Grossmann, I., Ydstie, E., Tahmassebi, T.: Dynamic modeling and classical control theory for supply chain management. Comput. Chem. Eng. 24, 1143–1149 (2000)CrossRefGoogle Scholar
  6. 6.
    Badole, C.M., Jain, R., Rathore, A.P.S., Nepal, B.: Research and opportunities in supply chain modeling: a review. Int. J. Supply Chain Manage. 1(3), 63–86 (2012)Google Scholar
  7. 7.
    Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S.: Quantitative models for sustainable supply chain management: developments and directions. Eur. J. Oper. Res. 233(2), 299–312 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Min, H., Zhou, G.: Supply chain modeling: past, present and future. Comput. Ind. Eng. 43, 231–249 (2002)CrossRefGoogle Scholar
  9. 9.
    Mastragostino, R., Patel, S., Swartz, C.L.E.: Robust decision making for hybrid process supply chain systems via model predictive control. Comput. Chem. Eng. 62, 37–55 (2014)CrossRefGoogle Scholar
  10. 10.
    Perea-Lpez, E., Ydstie, B.E., Grossmann, I.E.: A model predictive control strategy for supply chain optimization. Comput. Chem. Eng. 27, 1201–1218 (2003)CrossRefGoogle Scholar
  11. 11.
    Puigjaner, L., Lanez, J.M.: Capturing dynamics in integrated supply chain management. Comput. Chem. Eng. 32, 2582–2605 (2008)CrossRefGoogle Scholar
  12. 12.
    e Hashem, S.M.J.M.A., Aryanezhad, M.B., Sadjadi, S.J.: An efficient algorithm to solve a multi-objective robust aggregate production planning in an uncertain environment. Int. J. Adv. Manuf. Technol. 58(5–8), 765–782 (2012)Google Scholar
  13. 13.
    Li, X., Marlin, T.E.: Robust supply chain performance via model predictive control. Comput. Chem. Eng. 33, 2134–2143 (2009)CrossRefGoogle Scholar
  14. 14.
    Fu, D., Aghezzaf, E.H., Keyser, R.D.: A model predictive control framework for centralised management of a supply chain dynamical system. Syst. Sci. Control Eng. Open Access J. 2(1), 250–260 (2014)CrossRefGoogle Scholar
  15. 15.
    Kapsiotis, G., Tzafestas, S.: Decision making for inventory/production planning using model-based predictive control. Parallel and Distributed Computing in Engineering Systems, pp. 551–556 (1992)Google Scholar
  16. 16.
    Wang, W., Rivera, D.E., Kempf, K.G.: Model predictive control strategies for supply chain management in semiconductor manufacturing. Int. J. Prod. Econ. 107, 56–77 (2007)Google Scholar
  17. 17.
    Hai, D., Hao, Z., Ping, L.Y.: Model predictive control for inventory management in supply chain planning. Adv. Control Eng. Inf. Sci. 15, 1154–1159 (2011)Google Scholar
  18. 18.
    Pinho, T.M., Moreira, A.P., Veiga, G., Boaventura-Cunha, J.: Overview of mpc applications in supply chains: potential use and benefits in the management of forest-based supply chains. For. Syst. 24(3), 1–15 (2015)Google Scholar
  19. 19.
    Sarimveis, H., Patrinos, P., Tarantilis, C.D., Kiranoudis, C.T.: Dynamic modeling and control of supply chain systems: a review. Comput. Oper. Res. 35, 3530–3561 (2008)Google Scholar
  20. 20.
    Carlsson, D., Rnnqvist, M.: Backhauling in forest transportation: models, methods, and practical usage. Can. J. For. Res. 37, 26122623 (2007)Google Scholar
  21. 21.
    Angus-Hankin, C., Stokes, B.: A.T.: The transportation of fuelwood from forest to facility. Biomass Bioenergy 9(1–5), 191–203 (1995)CrossRefGoogle Scholar
  22. 22.
    Mollera, B., Nielsenb, P.S.: Analysing transport costs of danish forest wood chip resources by means of continuous cost surfaces. Biomass Bioenergy 31, 291–298 (2007)CrossRefGoogle Scholar
  23. 23.
    Gunnarsson, H., Ronnqvist, M., Lundgren, J.T.: Supply chain modelling of forest fuel. Eur. J. Oper. Res. 158, 103–123 (2004)CrossRefzbMATHGoogle Scholar
  24. 24.
    Frombo, F., Minciardi, R., Robba, M.B., Sacile, R.: A decision support system for planning biomass-based energy production. Energy 34, 362–369 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Tatiana M. Pinho
    • 1
    • 4
    Email author
  • João Paulo Coelho
    • 2
    • 4
  • António Paulo Moreira
    • 3
    • 4
  • José Boaventura-Cunha
    • 1
    • 4
  1. 1.Universidade de Trás-os-Montes e Alto Douro, UTAD, Escola de Ciências e Tecnologia, Quinta de PradosVila RealPortugal
  2. 2.Instituto Politécnico de Bragança, Escola Superior de Tecnologia e GestãoBragançaPortugal
  3. 3.Faculty of EngineeringUniversity of PortoPortoPortugal
  4. 4.INESC TEC Technology and SciencePortoPortugal

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