Intelligent Mathematical Modelling Agent for Supporting Decision-Making at Industry 4.0

  • Edrisi Muñoz
  • Elisabet Capón-García
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 865)


The basis of decision-making at industry consists of formally representing the system and its subsystems in a model, which adequately captures those features that are necessary to reach consistent decisions. New trends in semantics and knowledge models aim to formalize the mathematical domain and mathematical models in order to provide bases for machine reasoning and artificial intelligence. Hence, tools for improving information sharing and communication have proved to be highly promising to support the integration of performance assessment within industrial decision-making. This work presents an intelligent agent based on knowledge models and establishes the basis for automating the design, management, programming and solution of mathematical models used in the industry. A case study concerning a capacity limitation constraint demonstrates the performance of the agent and indicates the directions for future work.


Process optimization Knowledge management Intelligent agent Mathematical modelling Mathematical programming 


  1. 1.
    Munoz, E., Capon-Garcia, E., Lainez, J., Espuna, A., Puigjaner, L.: Integration of enterprise levels based on an ontological framework. Chem. Eng. Res. Des. 91, 1542–1556 (2013)CrossRefGoogle Scholar
  2. 2.
    Bender, E.: An Introduction to Mathematical Modelling. Dover Publications, New York (1978)zbMATHGoogle Scholar
  3. 3.
    Gubán, A., Kása, R.: Conceptualization of fluid flows of logistificated processes. Adv. Logistic Syst. 7(2), 27–34 (2013) Google Scholar
  4. 4.
    Mayer, V., Kenneth Cukier, S.: Big Data, A Revolution That Will Transform How We Live, Work, and Think (2013)Google Scholar
  5. 5.
    Munoz, E., Capon-Garcia, E., Espuna, A., Puigjaner, L.: Ontological framework for enterprise-wide integrated decision-making at operational level. Comput. Chem. Eng. 42, 217–234 (2012)CrossRefGoogle Scholar
  6. 6.
    International Society for Measurement and Control. Batch control part 1 models and terminology (1995)Google Scholar
  7. 7.
    International Society for Measurement and Control. Control batch part 4 batch production records (2006)Google Scholar
  8. 8.
    International Society for Measurement and Control. Batch control part 5 automated equipment control models & terminology (2007)Google Scholar
  9. 9.
    International Society for Measurement and Control. ISA-88/95 technical report: using isa-88 and isa-95 together. Technical report (2007)Google Scholar
  10. 10.
    Marzal Varó, A., García Sevilla, P., Gracia Luengo, I.: Introducción a la programación con Python 3. Castellón de la Plana: Universitat Jaume I. Servei de Comunicació i Publicacions (2014)Google Scholar
  11. 11.
    Avgeriou, P., Zdun, U.: Architectural patterns revisited: a pattern language. In 10th European Conference on Pattern Languages of Programs, pp. 1–39 (Euro-Plop 2005). Irsee (2005)Google Scholar
  12. 12.
    Mathematical markup language (MathML) version 3.0.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centro de Investigación en Matemáticas A.C.GuanajuatoMexico
  2. 2.ABB Switzerland Ltd.Baden-DättwilSwitzerland

Personalised recommendations