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Matheuristic Algorithms for Production Planning in Manufacturing Enterprises

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Technological Innovation for Applied AI Systems (DoCEIS 2021)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 626))

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Abstract

Production systems are moving towards new levels of smart manufacturing, which means that production processes become more autonomous, sustainable, and agile. Additionally, the increasing complexity and variety of individualized products makes manufacturing a challenge, since it must be produced by consuming the least number of resources, generating profitability. In mathematical perspective, most production planning problems, such as real-world scheduling and sequencing problems, are classified as NP-Hard problems, and there is most likely no polynomial-time algorithm for these kinds of problems. In addition, advances in information and communication technologies (ICT) are increasing, leading to an already existing trend towards real-time scheduling. In this context, the aim of this research is to develop matheuristic algorithms for the optimization of production planning in the supply chain. The development of matheuristic algorithms allows finding efficient solutions, achieving shorter computational times, providing companies with smart manufacturing skills to quickly respond to the market needs.

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Acknowledgments

Funding for this work has been provided by the Conselleria de Educación, Investigación, Cultura y Deporte - Generalitat Valenciana for hiring predoctoral research staff with Grant (ACIF/2018/170) and European Social Fund with Grant Operational Program of FSE 2014–2020, the Valencian Community.

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Authors and Affiliations

  1. Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València (UPV), Calle Alarcón, 03801, Alcoy, Alicante, Spain

    Eduardo Guzman, Beatriz Andres & Raul Poler

Authors
  1. Eduardo Guzman
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  2. Beatriz Andres
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  3. Raul Poler
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Correspondence to Eduardo Guzman .

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Editors and Affiliations

  1. NOVA University of Lisbon, Monte Caparica, Portugal

    Luis M. Camarinha-Matos

  2. NOVA University of Lisbon, Monte Caparica, Portugal

    Pedro Ferreira

  3. NOVA University of Lisbon, Monte Caparica, Portugal

    Guilherme Brito

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Guzman, E., Andres, B., Poler, R. (2021). Matheuristic Algorithms for Production Planning in Manufacturing Enterprises. In: Camarinha-Matos, L.M., Ferreira, P., Brito, G. (eds) Technological Innovation for Applied AI Systems. DoCEIS 2021. IFIP Advances in Information and Communication Technology, vol 626. Springer, Cham. https://doi.org/10.1007/978-3-030-78288-7_11

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  • DOI: https://doi.org/10.1007/978-3-030-78288-7_11

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