Multi-agent systems negotiation to deal with dynamic scheduling in disturbed industrial context

Abstract

It is now accepted that using multi-agent systems improve the reactivity to treat perturbation(s) within flexible manufacturing system. Intelligent algorithms shall be used to address these perturbation(s) and all smart decision entities within their environment have to continuously negotiate until their common and final goal is achieved. This paper proposes a negotiation-based control approach to deal with variability on a manufacturing system. It has initially formulated and modeled an environment in which all contributing entities or agents operate, communicate, and interact with each other productively. Then after, simulation and applicability implementation experiments on the basis of full-sized academic experimental platform have been conducted to validate the proposed control approach. Product and resource entities negotiate considering different key performance measures in order to set best priority-based product sequencing. This has been done with expectations that the applicability of the negotiation-based decision-making will be more adaptable to deal with perturbation(s) than another alternative decision-making approach called pure reactive control approach. The result showed that negotiation among the decisional entities has brought significant improvement in reducing makespan and hence conveyed better global performance of a manufacturing system.

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adapted from Wooldridge (2009))

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Notes

  1. 1.

    Communication protocols enable agents to exchange and understand messages and interaction protocols enable agents to have either structured or unstructured conversations (Weiss, 1999).

  2. 2.

    Decision entities (or agents) are autonomous and cooperative components within manufacturing system with capability to show physical and informational communication behaviors and hence make decisions(El Haouzi et al. 2008).

  3. 3.

    TRACILOGIS platform is a technological platform located at wood technology Campus, Epinal, France. It represents a manufacturing system and allows studying different types of identification, traceability, and control approaches for products and logistic chains in wood industry. This witnesses the industrial applicability of the proposed approach for controlling disturbed shop floor in wood industries. Meanwhile, it is composed of four intelligent machines/resources to execute different activities.

  4. 4.

    JADE stands for Java Agent DEvelopment Framework.

  5. 5.

    Myopia is drawback of autonomous agents during their decision such as limited capacity to predict events (Rey et al. 2014).

  6. 6.

    Interaction media includes cooperation, collaboration, communication etc.

  7. 7.

    System is an entire working environment within a shop floor.

  8. 8.

    APICS stands for American Production and Inventory Control Society (12th edition).

  9. 9.

    A resource with highest product’s operation processing time.

  10. 10.

    Negotiation is a process by which a joint decision is reached by two or more agents, each one trying to reach an individual objective (Madureira et al., 2014).

  11. 11.

    R-squared is a statistical measure of how close data are to the fitted regression line.

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Acknowledgments

The authors gratefully acknowledge the financial support of the CPER 2015-2020 Projet Cyber-Entreprises du programme Sciences du numérique, through regional (Région Lorraine, Grand EST), national (DRRT, CNRS, INRIA) and European (FEDER) funds used to extend The TRACILOGIS Platform.

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Correspondence to Tsegay Tesfay Mezgebe.

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Mezgebe, T.T., Bril El Haouzi, H., Demesure, G. et al. Multi-agent systems negotiation to deal with dynamic scheduling in disturbed industrial context. J Intell Manuf 31, 1367–1382 (2020). https://doi.org/10.1007/s10845-019-01515-7

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Keywords

  • Negotiation
  • Control protocol
  • Multi-agent system
  • Intelligent decision
  • Distributed reactive
  • Makespan