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The ASSISTANT project: AI for high level decisions in manufacturing

  • G. Castañé
  • , A. Dolgui
  • , N. Kousi
  • , B. Meyers
  • , S. Thevenin
  • , E. Vyhmeister
  • , P. O. Östberg
  • University College Cork
  • UBL
  • University of Patras
  • Flanders Make
  • Umeå University

Research output: Contribution to journalArticlepeer-review

Abstract

This paper outlines the main idea and approach of the H2020 ASSISTANT (LeArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments) project. ASSISTANT is aimed at the investigation of AI-based tools for adaptive manufacturing environments, and focuses on the development of a set of digital twins for integration with, management of, and decision support for production planning and control. The ASSISTANT tools are based on the approach of extending generative design, an established methodology for product design, to a broader set of manufacturing decision making processes; and to make use of machine learning, optimisation, and simulation techniques to produce executable models capable of ethical reasoning and data-driven decision making for manufacturing systems. Combining human control and accountable AI, the ASSISTANT toolsets span a wide range of manufacturing processes and time scales, including process planning, production planning, scheduling, and real-time control. They are designed to be adaptable and applicable in a both general and specific manufacturing environments.

Original languageEnglish
Pages (from-to)2288-2306
Number of pages19
JournalInternational Journal of Production Research
Volume61
Issue number7
DOIs
Publication statusPublished - 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • Artificial intelligence
  • data analytics
  • digital twins
  • process and production planning
  • reconfigurable manufacturing systems
  • scheduling and real-time control

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