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Symbolic Artificial Intelligence Methods for Prescriptive Analytics

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Digital Transformation

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Abstract

Prescriptive analytics in supply chain management and manufacturing addresses the question of “what” should happen “when”, where good recommendations require the solving of decision and optimization problems in all stages of the product life cycle at all decision levels. Artificial intelligence (AI) provides general methods and tools for the automated solving of such problems.

We start our contribution with a discussion of the relation between AI and analytics techniques. As many decision and optimization problems are computationally complex, we present the challenges and approaches for solving such hard problems by AI methods and tools. As a running example for the introduction of general problem-solving frameworks, we employ production planning and scheduling.

First, we present the fundamental modeling and problem-solving concepts of constraint programming (CP), which has a long and successful history in solving practical planning and scheduling tasks. Second, we describe highly expressive methods for problem representation and solving based on answer set programming (ASP), which is a variant of logic programming. Finally, as the application of exact algorithms can be prohibitive for very large problem instances, we discuss some methods from the area of local search aiming at near-optimal solutions. Besides the introduction of basic principles, we point out available tools and practical showcases.

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Notes

  1. 1.

    The concept of operation types is not needed to define the FJSSP. However, this concept is often given in real environments representing machine abilities.

  2. 2.

    https://www.ibm.com/products/ilog-cplex-optimization-studio

  3. 3.

    In OPL, it is also possible to write (mixed) integer linear programs, quadratic or continuous programs.

  4. 4.

    This is similar to structs in the C language.

  5. 5.

    https://www.minizinc.org/challenge.html

  6. 6.

    https://www.ibm.com/analytics/cplex-cp-optimizer

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Acknowledgements

This work has been conducted in the scope of the research project DynaCon (FFG-PNr.: 861263), funded by the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT) under the program “ICT of the Future” between 2017 and 2020, the research project Productive4.0, funded by EU-ECSEL under grant agreement no737459, and the KWF project no28472, funded by cms electronics GmbH, FunderMax GmbH, Hirsch Armbänder GmbH, incubed IT GmbH, Infineon Technologies Austria AG, Isovolta AG, Kostwein Holding GmbH and Privatstiftung Kärntner Sparkasse. We thank Karen Meehan for proofreading and suggestions.

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  1. Institute for Artificial Intelligence and Cybersecurity, Universität Klagenfurt, Klagenfurt, Austria

    Gerhard Friedrich & Erich C. Teppan

  2. Institute for Artificial Intelligence and Cybersecurity/Institute of Software Technology, Universität Klagenfurt/TU Graz, Klagenfurt, Austria

    Martin Gebser

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  1. Technische Universität München, Garching b. München, Bayern, Germany

    Birgit Vogel-Heuser

  2. Johannes Kepler University Linz, Linz, Oberösterreich, Austria

    Manuel Wimmer

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Friedrich, G., Gebser, M., Teppan, E.C. (2023). Symbolic Artificial Intelligence Methods for Prescriptive Analytics. In: Vogel-Heuser, B., Wimmer, M. (eds) Digital Transformation. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-65004-2_16

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