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Generalized higher-level automated innovization with application to inventory management

Author

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  • Bandaru, Sunith
  • Aslam, Tehseen
  • Ng, Amos H.C.
  • Deb, Kalyanmoy

Abstract

This paper generalizes the automated innovization framework using genetic programming in the context of higher-level innovization. Automated innovization is an unsupervised machine learning technique that can automatically extract significant mathematical relationships from Pareto-optimal solution sets. These resulting relationships describe the conditions for Pareto-optimality for the multi-objective problem under consideration and can be used by scientists and practitioners as thumb rules to understand the problem better and to innovate new problem solving techniques; hence the name innovization (innovation through optimization). Higher-level innovization involves performing automated innovization on multiple Pareto-optimal solution sets obtained by varying one or more problem parameters. The automated innovization framework was recently updated using genetic programming. We extend this generalization to perform higher-level automated innovization and demonstrate the methodology on a standard two-bar bi-objective truss design problem. The procedure is then applied to a classic case of inventory management with multi-objective optimization performed at both system and process levels. The applicability of automated innovization to this area should motivate its use in other avenues of operational research.

Suggested Citation

  • Bandaru, Sunith & Aslam, Tehseen & Ng, Amos H.C. & Deb, Kalyanmoy, 2015. "Generalized higher-level automated innovization with application to inventory management," European Journal of Operational Research, Elsevier, vol. 243(2), pages 480-496.
    • Handle: RePEc:eee:ejores:v:243:y:2015:i:2:p:480-496
    DOI: 10.1016/j.ejor.2014.11.015
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    References listed on IDEAS

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    1. Sunith Bandaru & Kalyanmoy Deb, 2013. "Higher and lower-level knowledge discovery from Pareto-optimal sets," Journal of Global Optimization, Springer, vol. 57(2), pages 281-298, October.
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    Cited by:

    1. Linnéusson, Gary & Ng, Amos H.C. & Aslam, Tehseen, 2020. "A hybrid simulation-based optimization framework supporting strategic maintenance development to improve production performance," European Journal of Operational Research, Elsevier, vol. 281(2), pages 402-414.
    2. Pietronudo, Maria Cristina & Croidieu, Grégoire & Schiavone, Francesco, 2022. "A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    3. Adam Deptuła & Andrzej Augustynowicz & Michał Stosiak & Krzysztof Towarnicki & Mykola Karpenko, 2022. "The Concept of Using an Expert System and Multi-Valued Logic Trees to Assess the Energy Consumption of an Electric Car in Selected Driving Cycles," Energies, MDPI, vol. 15(13), pages 1-24, June.

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