Generalized higher-level automated innovization with application to inventory management

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

European Journal of Operational Research, 2015, vol. 243, issue 2, 480-496

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.

Keywords: Automated innovization; Higher-level innovization; Genetic programming; Inventory management; Knowledge discovery (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:243:y:2015:i:2:p:480-496

DOI: 10.1016/j.ejor.2014.11.015

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