Generalized higher-level automated innovization with application to inventory management
2015 (English)In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 243, no 2, 480-496 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 243, no 2, 480-496 p.
Automated innovization, Higher-level innovization, Genetic programming, Inventory management, Knowledge discovery
IdentifiersURN: urn:nbn:se:his:diva-10693DOI: 10.1016/j.ejor.2014.11.015ISI: 000350834800012ScopusID: 2-s2.0-84923494070OAI: oai:DiVA.org:his-10693DiVA: diva2:789534
FunderKnowledge Foundation, 41128