The paper presents a case study related to a combination of artificial and natural intelligence in order to find the most effective sales strategy in retail. In particular, it proposes a solution which benefits from the combination of machine learning, genetic algorithms and simulation with a man in the loop approach to allow the decision maker to check additional proposals and to impose different constraints. The comparison of different techniques and their evaluation in terms of usability is presented.

Machine Learning and Genetic Algorithms to Improve Strategic Retail Management

Agostino G. Bruzzone;Kirill Sinelshchikov;Marina Massei;
2021-01-01

Abstract

The paper presents a case study related to a combination of artificial and natural intelligence in order to find the most effective sales strategy in retail. In particular, it proposes a solution which benefits from the combination of machine learning, genetic algorithms and simulation with a man in the loop approach to allow the decision maker to check additional proposals and to impose different constraints. The comparison of different techniques and their evaluation in terms of usability is presented.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1057245
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