Decision Maker Preference Modeling using SONIA-DNN and Its Application to Restaurant Work Assignment Problem

Abstract

To deal with a problem of incomplete comparisons in decision maker preference assessment, a SONIA (Self-Organized Network inspired by Immune Algorithm)-based Decision Neural Network (DNN) is proposed. The mutation mechanism of SONIA deals with a limited number of training data resulting from incomplete pair-wise comparisons by decision maker. Numerical experiments on Lp-metric function as underlying decision maker preference show that the error of SONIA-based DNN is 1/4 times lower than that of conventional DNN for decision maker preference assessment with incomplete comparisons. Real-world experiments on dish-up and oven related-work data of a chain restaurant work assignment problem show that the applicability of the proposed method to a real-world restaurant work manager preference modeling.