Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA-III

Abstract

Establishing a rapid-response mechanism to manage customer orders is very important in managing demand surges. In this study, combined with predicting order requests, we established a multiobjective optimization model to solve the warehouse space allocation problem. First, we developed a model based on the NAR neural network to predict order requests. Subsequently, we used the improved NSGA-III based on good point set theory to construct a multiobjective optimization model to minimize resource loss, maximize efficiency in goods selection, and maximize goods accumulation. The following three modes were tested to allocate warehouse storage space: random, ABC, and prediction-oriented. Finally, using actual order data, we conducted a comparative analysis of the three modes regarding their efficiency in goods selection. The method proposed by this study improved goods selection efficiency by a sizable margin (23.8%).