Optimal logistics control of an omnichannel supply chain

Abstract

This paper aims to find the best way to control logistics in an omnichannel supply chain (OSC). For this purpose, two steps of work were carried out around case-based reasoning (CBR). In the first step, the combined feedback which proved stability was selected to control logistics in the single node, while the variational method and the virtual siphon were combined to determine the optimal control curve. There is a linear part and a nonlinear part in the combined feedback. The new method of storing data mode is "data turning to picture". In the second step, image features were extracted by the hybrid method of SURF-GoogLeNet and used for case matching via the grey cloud method. SURF-GoogLeNet was firstly used to update the weight proportion of the defect points in the whole image via the speeded up robust features (SURF) method and secondly to self-extract features using the GoogLeNet method. Finally, the effectiveness of the proposed methods was verified through experiments. The research findings shed new light on the management of supply chains.