Abnormal operation status identification in warehousing based on neighborhood information entropy considering mixed-valued attributes

A warehousing system is critical to enterprises, as a connection between supply and demand in a supply chain. However, an abnormal operation status (AS) may appear in actual production operations, especially in a developing warehousing system. In this study, to identify an AS in a warehousing system, mixed-valued attributes are used to describe the warehousing operation status, and an integration method is performed based on neighbourhood information entropy. First, the neighbourhood information system is structured. A distance function and neighbourhood radius are defined for numeric data and categorical data, respectively, to eliminate information loss from transforming different types of attributes. Second, the relative neighbourhood information entropy, abnormal degree, and abnormal factor are gradually defined. Third, an evaluation index is defined to measure the identification accuracy in parameter adjustment for two key parameters: the adjustment parameter for the neighbourhood radius (λ), and the discrimination threshold for AS (μ). Finally, a real case study of AS identification in a manufacturing enterprise is implemented to demonstrate the effectiveness of the proposed method, and the identification results are analysed from the practical point of view.