## 1 Introduction  
To facilitate the successful management of a growing number of stock-keeping units (SKUs), inventory managers have found that inventory classification systems provide essential context for evaluating inventory management. ABC analysis is one of the most frequently used inventory classification techniques. Raw materials, subassemblies, intermediate products, parts, and end products can be divided into three classes: A (very important items), B (moderately important items), and C (relatively unimportant items). The ABC classification problem is presented as a ranking problem by the most current classification models [1; 2; 3]; that is, a group of inventory items is represented according to its overall weighted score of criteria in descending order. The idea of ABC analysis was applied to the inventory management at General Electric during the 1950s. This approach is based on [PERSON]'s famous theory of inequality in the distribution of incomes. A conventional ABC study is conducted on the basis of one criterion: the annual dollar usage (value of an item times its annual usage) of SKUs. Under [PERSON]'s theory, all items are ranked based on a single criterion; within inventory management, dollar usage is the only criterion for managers to classify items into the A, B, and C categories. However, managers sometimes want to consider more attributes of an item when classifying goods. Many item characteristics could influence inventory control policy and must be considered. [PERSON] (2018) noted that other vital criteria can be adopted in addition to dollar usage, such as commonality, reparability, substitutability, lead time, and commonality. For instance, an enterprise must pursue efficient operations that can both minimize total costs and maximize satisfaction brought to their internal or external customers. If SKUs are only classified based on the single criterion of dollar usage, an item with a lower dollar usage, but a long lead time and high criticality, would be misclassified into the C category, resulting in serious damage to the company if the item were to run out of stock. Detailed literary research has been carried out on multi-choice programming (MCP) theories and applications. MCP is a branch of multi-objective programming that stems from multiple-criteria decision-making (MCDM). MCDM tests several overlapping criteria of decision-making in various areas ([PERSON] and others, 2018; [PERSON] et al., 2018). Multi-criteria inventory classification (MCIC) can be viewed as an application of multi-criteria decision analysis ([PERSON] and others, 2018; [PERSON] et al., 2018). To solve the MCIC problem, the joint criteria matrix ([PERSON] et al., 2018) is a simple and easy-to-understand tool, but it is not practical for more than two criteria and involves too much subjectivity. The analytic hierarchy process (AHP) is a popular methodology, but it involves subjectivity as well. Methods for solving the ABC inventory classification problem have been systematically and thoroughly reviewed and discussed in the relevant literature ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). A number of methods were suggested in order to achieve multi-criteria classification of SKUs. These methods contribute much to the classification of items and help improve the efficiency and performance of a firm through better inventory management. However, these approaches contain some shortcomings, such as involving too much subjectivity or being overly complicated. To facilitate better allocation of the priorities of items and further classification, it is worth developing a model that can accommodate multiple criteria to create guidelines for inventory control. This study builds a proper model for categorizing SKUs and demonstrates an efficient algorithm for solving the nonlinear programming model, in which the feasible solution set does not have to be convex. The rest of this paper is structured as follows. Section 2 provides the details of the model's development.