# Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method
## 1 Introduction  
The transportation industry plays an essential role in national economics, while vehicles may suffer downtime from spare parts being out of stock. For spare parts classification management, decision-makers should select optimal inventory strategies in terms of cost and reliability. One third of the maintenance support cost of an industrial system may be spent on stocking ample spare parts for timely maintenance [1]. It is especially challenging to manage tens of thousands of spare parts necessary for modern industry. Thus, classification has been an important technique in the spare parts management that can support decision-makers in strategizing to allocate inventory with optimal cost and high reliability [2, 3]. Spare parts classification has been extensively investigated by scholars [4, 5, 6]. Different service backgrounds bring about significant differences in the criteria selection and algorithms adopted by decision-makers. Among the traditional spare parts classification methods, both ABC and VED analyses are simple and basic methods in which spare parts are arranged according to only one criterion. The ABC method was first proposed by [PERSON] [7] and [PERSON] et al. [8] to divide spare parts into categories (A, B, and C) using total annual cost and inventory criteria. VED analysis is a simple qualitative approach to decision-making based on spare parts \"criticality\" as defined by experienced experts. In practice, however, they do not entirely reflect the management requirements for spare parts because a single criterion is too subjective. As proposed in 1988 [9], a good-structured spare parts classification should be a value assessment systemthat requires the combination of maintenance engineers, material managers, quality control personnel, and other relevant experts. Therefore, [PERSON] [10] first proposed a second-order matrix of supply risk and criticality in view of multi-criteria, and other scholars have also applied this principle to develop new multi-criteria classification methods such as AHP (Analytic Hierarchy Process) and similar hierarchical-based analysis. [PERSON] [11] proposed the Multi-Attribute Spare parts Tree Analysis (MASTA) method, which uses a decision tree and AHP to assign calculations to various criteria. [PERSON] and [PERSON] [12] combined AHP with fuzzy logic to capture the uncertainty in an evaluation indicator and export priority values from a judging set via fuzzy optimization. [PERSON] et al. [13] proposed an algorithm that integrates AHP, fuzzy evaluation, and gray association analysis to translate qualitative descriptions into quantitative data for spare parts classification in a limited and uncertain environment. [PERSON] [14] also employed fuzzy AHP to determine the relative weights of attributes/criteria in a given inventory. [PERSON] [15] used an optimized approach to solve the weights of each criterion in a classification system--assuming that each criterion for all spare parts falls into a descending order--and finally translated the score of each spare part into a uniform scale score. Based on the Ng model, [PERSON] et al. [16] calculated the weight of each spare parts in a system using a nonlinear optimization algorithm as the basis for the subsequent calculation of the total score. Though effective in some regards, AHP-based methods are still not well-suited to real-world spare parts classification due to the subjective matrix assignments and limited learning ability. In order to overcome the defects in multi-criteria models regarding to weight assignment and reclassification learning ability, scholars have attempted to optimize heuristic intelligent algorithms such as Data Envelopment Analysis (DEA), Support Vector Machine (SVM), Artificial Neural Network (ANN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), gray theory, fuzzy logic, and combinations thereof. [PERSON] [17] compared SVM, backpropagation (BP), K-Nearest Neighbors (KNN) and other methods to find that artificial intelligence-based methods are relatively more accurate.