Journal of Quality Engineering and Management

Abstract Purpose: This study aims to determine the optimal stock portfolio using a combination of the Markowitz model with Support Vector Machine (SVM), Data Envelopment Analysis (DEA), and the DBSCAN clustering algorithm. The statistical population consists of companies listed on the Tehran Stock Exchange from 2012 to 2022.
Methodology: To achieve the research objectives and form an optimal stock portfolio, dimensionality reduction approaches, DEA, SVM, and the DBSCAN clustering algorithm were employed. Financial ratios derived from balance sheets, income statements, and cash flow statements, as well as composite financial ratios and risk-return analysis based on the hybrid Markowitz model, were used as inputs to construct four portfolios.
Findings: The SVM method and the fourth approach, which includes the hybrid model, exhibited superior performance in optimizing the stock portfolio.
Originality/Value: Given the innovation of this research in applying the hybrid Markowitz model, the results can assist investors and stock analysts in managing the quality of an optimal stock portfolio.

Abstract Purpose: This study aims to propose a fair fixed cost allocation scheme among a set of Decision-Making Units (DMUs), such as banks or factories, in an uncertain environment. The allocation is designed so as not to reduce DMU efficiency and may even lead to efficiency improvements.
Methodology: To achieve this goal, a model is developed based on Data Envelopment Analysis (DEA) integrated with robust optimization. The inputs and outputs of the DMUs are treated as fuzzy random variables to reflect environmental uncertainty. The model is linearized and converted into a deterministic programming model using principles from stochastic programming. Furthermore, a common set of weights is used to ensure fairness in the allocation process.
Findings: The results indicate that, under the proposed fixed-cost allocation plan, the DMUs' (banks') efficiency scores are not only maintained but, in many cases, improved, confirming the model's effectiveness in preserving and enhancing performance under uncertain conditions.
Originality/Value: The novelty of this research lies in integrating DEA and robust optimization in uncertain environments to design a cost allocation model that ensures non-decreasing efficiency. Using a common set of weights enhances the approach's fairness. Additionally, applying the model to the Iranian banking sector highlights its practical relevance and managerial value.

Abstract  In recent years, many efforts have been made to achieve comprehensive criteria and models for evaluating companies. In this article, the aim is to obtain the efficiency of petrochemical companies based on human resource functions, operations and sales. The efficiency model can show better performance for calculating efficiency by considering human resource functions, operations and sales. 14 active petrochemicals are considered in the stock exchange and the concept model is based on three units of human resources, operations unit and sales unit. Data extracted from Codal site. The efficiency ranking of petrochemical industries is calculated with the same data by three methods. A network model is used to obtain efficiency and the results show the efficiency of petrochemicals 1, 10 and 11 in each model. The implementation of models shows that human resource functions, operations and sales can perform better. Also, the different results of the three models from each other show the importance of choosing a performance appraisal model for organizations. 
  

Abstract Abstract: Data envelopment analysis (DEA) is a nonparametric technique based on mathematical programming to evaluate performance of homogenous decision-making units (DMUs). Many DMUs have network structure where the output of one stage is the input to the next stage. A supply chain, which consists of several members such as supplier, manufacturer and customer, has multi-stage process. In this paper we, for the first time, introduce network DEA approaches to obtain the most productivity in a supply chain as a multi-stage DMU. Our models, by looking inside the internal structure of supply chain, consider the intermediate links between the stages. This perspective proposes managerial implications to improve the overall performance of a supply chain and the productivity of each member.