Hybrid cluster analyzing and data envelopment analysis with interval data

Despite its intrinsic advantageous features as a tool for increasing discrimination power of the basic DEA (data envelopment analysis) model, augmented DEA has two main drawbacks including the presence of unrealistic efficiency scores and the presence of great distance between its efficiency scores and scores obtained by primary model. In this regard, this paper extends a heuristic method for dealing with both issues and improving the power of augmented DEA model in performance evaluation. Since different virtual DMUs lead to different results for ranking, the hierarchical clustering algorithm is applied in this study to select the best virtual DMUs in order to reduce the possibility of having inappropriate efficiency scores. Finally, to demonstrate the superiority of the proposed approach over previous approaches in literature, two numerical examples are provided.

Section: Literature Reviewmentioning

confidence: 99%

A heuristic method for choosing 'virtual best' DMUs to enhance discrimination power of augmented DEA model

Rezaei

Haeri

2019

“…In this statistical method, data is classified according to their measure of relation. Therefore, members of a cluster have the most similarity with each other and the least similarity with other clusters [53]. There are two main discussions in clustering: the selection of the number of clusters and the selection of similarity criterion between members.…”

Section: Cluster Analysismentioning

confidence: 99%

The effect of transportation and safety attitudes, personality, and lifestyle on vehicle type choice: a case study of Tehran

Mohammadi

Kermanshah

2019

In recent years, researchers have paid specific attention to safety, air pollution, and energy consumption, which are all affected by the vehicle type. The primary objective of this study is to achieve a thorough understanding of the relationships between transportation attitudes, safety attitudes, personality, as well as lifestyle and vehicle type for the first time in Iran, as a developing country. For this purpose, an interview questionnaire was designed, and 740 participants were interviewed in Tehran. Vehicle types are categorized into seven groups: mini, compact, medium, heavy, SUV, pickup, and motorcycle. In the first stage, factor analysis was used to identify the latent variables regarding transportation attitudes, safety attitudes, personality, and lifestyle. In the second step, by using these latent variables, individuals are classified, and a two-level clustering is formed. The correlation analysis performed in the third step shows a significant relationship between each category of people and the type of car. Likewise, the analyses carried out in this study reveal the significant relations between the vehicle type (classified based on fuel economy level, production country, vehicle emission, and vintage) and the attitudes of its owner. This information is beneficial to both vehicle manufacturers and transportation planners.

“…In previous studies, performance evaluation is less considered in production planning, and few studies have focused on the performance evaluation in the field of production planning [3][4][5]. Data Envelopment Analysis (DEA) is an approach to estimating the relative efficiency of Decision-Making Units (DMUs) [6]. On the other hand, DEA is regarded as a powerful mathematical tool for measuring the relative efficiency of DMUs that utilize inputs to produce outputs [7], and is a nonparametric approach that does not require any assumption about the functional form of production [8].…”

Section: Introduction *mentioning

confidence: 99%

Performance Evaluation in Aggregate Production Planning Using Integrated RED-SWARA Method under Uncertain Condition

Khalili

Alinezhad

2020

Nowadays, increasing the efficiency of production and determining the tools and methods of measuring performance are the biggest challenges faced by managers by increasing competition among companies and manufacturing centers. In the aggregate production planning, along with the performance evaluation in order to reduce the waste of resources due to the common use of resources for product family production. The present study aimed to evaluate the performance in the Aggregate Production Planning (APP). In this regard, the optimal values were determined by the multi-objective Grey Aggregate Production Planning (GAPP) model, and the weights of the input and output indicators for the performance evaluation were characterized by the Step-wise Weight Assessment Ratio Analysis (SWARA) method. Further, the efficiency of Decision-Making Units (DMUs) was determined by the Ratio Efficiency Dominance (RED) model. Then, the ranking of DMUs was conducted. In the case study of automobile parts manufacturing industry in Iran, the sensitivity analysis was performed on the proposed model and its effects were evaluated. The results indicated that the proposed model had a high degree of accuracy in evaluating performance, compared to previous models, and helps managers to make better decisions to increase the efficiency and reduce the waste of resources.