Congestion in multi-function parallel network DEA.

Congestion is an economic phenomenon of the production process in which the excessive values of inputs lead to a reduction of the outputs. As the existence of congestion makes to increase costs and decreases efficiency, this issue is not acceptable for decision makers. Hence, many methods have been proposed to detect the congestion in the Data Envelopment Analysis framework (DEA). Most of these methods are designed to deal with the decision making units (DMUs) that have no network structure. However, in most real-world applications, some units are composed of independent production subunits. Therefore, a new scheme is required to determine the congestion of such units. A multi-function parallel system is a more common case in the real world that is composed of the same number of subunits such that each subunit has specific functions. In this paper, considering the operation of individual components of each DMU, a new DEA model is proposed to identify and evaluate the congestion of the multi-function parallel systems. It is shown that the proposed method is highly economical in comparison with the existing black-box view from a computational viewpoint. Then, the proposed model is illustrated using a numerical example along with a real case study.

A novel algorithm for complete ranking of DMUs dealing with negative data using Data Envelopment Analysis and Principal Component Analysis: Pharmaceutical companies and another practical example.

When there is an extensive number of inputs and outputs compared to the number of DMUs, one of the drawbacks of Data Envelopment Analysis appears, which incorrectly classifies inefficient DMUs, as efficient ones. Accordingly, the DEA ranking power becomes further moderated. To improve the ranking power, this paper renders the details of an algorithm that presents a model combining the Principal Component Analysis and the Slacks-Based Measure (PCA-SBM) which reduces the number of the incorrectly determined efficient DMUs. Also to complete ranking of DMUs, the algorithm presents a Super-Efficiency model integrated with PCA (PCA-Super SBM) which can rank the efficient DMUs (extreme and non-extreme). Whereas the most important previous models for ranking efficient units cannot rank non-extreme ones. Additionally, in most previous studies, DEA models combined with PCA fail to handle negative data, while, the presented models can cover this data. Two case studies (pharmaceutical companies listed on the Iranian stock market and bank branches) are manipulated to demonstrate the applicability and performance of the algorithm. To show the superiority of the presented models, the SBM model without PCA and the Super SBM model without PCA have been implemented on the data of both cases. In comparing the two methods (PCA-SBM and SBM), the PCA-SBM model has higher ranking power (five efficient DMUs versus nineteen in the case of pharmaceutical companies and four efficient DMUs versus twenty-nine in the case of bank branches). Also in comparing the PCA-Super SBM and Super SBM, the PCA-Super SBM model works more powerfully in complete ranking. As the Super SBM model cannot rank non-extreme units unlike the PCA-Super SBM. Consequently, the presented algorithm works successfully in ranking the DMUs completely (inefficient, extreme, and non-extreme efficient) with low complexity.

Modeling land use/cover change based on LCM model for a semi-arid area in the Latian Dam Watershed (Iran).

The monitoring and modeling of changes, based on a time-series LULC approach, is fundamental for planning and managing regional environments. The current study analyzed the LULC changes as well as estimated future scenarios for 2027 and 2037. To achieve accuracy in predicting LULC changes, the Land Change Modeler (LCM) was used for the Latian Dam Watershed, which is located approximately in the northeast of Tehran. The LULC time-series technique was specified utilizing four atmospherically endorsed surface reflectance Landsat images for the years t1 (1987), t2 (1998), t3 (2007), and t4 (2017) to authenticate the LULC predictions, so to obtain estimates for t5 (2027) and t6 (2037). The LULC classes identified in the watershed were water bodies, build-up areas, vegetated areas, and bare lands. The dynamic modeling of the LULC was based on a multi-layer perceptron (MLP), the neural network in LCM, which presented good results with an average accuracy rate equivalent to 84.89 percent. The results of the LULC change analysis showed an increase in the build-up area and a decrease in bare lands and vegetated areas within the duration of the study period. The results of this research could help in the formulation of public policies designed to conserve environmental resources in the Latian Dam Watershed and, consequently, minimize the risks of the fragmentation of orchards and vegetated areas. Also, careful regional planning ensuring the preservation of natural landscapes and open spaces is critical to creating a resilient regional environment and sustainable development.

Stability analysis with general fuzzy measure: An application to social security organizations.

An effective method for evaluating the efficiency of peer decision-making units (DMUs) is data envelope analysis (DEA). In engineering sciences and real-world management problems, uncertainty in input and output data always exists. To achieve reliable results, uncertainties must be taken into account. In this research, a General Fuzzy (GF) approach is designed to cope with uncertainty in the presence of fuzzy observations for categorizing and specifying stability radius and alterations ranges of efficient and inefficient DMUs, which is applicable to real-world decision-making problems. For this purpose, a DEA sensitivity analysis model is presented, which will be modeled by fuzzy sets. Then, by applying the General Fuzzy (GF) approach, the fuzzy DEA sensitivity analysis model is transformed into the equivalent crisp form of fuzzy chance constraints according to specific confidence levels. Finally, a numerical example and a case study of branches of the social security organization are presented to illustrate sensitivity and stability analysis in the presence of fuzzy data. The obtained results provide the input and output changes of the evaluated units according to the attitude and preference of the decision maker with different confidence levels so that the data changes in the fuzzy environment do not change the units' classification from efficient to inefficient and vice versa.

Health, safety and environmental unit performance assessment model under uncertainty (case study: steel industry).

Performance assessment is a critical objective of management systems. As a result of the non-deterministic and qualitative nature of performance indicators, assessments are likely to be influenced by evaluators' personal judgments. Furthermore, in developing countries, performance assessments by the Health, Safety and Environment (HSE) department are based solely on the number of accidents. A questionnaire is used to conduct the study in one of the largest steel production companies in Iran. With respect to health, safety, and environment, the results revealed that control of disease, fire hazards, and air pollution are of paramount importance, with coefficients of 0.057, 0.062, and 0.054, respectively. Furthermore, health and environment indicators were found to be the most common causes of poor performance. Finally, it was shown that HSE management systems can affect the majority of performance safety indicators in the short run, whereas health and environment indicators require longer periods of time. The objective of this study is to present an HSE-MS unit performance assessment model in steel industries. Moreover, we seek to answer the following question: what are the factors that affect HSE unit system in the steel industry? Also, for each factor, the extent of impact on the performance of the HSE management system in the organization is determined.