International Journal of Applied Operational Research

Identify the status of returns to scale of the multi-period two-stage systems and efficiency decomposition

Sh. Razavyan

In the present world, calculating the efficiency (or the performance) of systems with an internal structure, such as two-stage systems, is principally imperative during multi-time periods. In the present approach, the traditional two-stage Data Envelopment Analysis (DEA) model is developed to a multi-period two-stage DEA model, which evaluates the overall and periods of efficiencies synchronously. This approach which is used, is not only alone incapable of having functional capacities under the assumption of variable returns to scale (VRS), but is also inattentive to the importance or magnitude of data in different periods. In this study, in order to surmount these shortcomings, we expand the existing approaches and introduce a generalized model to measure the overall efficiency of a multi-period two-stage system under the VRS assumption, wherein, the importance of data in time- periods is considered in a diverse manner. According to this generalized model, theorems are also being presented to determine the type of returns to scale (RTS) of both stages, as well as the system of the entire time periods and also each period. Finally, the real data of Taiwanese non-life insurance companies, which has been extracted from the extant literature, is used to illustrate the proposed approach.

A novel method for clustering basketball players with data mining and hierarchical algorithm

M. R. Yamaghani

The issue of talent identification in basketball is significantly underrepresented in the existing literature, despite its undeniable significance in the field of sports. The objective of this research is to use artificial intelligence algorithms in the talent detection procedure for basketball athletes. The timely identification of specific aptitudes in teenage athletes is a critical determinant of their prospective achievements. This research used player clustering techniques that included both individual and group talents in order to establish a talent detection approach for foundational age groups. The present investigation was carried out on a sample of 70 participants, ranging in age from 14 to 16 years. The algorithm described in this study demonstrated the improved performance in hierarchical clustering by using a combined strategy. This resulted in a greater level of accuracy when compared to comparable research endeavors. The primary objective of this approach is to provide a proficient aide for coaches and talent scouts. The findings of this study demonstrate a clustering accuracy of over 94 percent when categorizing players according to their talents and abilities, as compared to the evaluations provided by experienced coaches involved in this research endeavor. In summary, the present methodology shows a greater degree of precision in comparison to approaches used in analogous research investigations.

Inverse data envelopment analysis for merging two-stage network systems

Gh. Tohidi

The inverse data envelopment analysis (InvDEA) technique is an applicable method in order to estimate the input/output levels of decision-making units (DMUs) to preserve predetermined technical efficiency scores. In the managerial atmosphere, the decision maker (DM) aims to merge two or more units and needs to know the input/output levels of the merged unit, while the efficiency score of the new unit is set, however, in some cases, the units have two-stage network structures. The main purpose of this paper is merging DMUs with two-stage network structures. To reach this goal, in this paper, an InvDEA method is presented in order to estimate inputs and the intermediate products of two-stage DMUs, to achieve the different predetermined efficiency scores which have been set by the DM.

Empirical mode decomposition and ANFIS network-based prediction technique for financial forecasting

M. Faridi Masouleh

A financial market is non-linear and chaotic in nature. So, the accurate prediction of foreign exchange rate is very difficult and challengeable task. Hence, many proposed techniques and new approaches are used for forecasting various countries' exchange rates with different parameters. This paper proposes EMD-ANFIS for foreign currency exchange rate prediction. In this research, we would like to propose a model which could develop multivariate exchange rates information and put these features to better use. The performance of the proposed system has been tested with European EURO against US Dollar (EUR/USD), British POUND against US Dollar (GBP/USD), US Dollar against Swiss FRANK (USD/CHF), US Dollar against Japanese YEN (USD/JPY) and used to predict one day exchange rate in advance. Empirical mode decomposition (EMD) and QPSO (Quantum Particle Swarm Optimization) are techniques that used here which generates optimal weight for the proposed model. The proposed approach has been found with the best prediction rate against previous studies.

Designing a model for optimizing reverse logistics based on waste management to reduce environmental impacts

M. Modiri

Solid waste has become one of the most critical environmental issues in the world. Therefore, a waste management system to prevent further destruction of the environment is essential. Waste management includes collection, transport, cleaning, recycling, and disposal of the wastes. In recent years, due to environmental concerns, manufacturers have been forced to offer environmentally friendly products. So, the area of reverse logistics (RL) has recently received considerable attention, due to a combination of environmental, economic, and social factors.
In this research, the design of a multi-product and eleven-level reverse logistics network is conducted, which collects all the waste in one place and separates them according to the needs of the factories (in terms of the type and material of the waste, etc.) and sends them to the intended destination. This model can support all kinds of industries in which the revival of recycling and destruction of products.
This study provides a mixed integer mathematical model to reduce the costs of the whole system. The number of centers, the number of products and parts that should be sent from one center to another, the amount of CO₂ emissions, and the total cost of the model were determined. Finally, the sensitivity analysis was done on the parameters of the model. The model was validated by changing the input data in two different cases.
Sensitivity analyses are conducted on various parameters to illustrate the capabilities of the proposed model. The results reveal that the allowed cost of CO₂ emissions has a significant effect on the value of the objective function.

Congestion estimation of selected Iranian electricity generation firms using data envelopment analysis

M. Ghiyasi

Since the beginning of the industrial renaissance and the advent of the machine age, the focus and planning of enterprises has been to maximize production. The key and leading model of societies from that time to the present day, manufacturing companies has always been the maximum use of resource minimization that means increasing productivity and efficiency and this will not be achieved unless this end is to improve productivity in the production chain. In energy-producing enterprises, including electricity fixed basic resources such as fuel consumption and labor are used in production, therefore to evaluate the performance of power generation companies, two important factors can be examined. In this study, by presenting a model based on data envelopment analysis the efficiency and congestion of fuel resources and labor force in selected power generation companies in Iran have been investigated. The results show that 15 companies under evaluation during the years 2005-2019 have the highest average total congestion attached to Bakhtar company at 52% and also Yazd and Kerman electricity companies on the frontier of efficiency without congestion. According to the studies, it was found that the congestion in the inputs in matter will cause inefficiency in the unit and deep erosion destruction in the production in restructure in long intervals.