perfomance management
ebrahim golzar; seyyed esmaeil najafi; seyyed ahmad edalatpanah; Amir Azizi
Abstract
Undesirable outputs are an integral part of production in various decision-making units, and to bring analyses closer to the real world, it is necessary to consider, undesirable outputs in performance evaluation research. In this paper, a new hybrid model for evaluating the efficiency of decision-making ...
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Undesirable outputs are an integral part of production in various decision-making units, and to bring analyses closer to the real world, it is necessary to consider, undesirable outputs in performance evaluation research. In this paper, a new hybrid model for evaluating the efficiency of decision-making units in the oil industry is presented, which uses slack-based data envelopment analysis techniques and advanced machine learning algorithms. The proposed model specifically focuses on improving efficiency considering undesirable outputs and conditions of uncertainty. Three machine learning algorithms including artificial neural networks, support vector machines, and XGBoost are used to predict and improve the results of slack-based models. This study involves the evaluation of 37 decision-making units within the National Petroleum Products Distribution Company, and the results show a significant improvement in efficiency using predicted data compared to actual data. This research not only contributes to new perspectives in efficiency evaluation and improvement but also offers innovative hybrid methods to address challenges in operational management.
hossein mohebbi; adel azar; Abasali Heidari; Ameneh Khadivar
Abstract
Nowadays, most supply chains are starting to go green in their business with pay more attention to environmental protection as competitive advantage. Therefore, Designing a two-stage green supply chain for optimum assignment a green supplier to a green producer based on maximum efficiency and attention ...
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Nowadays, most supply chains are starting to go green in their business with pay more attention to environmental protection as competitive advantage. Therefore, Designing a two-stage green supply chain for optimum assignment a green supplier to a green producer based on maximum efficiency and attention to intermediate products and processes is essential. Because, economic performance and environmental performance of the supply chain will increases. One of the methods used to evaluating efficiency in the green supply chain management, is data envelopment analysis (DEA). The traditional DEA methods for evaluating efficiency of supply chain processes and multi-stage systems not working properly, Because, each decision making units is assumed as a black box and ignore its internal processes. In order to overcome this deficiency, a novel two-stage network DEA will be presented based on the concepts of Electrical Engineering that ability to consider all inputs, Intermediate products, desirable and undesirable outputs between supplier and producer in the green supply chain for optimum assignment a supplier to producer based on maximum efficiency. The proposed model has been described by an application example and Its reliability has been confirmed.
