The Experts below are selected from a list of 150 Experts worldwide ranked by ideXlab platform
Reza Farzipoor Saen - One of the best experts on this subject based on the ideXlab platform.
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Performance assessment of airlines using range-adjusted measure, strong Complementary Slackness Condition, and discriminant analysis
Journal of Air Transport Management, 2016Co-Authors: Mohammad Tavassoli, Taliva Badizadeh, Reza Farzipoor SaenAbstract:Abstract This study integrates RAM (range-adjusted measure), SCSC (strong Complementary Slackness Condition), and DEA–DA (data envelopment analysis–discriminant analysis) to rank airlines. As conventional DEA models do not fully use all inputs and outputs, they result zero in many multipliers. These sorts of DEA models may yield many efficient decision-making units (DMUs). This decreases the discrimination power of DEA. To overcome this limitation, this study proposes a novel application of RAM–DEA/SCSC along with DA. A case study demonstrates the applicability of our proposed approach.
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A new preference voting method for sustainable location planning using geographic information system and data envelopment analysis
Journal of Cleaner Production, 2016Co-Authors: Mohammad Izadikhah, Reza Farzipoor SaenAbstract:Abstract Supply chain operations with sustainability considerations have become an increasingly important issue in recent years and location planning for sustainable development plays an important role in guiding future of local, regional and national systems. Geographic information system is a technology for making better decisions about location. To solve location planning problem, in this paper, a new preference aggregation algorithm using Complementary Slackness Condition and discriminant analysis is developed. Applying a voting system for solving sustainable location planning problem is new and cannot be found in literature. Using geographic information system and factor analysis, a multiple attribute decision making problem is developed. In this paper, multiple attribute decision making problems are solved by our proposed method for ranking a voting system and also the sustainable location is obtained. A case study demonstrates efficiency of proposed method. For this purpose, the proposed method is used to determine the most appropriate locations for constructing agro-industries in Markazi province. In our real application, ten main criteria using obtained variances and eigen values are recognized. Seven locations are selected and ranked. Results show that Komain is the best location for constructing agro-industry and Komijan is the worst location.
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Ranking electricity distribution units using slacks-based measure, strong Complementary Slackness Condition, and discriminant analysis
International Journal of Electrical Power & Energy Systems, 2015Co-Authors: Mohammad Tavassoli, Gholam Reza Faramarzi, Reza Farzipoor SaenAbstract:This study integrates SBM (slacks-based measure), SCSC (strong Complementary Slackness Condition), and DEA–DA (data envelopment analysis–discriminant analysis) to rank electricity distribution units in Iran. DEA is a popular technique that many researchers use it to evaluate the performance of different organizations in public and private sectors. As conventional DEA models do not fully exploit information of all inputs and outputs, they result zero in many multipliers. These DEA models may yield many efficient decision making units (DMUs). This decreases discrimination power of the DEA. To rectify this shortcoming, this study proposes a novel application of SBM–DEA/SCSC along with DEA–DA to reduce number of efficient DMUs. Using the proposed approach, electricity distribution units are classified into efficient and inefficient groups based upon their efficiency scores. Then, DEA–DA is utilized to rank efficient DMUs. Sensitivity analysis validates the proposed approach.
Mika Goto - One of the best experts on this subject based on the ideXlab platform.
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Efficiency-based rank assessment for electric power industry: A combined use of Data Envelopment Analysis (DEA) and DEA-Discriminant Analysis (DA)
Energy Economics, 2012Co-Authors: Toshiyuki Sueyoshi, Mika GotoAbstract:Abstract This study discusses a combined use of DEA (Data Environment Analysis) and DEA–DA (Discriminant Analysis) to determine the efficiency-based rank of energy firms. This type of performance evaluation is important because we often have a difficulty in accessing a large sample on energy firms to derive reliable empirical results. The proposed approach is useful in dealing with such a limited number of energy firms, often found in previous DEA studies on energy industries in the world. The proposed approach uses DEA to classify energy firms into efficient and inefficient groups based upon their efficiency scores. Then, it utilizes DEA–DA to assess their efficiency scores and ranks. In this stage, we can find an adjusted efficiency score for each energy firm. The proposed approach provides us with the following analytical capabilities, all of which cannot be found in a conventional use of DEA in assessing energy firms. First, the proposed DEA approach can avoid zero in all multipliers on efficient energy firms by incorporating SCSC (Strong Complementary Slackness Condition) so that it can handle an occurrence of multiple reference sets and multiple projections. The DEA result classifies all energy firms into efficient and inefficient groups. Second, DEA–DA, applied to the two groups, evaluates all energy firms by an industry-wide evaluation, not depending upon a limited number of efficient energy firms in a reference set, as found in a conventional use of DEA. The analytical capability can reduce the number of efficient energy firms. Third, the proposed approach can provide their efficiency-based ranking scores. Finally, we can conduct a rank sum test based upon their ranking scores to obtain a statistical inference. As an application, this study uses the proposed approach to examine the performance of Japanese electric power industry. We find two economic implications. One of the two implications is that no major change has occurred in the operational performance of Japanese electric power industry because of Japanese sluggish economy from 2005 to 2009. The other implication indicates that there are strategic differences in the operation of Japanese electric power firms after the liberalization.
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a combined use of dea data envelopment analysis with strong Complementary Slackness Condition and dea da discriminant analysis
Applied Mathematics Letters, 2011Co-Authors: Toshiyuki Sueyoshi, Mika GotoAbstract:Abstract This study discusses a combined use of DEA (Data Environment Analysis) with SCSC (Strong Complementary Slackness Condition) and DEA–DA (Discriminant Analysis). Many studies use DEA to evaluate the performance of various organizations in private and public sectors. A conventional use of DEA is not perfect because it still contains zero in many multipliers. This implies that DEA does not fully utilize information on all inputs and outputs. As a result, DEA produces many efficient organizations. To overcome the methodological difficulty, this study proposes a new use of DEA/SCSC and DEA–DA to reduce the number of efficient organizations.
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A combined use of DEA (Data Envelopment Analysis) with Strong Complementary Slackness Condition and DEA–DA (Discriminant Analysis)
Applied Mathematics Letters, 2011Co-Authors: Toshiyuki Sueyoshi, Mika GotoAbstract:Abstract This study discusses a combined use of DEA (Data Environment Analysis) with SCSC (Strong Complementary Slackness Condition) and DEA–DA (Discriminant Analysis). Many studies use DEA to evaluate the performance of various organizations in private and public sectors. A conventional use of DEA is not perfect because it still contains zero in many multipliers. This implies that DEA does not fully utilize information on all inputs and outputs. As a result, DEA produces many efficient organizations. To overcome the methodological difficulty, this study proposes a new use of DEA/SCSC and DEA–DA to reduce the number of efficient organizations.
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Measurement of a linkage among environmental, operational, and financial performance in Japanese manufacturing firms: A use of Data Envelopment Analysis with strong Complementary Slackness Condition
European Journal of Operational Research, 2010Co-Authors: Toshiyuki Sueyoshi, Mika GotoAbstract:This study investigates a linkage among environmental, operational and financial performance in Japanese manufacturing industry. All manufacturing firms examined in this study are listed in Tokyo stock exchange market. We use DEA (Data Envelopment Analysis) as an evaluation methodology. This study finds that large firms have managerial capabilities to improve their operational and environmental performance. The improvement leads to the enhancement of their financial performance. However, we cannot find such a business linkage in small and medium-sized firms. They improve their operational performance and then direct themselves toward the improvement of their environmental performance. Their environmental performance is, not the first priority, the second priority for the small and medium-sized firms even though Japanese government is currently making a policy pressure on all manufacturing firms to pay attention to various environmental issues related to the global warming and climate change. The environmental protection policy is effective on only large Japanese manufacturing firms that have technological and financial capabilities for environmental protection.
Toshiyuki Sueyoshi - One of the best experts on this subject based on the ideXlab platform.
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Efficiency-based rank assessment for electric power industry: A combined use of Data Envelopment Analysis (DEA) and DEA-Discriminant Analysis (DA)
Energy Economics, 2012Co-Authors: Toshiyuki Sueyoshi, Mika GotoAbstract:Abstract This study discusses a combined use of DEA (Data Environment Analysis) and DEA–DA (Discriminant Analysis) to determine the efficiency-based rank of energy firms. This type of performance evaluation is important because we often have a difficulty in accessing a large sample on energy firms to derive reliable empirical results. The proposed approach is useful in dealing with such a limited number of energy firms, often found in previous DEA studies on energy industries in the world. The proposed approach uses DEA to classify energy firms into efficient and inefficient groups based upon their efficiency scores. Then, it utilizes DEA–DA to assess their efficiency scores and ranks. In this stage, we can find an adjusted efficiency score for each energy firm. The proposed approach provides us with the following analytical capabilities, all of which cannot be found in a conventional use of DEA in assessing energy firms. First, the proposed DEA approach can avoid zero in all multipliers on efficient energy firms by incorporating SCSC (Strong Complementary Slackness Condition) so that it can handle an occurrence of multiple reference sets and multiple projections. The DEA result classifies all energy firms into efficient and inefficient groups. Second, DEA–DA, applied to the two groups, evaluates all energy firms by an industry-wide evaluation, not depending upon a limited number of efficient energy firms in a reference set, as found in a conventional use of DEA. The analytical capability can reduce the number of efficient energy firms. Third, the proposed approach can provide their efficiency-based ranking scores. Finally, we can conduct a rank sum test based upon their ranking scores to obtain a statistical inference. As an application, this study uses the proposed approach to examine the performance of Japanese electric power industry. We find two economic implications. One of the two implications is that no major change has occurred in the operational performance of Japanese electric power industry because of Japanese sluggish economy from 2005 to 2009. The other implication indicates that there are strategic differences in the operation of Japanese electric power firms after the liberalization.
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a combined use of dea data envelopment analysis with strong Complementary Slackness Condition and dea da discriminant analysis
Applied Mathematics Letters, 2011Co-Authors: Toshiyuki Sueyoshi, Mika GotoAbstract:Abstract This study discusses a combined use of DEA (Data Environment Analysis) with SCSC (Strong Complementary Slackness Condition) and DEA–DA (Discriminant Analysis). Many studies use DEA to evaluate the performance of various organizations in private and public sectors. A conventional use of DEA is not perfect because it still contains zero in many multipliers. This implies that DEA does not fully utilize information on all inputs and outputs. As a result, DEA produces many efficient organizations. To overcome the methodological difficulty, this study proposes a new use of DEA/SCSC and DEA–DA to reduce the number of efficient organizations.
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A combined use of DEA (Data Envelopment Analysis) with Strong Complementary Slackness Condition and DEA–DA (Discriminant Analysis)
Applied Mathematics Letters, 2011Co-Authors: Toshiyuki Sueyoshi, Mika GotoAbstract:Abstract This study discusses a combined use of DEA (Data Environment Analysis) with SCSC (Strong Complementary Slackness Condition) and DEA–DA (Discriminant Analysis). Many studies use DEA to evaluate the performance of various organizations in private and public sectors. A conventional use of DEA is not perfect because it still contains zero in many multipliers. This implies that DEA does not fully utilize information on all inputs and outputs. As a result, DEA produces many efficient organizations. To overcome the methodological difficulty, this study proposes a new use of DEA/SCSC and DEA–DA to reduce the number of efficient organizations.
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Measurement of a linkage among environmental, operational, and financial performance in Japanese manufacturing firms: A use of Data Envelopment Analysis with strong Complementary Slackness Condition
European Journal of Operational Research, 2010Co-Authors: Toshiyuki Sueyoshi, Mika GotoAbstract:This study investigates a linkage among environmental, operational and financial performance in Japanese manufacturing industry. All manufacturing firms examined in this study are listed in Tokyo stock exchange market. We use DEA (Data Envelopment Analysis) as an evaluation methodology. This study finds that large firms have managerial capabilities to improve their operational and environmental performance. The improvement leads to the enhancement of their financial performance. However, we cannot find such a business linkage in small and medium-sized firms. They improve their operational performance and then direct themselves toward the improvement of their environmental performance. Their environmental performance is, not the first priority, the second priority for the small and medium-sized firms even though Japanese government is currently making a policy pressure on all manufacturing firms to pay attention to various environmental issues related to the global warming and climate change. The environmental protection policy is effective on only large Japanese manufacturing firms that have technological and financial capabilities for environmental protection.
Joe Zhu - One of the best experts on this subject based on the ideXlab platform.
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Multiplier bounds in DEA via strong Complementary Slackness Condition solution
International Journal of Production Economics, 2003Co-Authors: Yao Chen, Hiroshi Morita, Joe ZhuAbstract:Abstract Data envelopment analysis (DEA) is a methodology for evaluating the relative efficiency of peer decision making units (DMUs) with multiple inputs and multiple outputs. In DEA evaluation, the efficiency scores for inefficient DMUs are obtained by calculating the proportional input or output Changes required to reach the DEA efficient frontier. It is likely that further individual input and output changes can be made to improve the performance. Such individual changes are called DEA slacks which also represent inefficiency. Methods have been developed to deal with the non-zero DEA slacks by re-shaping the original DEA frontier. This paper develops an alternative approach to eliminate the non-zero DEA slacks while keeping the original DEA frontier unchanged.
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An Approach for Determining DEA Efficiency Bounds
Multi-Objective Programming and Goal Programming, 2003Co-Authors: Yao Chen, Hiroshi Morita, Joe ZhuAbstract:Constrained facet analysis is used to evaluate decision making units (DMUs) which have non-zero slacks in data envelopment analysis (DEA) by requiring a full dimensional efficient facet (FDEF). The current paper shows that the FDEF-based approach may deem those extreme efficient DMUs which are not located on any FDEF as inefficient. Using strong Complementary Slackness Condition (SCSC) solutions, this paper develops an alternative method for the treatment of non-zero slack values in DEA. The newly proposed method can deal with the situation when FDEFs do not exist.
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Chapter 15 DEA/AR analysis of the 1988–1989 performance of the Nanjing textiles corporation
Annals of Operations Research, 1996Co-Authors: Joe ZhuAbstract:This article employs new data envelopment analysis/assurance region (DEA/AR) methods to evaluate the efficiency of the 35 textile factories of the Nanjing Textiles Corporation (NTC), Nanjing, China. The returns to scale (RTS) of these factories were studied without assuming that the optimal DEA solutions were unique. All DMUs are identified with points E (Extreme Efficient), E ′ (Efficient but not an extreme point) and F (Frontier but not efficient). We then further identify the nonfrontier DMUs with points NE, NE ′ and NF according to whether they are projected onto a point in E, E ′, or F en route to evaluating their performances. All of the inefficient factories were in class NF and had unique optimal primal-dual solution pairs. Consequently, the solution pairs satisfy the strong Complementary Slackness Condition (SCSC). Application of cone-ratio (CR) ARs reduced significantly the number of factories in class E , and showed that some AR-efficient factories were more flexible in adopting the mixture of central planning and market economies that China currently is trying to use. Also, linked-cone (LC) ARs were applied to measure maximum and minimum profit ratios. The SCSC multiplier space approach was utilized to analyze the sensitivity of the efficiency results to potential errors in the data with and without ARs. The results in this article suggest that collective units had a better performance than state-owned units in the two consecutive years analyzed.
Mohammad Tavassoli - One of the best experts on this subject based on the ideXlab platform.
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Performance assessment of airlines using range-adjusted measure, strong Complementary Slackness Condition, and discriminant analysis
Journal of Air Transport Management, 2016Co-Authors: Mohammad Tavassoli, Taliva Badizadeh, Reza Farzipoor SaenAbstract:Abstract This study integrates RAM (range-adjusted measure), SCSC (strong Complementary Slackness Condition), and DEA–DA (data envelopment analysis–discriminant analysis) to rank airlines. As conventional DEA models do not fully use all inputs and outputs, they result zero in many multipliers. These sorts of DEA models may yield many efficient decision-making units (DMUs). This decreases the discrimination power of DEA. To overcome this limitation, this study proposes a novel application of RAM–DEA/SCSC along with DA. A case study demonstrates the applicability of our proposed approach.
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Ranking electricity distribution units using slacks-based measure, strong Complementary Slackness Condition, and discriminant analysis
International Journal of Electrical Power & Energy Systems, 2015Co-Authors: Mohammad Tavassoli, Gholam Reza Faramarzi, Reza Farzipoor SaenAbstract:This study integrates SBM (slacks-based measure), SCSC (strong Complementary Slackness Condition), and DEA–DA (data envelopment analysis–discriminant analysis) to rank electricity distribution units in Iran. DEA is a popular technique that many researchers use it to evaluate the performance of different organizations in public and private sectors. As conventional DEA models do not fully exploit information of all inputs and outputs, they result zero in many multipliers. These DEA models may yield many efficient decision making units (DMUs). This decreases discrimination power of the DEA. To rectify this shortcoming, this study proposes a novel application of SBM–DEA/SCSC along with DEA–DA to reduce number of efficient DMUs. Using the proposed approach, electricity distribution units are classified into efficient and inefficient groups based upon their efficiency scores. Then, DEA–DA is utilized to rank efficient DMUs. Sensitivity analysis validates the proposed approach.