•We examine GPDEA models in relation to the MCDEA framework.•GPDEA models perform poorly in weight dispersion and discrimination power.•The proposed BiO-MCDEA model outperforms GPDEA models in all ...datasets tested.
Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches; that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said problems in a multi-objective framework. We found GPDEA models to be invalid and demonstrate that our proposed bi-objective multiple criteria DEA (BiO-MCDEA) outperforms the GPDEA models in the aspects of discrimination power and weight dispersion, as well as requiring less computational codes. An application of energy dependency among 25 European Union member countries is further used to describe the efficacy of our approach.
•We examine DEA models with asymmetric inputs-outputs in the energy context.•The model accounts for both crisp and fuzzy efficiency measures across α-levels.•The model handles undesirable outputs ...without producing overly optimistic results.•The computation of the proposed model requires fewer procedures.
Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency of alternatives based on their inputs and outputs. The alternatives can be in the form of countries who attempt to enhance their productivity and environmental efficiencies concurrently. However, when desirable outputs such as productivity increases, undesirable outputs increase as well (e.g. carbon emissions), thus making the performance evaluation questionable. In addition, traditional environmental efficiency has been typically measured by crisp input and output (desirable and undesirable). However, the input and output data, such as CO2 emissions, in real-world evaluation problems are often imprecise or ambiguous. This paper proposes a DEA-based framework where the input and output data are characterized by symmetrical and asymmetrical fuzzy numbers. The proposed method allows the environmental evaluation to be assessed at different levels of certainty. The validity of the proposed model has been tested and its usefulness is illustrated using two numerical examples. An application of energy efficiency among 23 European Union (EU) member countries is further presented to show the applicability and efficacy of the proposed approach under asymmetric fuzzy numbers.
Growing environmental concern regarding multi elements-contaminated soils reveals the necessity of paying more attention to environmentally friendly remediation techniques such as phytoremediation. A ...large number of factors influences phytoremediation of potentially toxic elements (PTEs) and investigation on a variety of these factors need appropriate statistical approaches such as “Taguchi optimization” which effectively decreases time and cost of experiments. In the present study, based on the Taguchi optimization method, the effects of several biological (plant type and mycorrhizal fungi (AMF)) and chemical (chelating agents, surfactants and organic acids) factors, on the phytoremediation of soils contaminated with zinc (Zn), lead (Pb), cadmium (Cd) and nickel (Ni) were investigated. The goal was to find out the most effective factors as well as the best level for each factor. The values of dry weights in roots and aerial parts of the studied plants were in orders of maize > sorghum > sunflower and sorghum > maize > sunflower, respectively. AMF was the main factor in increasing dry weight of shoots. Inoculation of AMF caused increases in root and shoot uptake of some PTEs.
showed that phytoremediation of PTEs is element-dependent; as Zn showed the highest translocation factor (TF) and bioconcentration factor (BCF) values, while Ni showed the lowest ones and the intermediate values belonged to Pb and Cd. These results show the diverse distribution of elements in plant parts, as Zn and Ni were mostly accumulated in shoot and root, respectively. Although different factors caused impacts on phytoremediation criteria, the role of plant type in the phytoremediation of PTEs was at the first rank. Mean TF of PTEs in sunflower was 6.3 times that of maize. Sunflower showed high TF value for the four elements and translocated most of the PTEs from root to the aerial parts demonstrating phytoextraction as the main mechanism in this plant. Maize and sorghum, however, showed low TF and accumulated most of PTEs in their roots revealing phytostabilization as the main mechanism. In general, it can be concluded that plant type was the most influential factor in the phytoremediation of PTEs followed by EDTA and AMF. Taguchi optimization revealed the appropriateness and significance of different chemical and biological treatments on phytoremediation criteria of different elements.
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•Plant type showed the first rank in a significant number of phytoremediation criteria.•Chemical treatments were more effective than biological treatments in phytoremediation of multi-metals contaminated soil.•Among the chemical treatments, the role of EDTA on the phytoremediation of multi-metal polluted soil was predominant.•Taguchi optimization makes it possible to conduct comprehensive phytoremediation study with multiple factors.
•Experimental study of parameters on force in the cortical bone milling.•The effect of machining direction on the osteon orientation was investigated.•RSM and Sobol sensitivity analysis were used for ...this study.•Optimization was performed using Dringer algorithm.
Bone milling force is a key factor to be controlled during the orthopedic surgery. Cutting force has significant influence on the breaking of the tools or causing bone cracks. The cutting force depends on machining parameters, cutting tools and the cortical bone tissue. In this paper, rotational speed, feed rate, cutting depth, tool diameter and the osteon orientation are considered as input parameters. For statistical modeling and experimental study, the response surface method was used. Moreover, using the Sobol statistical sensitivity analysis method the effect of each input parameter on the process force is investigated both qualitatively and quantitatively. Results revealed that bone milling force decreases with increasing rotational speed while it increases with feed rate due to an increase in the thickness of the deformed chip as well as an escalation of friction. Moreover, increasing cutting depth due to increased thickness of the deformed chip, increases friction and thus increases cutting force. Additionally, as the diameter of the blade increases, the cutting force increases. Finally, in the perpendicular direction to the osteon, less force is applied to the bone tissue than that of parallel to osteon. Based on Sobol sensitivity analysis, cutting depth (51.4%), feed rate (21.9%), tool rotational speed (19%), milling direction (4.8%) and tool diameter (1.9%) are the most effective respectively. Response optimization was also presented using Derringer algorithm, which provided a minimum cutting force of 3.76 N, when tool diameter of 4 mm, rotational speed of 3000 rpm and feed rate of 100 mm/min and cutting depth of 1 mm were selected in milling perpendicular to the osteon orientation. This research can be used to optimize milling parameters in order to assist robotic surgery and orthopedic tool design.
This paper proposes a novel hybrid BSC-DEA framework for performance evaluation in sustainable supply chains. The proposed DEA model is capable of dealing with both qualitative and quantitative ...indicators while accounting for desirable and undesirable indicators. A tailored network DEA model involving a set of comprehensive sustainability indicators is applied to rank different supply chains from sustainability viewpoint to find the efficient and benchmarked units at each echelon. Then, sustainability indicators are classified into four groups according to BSC perspectives to help policy makers and top managers to have a more comprehensive and thorough understanding of the sustainability with respect to the long- and short term strategies. Finally, a number of sensitivity analyses are performed to identify the effective factors and strengths and weaknesses of each supply chain are identified based on BSC perspectives. To demonstrate the capabilities of the proposed approach, this framework is implemented for performance evaluation of plastic recycling companies in Mazandaran and Golestan provinces of Iran and some helpful managerial insights are derived from the numerical results.
•Developing a hybrid DEA-BSC model for performance evaluation in sustainable supply chains.•Proposing a novel network DEA model capable of dealing with both qualitative and quantitative indicators.•Proposing a novel network DEA model capable of dealing with desirable and undesirable outputs.•Identifying the most effective sustainability factors for plastic recycling industry.•Assessing the sustainability performance of a number of plastic recycling companies as a case study.
Fuzzy data envelopment analysis (DEA) models emerge as another class of DEA models to account for imprecise inputs and outputs for decision making units (DMUs). Although several approaches for ...solving fuzzy DEA models have been developed, there are some drawbacks, ranging from the inability to provide satisfactory discrimination power to simplistic numerical examples that handles only triangular fuzzy numbers or symmetrical fuzzy numbers. To address these drawbacks, this paper proposes using the concept of expected value in generalized DEA (GDEA) model. This allows the unification of three models – fuzzy expected CCR, fuzzy expected BCC, and fuzzy expected FDH models – and the ability of these models to handle both symmetrical and asymmetrical fuzzy numbers. We also explored the role of fuzzy GDEA model as a ranking method and compared it to existing super-efficiency evaluation models. Our proposed model is always feasible, while infeasibility problems remain in certain cases under existing super-efficiency models. In order to illustrate the performance of the proposed method, it is first tested using two established numerical examples and compared with the results obtained from alternative methods. A third example on energy dependency among 23 European Union (EU) member countries is further used to validate and describe the efficacy of our approach under asymmetric fuzzy numbers.
A comprehensive model was derived based on the balance between drag, collision, and gravity as separation and van der Waals and hydrogen bond as adhesion forces to estimate the equilibrium size of ...agglomerates formed during the fluidization of nanoparticles. Due to the approximately less than 9% of the total amount of forces, drag and collision forces were not considered in the final model. Also, the influence of using the vapor of different alcohols on the fluidization behavior of hydrophilic silica and alumina nanoparticles was studied by experiments. To justify the improving effect of using alcohols, the electrostatic repulsion force was added to the model for the first time. Methanol and 2-propanol were the most effective alcohols on fluidization improvement, and consequently the smallest size of agglomerates was estimated using physical properties of these two alcohols. The Richardson–Zaki (R-Z) analysis indicated that the fluidization degree of cohesive hydrophilic nanoparticles can be greatly improved by adding polar alcohols to the system. The agglomerate sizes predicted based on R-Z showed a good agreement with the calculated ones by model in the presence of alcohols.
VGI projects include geographic information, which are the product of many unorganized volunteers, making it a challenge to ensure the quality of their information. In this field of study, several ...researchers have suggested using intrinsic factors to evaluate the quality of VGI instead of using explicit methods such as comparing with real or reference datasets. In addition, the measurement of the reliability of VGI contributors as an essential intrinsic factor in determining the credibility of their contributions remains an open question. Various types of contributors’ activities and interactions are introduced and discussed in detail in this study at first. Then a comprehensive spatio-temporal contributor reliability model is proposed to assess their performance based on multiple implicit interactions between volunteers in their contribution process. Finally, several cities with different contribution rate (based on their population, number of users and area extent) are chosen and the proposed model is applied to the VGI data of selected regions, finally the results are compared and discussed.
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